Training models for prediction and monitoring using internet of things data collection

ABSTRACT

Systems and methods for transaction platforms include various systems interacting with each other and transacting in various ways. A method for configuring and launching a marketplace includes: identifying, by a processing system having one or more processors, an opportunity to facilitate configuration of a new marketplace; receiving marketplace opportunity data, wherein the marketplace opportunity data includes information related to a set of assets of one or more types; determining configuration parameters to be implemented in the new marketplace; determining the feasibility of implementing the configuration parameters in the new marketplace; determining data resources to support the new marketplace; determining an architecture of the new marketplace; determining the configuration of the data resources in a data model for the marketplace; configuring a marketplace object; connecting selected data resources to populate the marketplace object; and launching the new marketplace.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a bypass continuation of International ApplicationNo. PCT/US2022/050937, filed Nov. 23, 2022, which claims priority to:U.S. Provisional Patent Application No. 63/282,502, filed, Nov. 23,2021; U.S. Provisional Patent Application No. 63/291,306, filed Dec. 17,2021; U.S. Provisional Patent Application No. 63/299,703, filed Jan. 14,2022; U.S. Provisional Patent Application No. 63/302,014, filed Jan. 21,2022; provisional India Patent Application No. 202211008634, filed Feb.18, 2022; U.S. Provisional Patent Application No. 63/392,083, filed Jul.25, 2022; and U.S. Provisional Patent Application No. 63/381,546, filedOct. 28, 2022. Each patent application referenced above is herebyincorporated by reference as if fully set forth herein in its entirety.

FIELD

The present disclosure relates to transaction platforms, and moreparticularly relates to transaction platforms that include systems thatinclude sets of other systems interoperating within the transactionplatforms to define the larger systems.

BACKGROUND Intelligent Data Layers Background

Brought about by exponentially increasing connectivity and intelligenceof devices of all types, the world is experiencing orders-of-magnitudeincreases in scale and granularity of data, as well as the emergence ofentirely new types of data, all available to enable or enhance digitaltransactions in markets of all types. This expansion brings newchallenges to parse, analyze, and derive intelligence from the fractallyexpanding data layers, as well as regulatory and business requirementsto understand and act upon the transactions, transactors, and allcorporate, individual, or AI intermediaries that operate on or interactwith data.

SUMMARY Market Orchestration Configuring and Launching a Marketplace

In embodiments, a method for configuring and launching a marketplaceincludes: identifying, by a processing system having one or moreprocessors, an opportunity to facilitate configuration of a newmarketplace; receiving, by a processing system, marketplace opportunitydata, where the marketplace opportunity data includes informationrelated to a set of assets of one or more types; determining, by theprocessing system, configuration parameters to be implemented in the newmarketplace; determining, by the processing system, the feasibility ofimplementing the configuration parameters in the new marketplace;determining, by the processing system, data resources to support the newmarketplace; determining, by the processing system, an architecture ofthe new marketplace; determining, by the processing system, theconfiguration of the data resources in a data model for the marketplace;configuring, by the processing system, a marketplace object; connecting,by the processing system, selected data resources to populate themarketplace object; and launching, by the processing system, the newmarketplace.

RPA for Configuring and Launching a New Marketplace

In embodiments, a method for configuring and launching a marketplaceincludes: taking an identified type of asset and defining anexchangeable marketplace object that represents a set of rights tocontrol the type of asset, where defining the marketplace objectincludes specifying a data model for the marketplace object and a set ofdata resources for populating instances of the marketplace object;configuring the mechanism for exchange of instances of the marketplaceobject, where the mechanism for exchange includes a set of interfaceswhereby instances of the objects may be exchanged in defined quantitiesfor defined units of value; and configuring a set of computational andconnectivity resources to support a marketplace by which the definedmarketplace objects are exchanged; where at least one defining themarketplace object, configuring the mechanism of exchange andconfiguring the set of computational and connectivity resources isperformed by robotic process automation that is trained on a trainingset of interactions by a set of human users.

Updating Properties of Market Orchestration Digital Twins

In embodiments, a method for updating one or more properties of one ormore market orchestration digital twins includes: receiving a request toupdate one or more properties of one or more digital twins; retrievingthe one or more digital twins required to fulfill the request; selectingdata sources from a set of available data sources; retrieving data fromselected data sources; and updating one or more properties of the one ormore digital twins based on the retrieved data. In embodiments, thedigital twins are selected from the set of marketplace digital twins,asset digital twins, trader digital twins, broker digital twins,environment digital twins, and marketplace host digital twins. Inembodiments, the one or more properties of the one or more digital twinsrelates to asset ownership. In embodiments, the data source is selectedfrom the set of an Internet of Things connected device, a machine visionsystem, an analog vibration sensor, a digital vibration sensor, a fixeddigital vibration sensor, a tri-axial vibration sensor, a single axisvibration sensor, an optical vibration sensor, and a crosspoint switch.

Method of Generating a Fairness Score

In embodiments, a method for generating a fairness score for atransaction includes: receiving, by a fairness engine, transaction datafrom a set of transactions from an execution engine; and calculating, bythe fairness engine, a fairness score representing the fairness of atransaction. In embodiments, the fairness engine includes an executiontiming fairness engine that determines or receives a set of measures oflatency for a set of users. In embodiments, the execution timingfairness engine automatically orchestrates a set of configurationparameters or other features that mitigate unfairness that may be causedby disparate latency. In embodiments, the set of measures of latency aredetermined by testing network return times. In embodiments, testingnetwork return times includes determining the ping, the upload speed, orthe download speed. In embodiments, the set of transactions are executedbased upon the fairness score exceeding a predetermined threshold.

Enterprise Access Layer Summary Enterprise Data Set Exchange

In embodiments, a computer-implemented method includes: receiving, at anaccess layer controlled by an enterprise, a data set characterizing oneor more attributes associated with a group of assets or resourcescontrolled by the enterprise, where the access layer corresponds to anintelligence system that hosts exchangeable enterprise assets;determining, by a permissions system of the access layer, whether thedata set satisfies a set of permission criteria indicating a set ofgoverning rules for assets or resources controlled by the enterprise; inresponse to the data set satisfying the permission criteria, generating,by a data services system associated with the access layer, an encodeddata set that satisfies the set of governing rules; and converting theencoded data set to an exchangeable digital asset by: publishing arepresentation of the encoded data set to a digital wallet system of theaccess layer; and configuring an interface system of the access layerwith access to the encoded data set represented in the digital walletsystem, where the interface system is accessible by a third party. Someembodiments further include assigning a monetary value to the encodeddata set that is viewable via the interface system. In embodiments,assigning the monetary value to the encoded data set includes generatingan estimated monetary value from valuation data compiled from a set oftarget consumers. In embodiments, assigning the monetary value to theencoded data set includes: generating an invite to a set of targetconsumers for the data set; requesting the set of target consumersassign a proposed value to a set of secondary data sets that share oneor more characteristics with the data set; and determining the monetaryvalue for the encoded data set by statistical inference from theproposed values returned from the set of target consumers. Someembodiments further include adjusting the monetary value based onfeedback from the enterprise. In embodiments, adjusting the monetaryvalue includes: generating a feedback request to the enterprise toauthorize the monetary value assigned to the encoded data set; and inresponse to the feedback request, receiving a message from theenterprise to modify the monetary value of the encoded data set. In someembodiments generating the encoded data set includes partially encodinga portion of the data set that includes information failing to satisfythe set of governing rules. In embodiments, publishing therepresentation of the encoded data set to the digital wallet systemincludes publishing the representation of the encoded data set to a hotwallet of the wallet system. In embodiments, publishing therepresentation of the encoded data set to the digital wallet systemincludes publishing the representation of the encoded data set to a coldwallet of the wallet system. In embodiments, publishing the encoded dataset to the digital wallet system includes publishing the encoded dataset to a custodial wallet of the wallet system. In embodiments, thegroup of resources is enterprise-owned devices. In embodiments, thegroup of resources is production equipment of the enterprise. Inembodiments, the data set includes logistics information. Inembodiments, the data set includes inventory information. Inembodiments, the data set includes procurement information. Inembodiments, the data set includes enterprise marketing information. Inembodiments, the data set includes client-purchasing information. Inembodiments, the access layer is a network access layer. In embodiments,the enterprise assets are digital assets. In embodiments, the governingrules are privacy rules. In embodiments, re the governing rules areprioritization rules.

In embodiments, a system includes: an access layer including a processorand storage hardware in communication with the processor, where thestorage hardware includes instructions that when executed by theprocessor perform operations, and where the operations include:receiving, at the access layer controlled by an enterprise, a data setcharacterizing one or more attributes associated with a group of assetsor resources controlled by the enterprise, where the access layercorresponds to an intelligence system that hosts exchangeable enterpriseassets; determining, by a permissions system of the access layer,whether the data set satisfies a set of permission criteria indicating aset of governing rules for resources controlled by the enterprise; inresponse to the data set satisfying the permission criteria, generating,by a data services system associated with the access layer, an encodeddata set that satisfies the set of governing rules; and converting theencoded data set to an exchangeable digital asset by: publishing arepresentation of the encoded data set to a digital wallet system of theaccess layer; and configuring an interface system of the access layerwith access to the encoded data set represented in the digital walletsystem, where the interface system is accessible by a third party. Inembodiments, the operations further comprise assigning a monetary valueto the encoded data set that is viewable via the interface system. Inembodiments, assigning the monetary value to the encoded data setincludes generating an estimated monetary value from valuation datacompiled from a set of target consumers. In embodiments, assigning themonetary value to the encoded data set includes: generating an invite toa set of target consumers for the data set; requesting the set of targetconsumers assign a proposed value to a set of secondary data sets thatshare one or more characteristics with the data set; and determining themonetary value for the encoded data set by statistical inference fromthe proposed values returned from the set of target consumers. Inembodiments, the operations further comprise adjusting the monetaryvalue based on feedback from the enterprise. In embodiments, adjustingthe monetary value includes: generating a feedback request to theenterprise to authorize the monetary value assigned to the encoded dataset; and in response to the feedback request, receiving a message fromthe enterprise to modify the monetary value of the encoded data set. Inembodiments, generating the encoded data set includes partially encodinga portion of the data set that includes information failing to satisfythe set of governing rules. In embodiments, publishing the encoded dataset to the digital wallet system includes publishing the encoded dataset to a hot wallet of the wallet system. In embodiments, publishing theencoded data set to the digital wallet system includes publishing theencoded data set to a cold wallet of the wallet system. In embodiments,publishing the encoded data set to the digital wallet system includespublishing the encoded data set to a custodial wallet of the walletsystem. In embodiments, the group of resources is enterprise-owneddevices. In embodiments, the group of resources is production equipmentof the enterprise. In embodiments, the data set includes logisticsinformation. In embodiments, the data set includes inventoryinformation. In embodiments, the data set includes procurementinformation. In embodiments, the data set includes enterprise marketinginformation. In embodiments, the data set includes client-purchasinginformation. In embodiments, the access layer is a network access layer.In embodiments, the enterprise assets are digital assets. Inembodiments, the governing rules are privacy rules. In embodiments, thegoverning rules are prioritization rules.

Control Plane and Data Plane Coverage

In embodiments, a computer-implemented method includes: receiving, at anetwork access layer, an asset request from a requesting entity, wherethe asset request indicates an asset available in a digital walletsystem associated with the network access layer, and where the networkaccess layer includes a data plane configured to exchange assetsprivately-generated by an enterprise entity operating a control planeassociated with the network access layer; identifying an asset controlassociated with the asset indicated by the asset request, where theasset control is configured by a permissions system of the networkaccess layer and indicates a control parameter determined by anintelligence system of the network access layer, and where the controlparameter is configured using data derived from the enterprise entitythat privately generated the asset; determining whether the assetcontrol is satisfied by at least one of the asset request or therequesting entity; and in response to the asset control being satisfied,facilitating fulfillment of the asset request. In embodiments, the assetis available in a hot wallet of the digital wallet system. Inembodiments, the asset is available in a cold wallet of the digitalwallet system. In embodiments, the asset is available in a custodialwallet of the digital wallet system. In embodiments, facilitatingfulfillment of the asset request includes transferring a set of keys forthe cold wallet to a hot wallet of the digital wallet system. Inembodiments, facilitating fulfillment of the asset request includes:signing a transaction involving the asset on the cold wallet; andrelaying the signed transaction using a hot wallet of the digital walletsystem that is associated with the cold wallet. In embodiments,facilitating fulfillment of the asset request includes connecting thecold wallet to the requesting entity. In embodiments, the asset controlmatches an access control for an enterprise entity that submitted theasset to the digital wallet system. In embodiments, the asset controlindicates a security clearance level. In embodiments, the asset controlincludes transactional detail requirements for the asset.

In embodiments, a system includes: a network access layer including aprocessor and storage hardware in communication with the processor,where the storage hardware includes instructions that when executed bythe processor perform operations, and where the operations include:receiving, at the network access layer, an asset request from arequesting entity, where the asset request indicates an asset availablein a digital wallet system associated with the network access layer, andwhere the network access layer includes a data plane configured toexchange assets privately-generated by an enterprise entity operating acontrol plane associated with the network access layer; identifying anasset control associated with the asset indicated by the asset request,where the asset control is configured by a permissions system of thenetwork access layer and indicates a control parameter determined by anintelligence system of the network access layer, and where the controlparameter is configured using data derived from the enterprise entitythat privately generated the asset; determining whether the assetcontrol is satisfied by at least one of the asset request or therequesting entity; and in response to the asset control being satisfied,facilitating fulfillment of the asset request. In embodiments, the assetis available in a hot wallet of the digital wallet system. Inembodiments, the asset is available in a cold wallet of the digitalwallet system. In embodiments, the asset is available in a custodialwallet of the digital wallet system. In embodiments, facilitatingfulfillment of the asset request includes transferring a set of keys forthe cold wallet to a hot wallet of the digital wallet system. Inembodiments, facilitating fulfillment of the asset request includes:signing a transaction involving the asset on the cold wallet; andrelaying the signed transaction using a hot wallet of the digital walletsystem that is associated with the cold wallet. In embodiments,facilitating fulfillment of the asset request includes connecting thecold wallet to the requesting entity. In embodiments, the asset controlmatches an access control for an enterprise entity that submitted theasset to the digital wallet system. In embodiments, the asset controlindicates a security clearance level. In embodiments, the asset controlincludes transactional detail requirements for the asset.

Private to Public Block Chain Via an Enterprise Access Layer

In embodiments, a computer-implemented method includes: receiving, at anetwork access layer, an asset request from a requesting entity, wherethe asset request indicates an asset available in a digital walletsystem associated with the network access layer, where the networkaccess layer corresponds to a client-facing intelligence system thathosts exchangeable digital assets, and where the exchangeable digitalassets correspond to one or more assets stored in a private append-onlydata structure associated with an owner of the exchangeable digitalassets; identifying an asset control associated with the asset indicatedby the asset request, where the asset control is configured by apermissions system of the network access layer and indicates a controlparameter determined by an intelligence system of the network accesslayer; determining whether the asset control is satisfied by at leastone of the asset request or the requesting entity; and in response tothe asset control being satisfied by the at least one of the assetrequest or the requesting entity, facilitating fulfillment of the assetrequest, where fulfillment includes storing the asset in a publicappend-only data structure to represent an exchange of the asset withthe requesting entity. In embodiments, the asset is available in a hotwallet of the digital wallet system. In embodiments, the asset isavailable in a cold wallet of the digital wallet system. In embodiments,the asset is available in a custodial wallet of the digital walletsystem. In embodiments, facilitating fulfillment of the asset requestincludes transferring a set of keys for the cold wallet to a hot walletof the digital wallet system. In embodiments, facilitating fulfillmentof the asset request includes: signing a transaction involving the asseton the cold wallet; and relaying the signed transaction using a hotwallet of the digital wallet system that is associated with the coldwallet. In embodiments, facilitating fulfillment of the asset requestincludes connecting the cold wallet to the requesting entity. Inembodiments, the asset control matches an access control for anenterprise entity that submitted the asset to the digital wallet system.In embodiments, the asset control indicates a security clearance level.In embodiments, the asset control includes transactional detailrequirements for the asset.

In embodiments, a system includes: a network access layer including aprocessor and storage hardware in communication with the processor,where the storage hardware includes instructions that when executed bythe processor perform operations, and where the operations include:receiving, at a network access layer, an asset request from a requestingentity, where the asset request indicates an asset available in adigital wallet system associated with the network access layer, wherethe network access layer corresponds to a client-facing intelligencesystem that hosts exchangeable digital assets, and where theexchangeable digital assets correspond to one or more assets stored in aprivate append-only data structure associated with an owner of theexchangeable digital assets; identifying an asset control associatedwith the asset indicated by the asset request, where the asset controlis configured by a permissions system of the network access layer andindicates a control parameter determined by an intelligence system ofthe network access layer; determining whether the asset control issatisfied by at least one of the asset request or the requesting entity;and in response to the asset control being satisfied by the at least oneof the asset request or the requesting entity, facilitating fulfillmentof the asset request, where fulfillment includes storing the asset in apublic append-only data structure to represent an exchange of the assetwith the requesting entity. In embodiments, the asset is available in ahot wallet of the digital wallet system. In embodiments, the asset isavailable in a cold wallet of the digital wallet system. In embodiments,the asset is available in a custodial wallet of the digital walletsystem. In embodiments, facilitating fulfillment of the asset requestincludes transferring a set of keys for the cold wallet to a hot walletof the digital wallet system. In embodiments, facilitating fulfillmentof the asset request includes: signing a transaction involving the asseton the cold wallet; and relaying the signed transaction using a hotwallet of the digital wallet system that is associated with the coldwallet. In embodiments, facilitating fulfillment of the asset requestincludes connecting the cold wallet to the requesting entity. Inembodiments, the asset control matches an access control for anenterprise entity that submitted the asset to the digital wallet system.In embodiments, the asset control indicates a security clearance level.In embodiments, the asset control includes transactional detailrequirements for the asset.

Assigning Access Controls to an Enterprise-Generated Asset

In embodiments, a computer-implemented method includes: receiving, at anetwork access layer controlled by an enterprise, a set of assetsprivately generated by the enterprise, where the network access layercorresponds to a client-facing intelligence system that hostsexchangeable enterprise digital assets; for each asset of the set ofassets: classifying, by an artificial-intelligence system of the networkaccess layer, the respective asset into an access control category,where each asset control category is associated with a set of assetcontrols that dictate one or more transaction parameters for theexchange of the respective asset with a third party; and assigning, by apermissions system of the network access layer, the set of assetcontrols for the access control category classified by the AI system forthe respective asset; and

converting the set of assets to exchangeable digital assets by:publishing the set of assets to a digital wallet system of the networkaccess layer; and configuring an interface system of the network accesslayer with access to the set in the digital wallet system, where theinterface system is accessible by a third party. In embodiments,publishing the set of assets to the digital wallet system includespublishing at least a portion of assets in the set to a hot wallet ofthe digital wallet system. In embodiments, publishing the set of assetsto the digital wallet system includes publishing at least a portion ofassets in the set to a cold wallet of the digital wallet system. Inembodiments, publishing the set of assets to the digital wallet systemincludes publishing at least a portion of assets in the set to acustodial wallet of the digital wallet system. In embodiments,publishing the set of assets to the digital wallet system includespublishing a first portion of assets in the set to a hot wallet of thedigital wallet system and a second portion of the assets in the set to acold wallet of the digital wallet system. In embodiments, the firstportion has a first access control category that indicates that a firstset of asset controls of the first access control category is lessrestrictive than a second set of asset controls for a second accesscontrol category classified for the second portion. In embodiments, thefirst portion has a first access control category that indicates agreater frequency of access than a second access control categoryclassified for the second portion. In embodiments, the set of assetcontrols includes an asset control that matches an access control for anenterprise entity that communicated at least one of the assets from theset of assets to the network asset layer. In embodiments, the set ofasset controls includes an asset control that indicates a securityclearance level. In embodiments, the one or more transaction parametersinclude a minimum pricing requirement. In embodiments, a systemincluding: a network access layer including a processor and storagehardware in communication with the processor, where the storage hardwareincludes instructions that when executed by the processor performoperations, and where the operations include: receiving, at a networkaccess layer controlled by an enterprise, a set of assets privatelygenerated by the enterprise, where the network access layer correspondsto a client-facing intelligence system that hosts exchangeableenterprise digital assets; for each asset of the set of assets:classifying, by an artificial-intelligence system of the network accesslayer, the respective asset into an access control category, where eachasset control category is associated with a set of asset controls thatdictate one or more transaction parameters for the exchange of therespective asset with a third party; and assigning, by a permissionssystem of the network access layer, the set of asset controls for theaccess control category classified by the AI system for the respectiveasset; and converting the set of assets to exchangeable digital assetsby: publishing the set of assets to a digital wallet system of thenetwork access layer; and configuring an interface system of the networkaccess layer with access to the set in the digital wallet system, wherethe interface system is accessible by a third party. In embodiments,publishing the set of assets to the digital wallet system includespublishing at least a portion of assets in the set to a hot wallet ofthe digital wallet system. In embodiments, publishing the set of assetsto the digital wallet system includes publishing at least a portion ofassets in the set to a cold wallet of the digital wallet system. Inembodiments, publishing the set of assets to the digital wallet systemincludes publishing at least a portion of assets in the set to acustodial wallet of the digital wallet system. In embodiments,publishing the set of assets to the digital wallet system includespublishing a first portion of assets in the set to a hot wallet of thedigital wallet system and a second portion of the assets in the set to acold wallet of the digital wallet system. In embodiments, the firstportion has a first access control category that indicates that a firstset of asset controls of the first access control category is lessrestrictive than a second set of asset controls for a second accesscontrol category classified for the second portion. In embodiments, thefirst portion has a first access control category that indicates agreater frequency of access than a second access control categoryclassified for the second portion. In embodiments, the set of assetcontrols includes an asset control that matches an access control for anenterprise entity that communicated at least one of the assets from theset of assets to the network asset layer. In embodiments, the set ofasset controls includes an asset control that indicates a securityclearance level. In embodiments, the one or more transaction parametersinclude a minimum pricing requirement.

Monitoring Public Data Exchanges for Viable Enterprise Data Transactions

In embodiments, a computer-implemented method includes: monitoring aplurality of public market participants via an interface system of anetwork access layer, where the network access layer is controlled by anenterprise and corresponds to an intelligence system that hostsexchangeable enterprise digital assets; receiving, at the network accesslayer via the interface system, an indication that a monitored publicmarket participant requests a digital asset candidate; determining, bythe intelligence system of the network access layer, whether the digitalasset candidate matches an asset available in a digital wallet systemassociated with the network access layer; and in response to the digitalasset candidate matching the asset available in the digital walletsystem: identifying a set of asset controls managed by a permissionsystem of the network asset layer, where the permission system isconfigured to assign the set of asset controls to exchangeableenterprise digital assets in the digital wallet system; determiningwhether a transaction with the monitored public market participant thatinvolves the asset available in the digital wallet system satisfies anasset control criteria corresponding to the asset available, where theasset control criteria indicates that a threshold number of the set ofasset controls have been violated; and in response to determining thatthe transaction with the monitored public market participant thatinvolves the asset available in the digital wallet system satisfies theasset control criteria, generating a message data packet requesting anactual transaction with the monitored public market participantinvolving the asset available, where the message data packet isconfigured for communication via the interface system. In embodiments,the asset is available in a hot wallet of the digital wallet system. Inembodiments, the asset is available in a cold wallet of the digitalwallet system. In embodiments, the asset is available in a custodialwallet of the digital wallet system. Some embodiments include: receivinga response message from the monitored public market participant; anddetermining that the response message indicates an acknowledgement tofulfill the request for the actual transaction; and facilitatingfulfillment of the actual transaction. In embodiments, facilitatingfulfillment of the actual transaction includes storing a digital form ofthe asset in a public append-only data structure to represent executionof the actual transaction. In embodiments, facilitating fulfillment ofthe asset request includes: signing the actual transaction involving theasset on a cold wallet; and relaying the signed transaction using a hotwallet of the digital wallet system that is associated with the coldwallet. In embodiments, storing the digital form of the asset to apublic append-only data structure facilitating uses at least one keyfrom a hot wallet of the digital wallet system. In embodiments, storingthe digital form of the asset to a public append-only data structurefacilitating uses at least one key from a cold wallet of the digitalwallet system. In embodiments, the set of asset controls includes anasset control that matches an access control for an enterprise entitythat submitted the asset to the digital wallet system. In embodiments,the set of asset controls includes an asset control that indicates asecurity clearance level for the asset. In embodiments, the set of assetcontrols transactional detail requirements for the asset.

In embodiments, a system includes: a network access layer including aprocessor and storage hardware in communication with the processor,where the storage hardware includes instructions that when executed bythe processor perform operations, and where the operations include:monitoring a plurality of public market participants via an interfacesystem of a network access layer, where the network access layer iscontrolled by an enterprise and corresponds to an intelligence systemthat hosts exchangeable enterprise digital assets; receiving, at thenetwork access layer via the interface system, an indication that amonitored public market participant requests a digital asset candidate;determining, by the intelligence system of the network access layer,whether the digital asset candidate matches an asset available in adigital wallet system associated with the network access layer; and inresponse to the digital asset candidate matching the asset available inthe digital wallet system: identifying a set of asset controls managedby a permission system of the network asset layer, where the permissionsystem is configured to assign the set of asset controls to exchangeableenterprise digital assets in the digital wallet system; determiningwhether a transaction with the monitored public market participant thatinvolves the asset available in the digital wallet system satisfies anasset control criteria corresponding to the asset available, where theasset control criteria indicates that a threshold number of the set ofasset controls have been violated; and in response to determining thatthe transaction with the monitored public market participant thatinvolves the asset available in the digital wallet system satisfies theasset control criteria, generating a message data packet requesting anactual transaction with the monitored public market participantinvolving the asset available, where the message data packet isconfigured for communication via the interface system. In embodiments,the asset is available in a hot wallet of the digital wallet system. Inembodiments, the asset is available in a cold wallet of the digitalwallet system. In embodiments, the asset is available in a custodialwallet of the digital wallet system. In embodiments, the operationsfurther comprise: receiving a response message from the monitored publicmarket participant; and determining that the response message indicatesan acknowledgement to fulfill the request for the actual transaction;and facilitating fulfillment of the actual transaction. In embodiments,facilitating fulfillment of the actual transaction includes storing adigital form of the asset in a public append-only data structure torepresent execution of the actual transaction. In embodiments,facilitating fulfillment of the asset request includes: signing theactual transaction involving the asset on a cold wallet; and relayingthe signed transaction using a hot wallet of the digital wallet systemthat is associated with the cold wallet. In embodiments, storing thedigital form of the asset to a public append-only data structurefacilitating uses at least one key from a hot wallet of the digitalwallet system. In embodiments, storing the digital form of the asset toa public append-only data structure facilitating uses at least one keyfrom a cold wallet of the digital wallet system. In embodiments, the setof asset controls includes an asset control that matches an accesscontrol for an enterprise entity that submitted the asset to the digitalwallet system. In embodiments, the set of asset controls includes anasset control that indicates a security clearance level for the asset.In embodiments, the set of asset controls transactional detailrequirements for the asset.

Managing Tenancy in a Multi-Party Enterprise Access Layer

In embodiments, a computer-implemented method includes: monitoring,using an access layer accessible to a plurality of tenant enterprises, aset of assets associated with a set of digital wallets of a digitalwallet system for the access layer, where the access layer correspondsto a tenant-facing intelligence system that hosts exchangeableenterprise assets; receiving, at the access layer, an indication that arequesting tenant enterprise of the plurality of tenant enterprisesrequests a transaction involving an asset of the set of assets;determining, by the access layer, whether the requesting tenantenterprise has a set of access rights that satisfy an access criteriafor the asset of the requested transaction; in response to therequesting tenant having the set of access rights that satisfy theaccess criteria, deploying, for the requesting tenant enterprise, a setof resources associated with the access layer and shared among theplurality of tenant enterprises to facilitate the transaction involvingthe asset on behalf of the tenant enterprise.

In embodiments, the requesting tenant enterprise includes a first tenantenterprise and a second tenant enterprise; the method further includesdetermining a transaction priority for each of the first tenantenterprise and the second tenant enterprise; and deploying the set ofresources occurs for the first tenant enterprise having a firsttransaction priority greater than a second transaction priority of thesecond tenant enterprise. In embodiments, each tenant enterprise isassociated with (i) a set of private resources inaccessible to eachother tenant and (ii) a set of shared resources associated with theaccess layer and shared among the plurality of tenant enterprises. Inembodiments, the digital wallet system includes: a first subset ofdigital wallets accessible to one of the tenant enterprises andinaccessible to other tenant enterprises; and a second subset of digitalwallets accessible to and shared among a set of the plurality of tenantenterprises. In embodiments, the access layer is a network access layer.In embodiments, the exchangeable enterprise assets are digital assets.In embodiments, the set of digital wallets includes a cold wallet. Inembodiments, the set of digital wallets includes a hot wallet and a coldwallet. In embodiments, the set of digital wallets includes a custodialwallet. In embodiments, the set of digital wallets includes a custodialwallet and a cold wallet. In embodiments, the set of digital walletsincludes at least two of a hot wallet, a cold wallet, or a custodialwallet.

Peer-to-Peer Enterprise Access Layer

In embodiments, a computer-implemented method includes: receiving, at anaccess layer, an asset request from a requesting entity, where the assetrequest indicates a transaction involving an asset available in adigital wallet system associated with the access layer, and where theaccess layer corresponds to an intelligence system that hostsexchangeable enterprise assets; identifying an asset control associatedwith the asset indicated by the asset request, where the asset controlis configured by a permissions system of the access layer and indicatesa control parameter determined by an intelligence system of the accesslayer; determining whether the asset control is satisfied by at leastone of the asset request or the requesting entity; and in response tothe asset control being satisfied, establishing a peer-to-peer accesslayer between the requesting entity and another transacting entityassociated with the transaction indicated by the asset request, wherethe peer-to-peer access layer provides the other transacting entity withaccess to a limited set of digital assets and resources of therequesting entity. In embodiments, the transacting entity includes aplurality of entities forming a multilateral connection between therequesting entity and the plurality of entities. In embodiments, thepeer-to-peer connection is a secure connection. Some embodiments furtherinclude generating, using a data processing system of the access layer,an encrypted message packet for communication using the peer-to-peerconnection. In embodiments, the access layer is a network access layer.In embodiments, the exchangeable enterprise assets are digital assets.In embodiments, the asset is available in a digital wallet of thedigital wallet system. In embodiments, the digital wallet is a coldwallet. In embodiments, the digital wallet is a hot wallet. Inembodiments, the digital wallet is a custodial wallet. In embodiments,the peer-to-peer access layer provides an interface that is accessibleby a wallet system of the other transacting party, whereby the walletsystem of the other transacting party accesses the limited set ofdigital assets and resources of the requesting enterprise via theinterface. Some embodiments further include: receiving a set of accessrules from a user device associated with the requesting entity, wherethe set of access rules define the set of digital assets and resourcesthat are accessible to the other transacting enterprise; and configuringthe peer-to-peer access layer based on the set of access rules.

Market Orchestration Architecture

In embodiments, a system for normalizing an item value for a pluralityof exchanges includes: a plurality of electronic exchanges configuredfor conducting transactions for at least one item in a set of items; anitem value normalization system configured to identify a reference itemin the set of items, and state a value for at least one other item inthe set of items as a normalized value relative to a value of thereference item; and a robotic process automation system executing a setof computer-readable instructions on at least one processor, theinstructions causing the robotic process automation system to automateitem value normalization through automated operation of the item valuenormalization system. In embodiments, the item value normalizationsystem is configured to identify the reference item based on atransaction history for one or more candidate reference items in the setof items. In embodiments, the item value normalization system isconfigured to identify the reference item based on a transaction historyfor one or more items that are similar to a candidate reference item. Inembodiments, the item value normalization system is configured toidentify the reference item based on a degree of commonality of acandidate reference item to other items in the set of items. Inembodiments, an item identified as a reference item from the set ofitems for a first exchange is distinct from an item identified as areference item from the set of items for a second exchange. Inembodiments, to automate item value normalization includes stating thenormalized item value based on a native currency of a target electronicexchange of the plurality exchanges. In embodiments, to state a valuefor at least one other item in the set of items as a normalized valueincludes at least one exchange-specific fee associated with conducting atransaction for the item. In embodiments, the item value normalizationsystem is further configured to identify a reference set of items, andstate a value for at least one other item in a different set of items asa normalized value relative to a value of at least one item in the setof reference items. Some embodiments further include a set of roboticprocess automation services that are configured to generate a token thatrepresents an item in the second exchange based on characteristics ofthe item determined from data from the first exchange. Some embodimentsfurther include a set of robotic process automation services that areconfigured to generate a digital representation of a set of rightsrelating to an item that is consistent with governing rules of thesecond exchange based on processing at least one of a set of smartcontracts and a set of terms and conditions relating to the item. Someembodiments further include a set of robotic process automation servicesthat are configured to orchestrate a set of transaction workflows ineach of a plurality of exchanges, such that initiation of a set ofactions in one exchange of the plurality of exchanges automaticallyresults in the triggering of a set of actions in at least one otherexchange. Some embodiments further include a digital twin thatrepresents a set of entities, workflows, and transaction parameters of aplurality of exchanges, such that interaction with an interface of thedigital twin can orchestrate an interaction in each of the plurality ofexchanges. Some embodiments further include a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to set of smart contracts that include terms, conditions andparameters for a set of transaction workflows involving the assets,where the pipeline is automatically configured to adjust a network pathbased on the characteristics of the data and at least one performanceparameter of the network path. Some embodiments further include a dataand network infrastructure pipeline that is configured to deliver datafrom a set of assets to an interface by which an operator orchestrates aset of parameters for a set of transaction workflows involving theassets, where the pipeline is automatically configured to adjust timingof data delivery based on at least one of a transaction parameter and anetwork performance parameter.

Some embodiments further include a set of application programminginterfaces to a marketplace that are configured to be integrated into anelectronic wallet system, such that interactions with a set ofinterfaces of the wallet system automatically trigger a set oftransaction workflows within the marketplace. Some embodiments furtherinclude a set of application programming interfaces to a marketplacethat are configured to be integrated into a digital twin platform, suchthat interactions with a set of interfaces of the digital twin platformautomatically trigger a set of transaction workflows within themarketplace. Some embodiments further include a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into an enterprise database platform, such that interactionswith a set of interfaces of the enterprise database platformautomatically trigger a set of transaction workflows within themarketplace. Some embodiments further include a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a platform-as-a-service platform, such that interactionswith a set of interfaces of the platform-as-a-service platformautomatically trigger a set of transaction workflows within themarketplace. Some embodiments further include a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a computer-aided design platform, such that interactionswith a set of interfaces of the computer-aided design platformautomatically trigger a set of transaction workflows within themarketplace. Some embodiments further include a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a video game, such that interactions with a set ofinterfaces of the video game automatically trigger a set of transactionworkflows within the marketplace.

In embodiments, a system for normalizing an item value for a pluralityof exchanges includes: a plurality of electronic exchanges configuredfor conducting transactions for at least one item in a set of items inan exchange-native currency for each of the plurality of electronicexchanges; an item value normalization system configured to identify areference currency of a plurality of exchange-native currencies for theplurality of electronic exchanges, and to state a value for the at leastone item in the set of items as a normalized value relative to areference currency value of the at least one item; and a robotic processautomation system executing a set of computer-readable instructions onat least one processor, the instructions causing the robotic processautomation system to automate item value normalization through automatedoperation of the item value normalization system. In embodiments, theitem value normalization system is further configure to identify areference currency based on a candidate currency exchange rate history,a futures value of a candidate currency, a volatility score of acandidate currency, or a relative valuation of a candidate currency. Inembodiments, the item value normalization system is configured toidentify the reference currency based on an exchange rate for a portionof the plurality of exchange-native currencies. In embodiments, to statea value for the at least one item in the set of items as a normalizedvalue includes at least one exchange-specific fee associated withconducting a transaction for the item. Some embodiments further includea set of robotic process automation services that are configured togenerate a token that represents an item in the second exchange based oncharacteristics of the item determined from data from the firstexchange. Some embodiments further include a set of robotic processautomation services that are configured to generate a digitalrepresentation of a set of rights relating to an item that is consistentwith governing rules of the second exchange based on processing at leastone of a set of smart contracts and a set of terms and conditionsrelating to the item.

Some embodiments further include a set of robotic process automationservices that are configured to orchestrate a set of transactionworkflows in each of a plurality of exchanges, such that initiation of aset of actions in one exchange of the plurality of exchangesautomatically results in the triggering of a set of actions in at leastone other exchange. Some embodiments further include a digital twin thatrepresents a set of entities, workflows, and transaction parameters of aplurality of exchanges, such that interaction with an interface of thedigital twin can orchestrate an interaction in each of the plurality ofexchanges. Some embodiments further include a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to set of smart contracts that include terms, conditions andparameters for a set of transaction workflows involving the assets,where the pipeline is automatically configured to adjust a network pathbased on the characteristics of the data and at least one performanceparameter of the network path. Some embodiments further include a dataand network infrastructure pipeline that is configured to deliver datafrom a set of assets to an interface by which an operator orchestrates aset of parameters for a set of transaction workflows involving theassets, where the pipeline is automatically configured to adjust timingof data delivery based on at least one of a transaction parameter and anetwork performance parameter. Some embodiments further include a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into an electronic wallet system, such thatinteractions with a set of interfaces of the wallet system automaticallytrigger a set of transaction workflows within the marketplace. Someembodiments further include a set of application programming interfacesto a marketplace that are configured to be integrated into a digitaltwin platform, such that interactions with a set of interfaces of thedigital twin platform automatically trigger a set of transactionworkflows within the marketplace.

Some embodiments further include a set of application programminginterfaces to a marketplace that are configured to be integrated into anenterprise database platform, such that interactions with a set ofinterfaces of the enterprise database platform automatically trigger aset of transaction workflows within the marketplace. Some embodimentsfurther include a set of application programming interfaces to amarketplace that are configured to be integrated into aplatform-as-a-service platform, such that interactions with a set ofinterfaces of the platform-as-a-service platform automatically trigger aset of transaction workflows within the marketplace. Some embodimentsfurther include a set of application programming interfaces to amarketplace that are configured to be integrated into a computer-aideddesign platform, such that interactions with a set of interfaces of thecomputer-aided design platform automatically trigger a set oftransaction workflows within the marketplace. Some embodiments furtherinclude a set of application programming interfaces to a marketplacethat are configured to be integrated into a video game, such thatinteractions with a set of interfaces of the video game automaticallytrigger a set of transaction workflows within the marketplace. Inembodiments, a system for item token generation including: a smartcontact for an item, the smart contract for control of at least aportion of aspects of conducting a transaction for the item in a firstexchange; a set of item characteristics that facilitate tokenization ofthe item; a set of target exchange characteristics rules; a smartcontract parsing system configured to parse the smart contract for theitem into a set of contract terms for the item; a token generationsystem configured to receive the set of contract terms for the item, toreceive the set of item characteristics, to receive the set of targetexchange characteristics rules and to generate through cooperativeoperation of a smart contract engine, a token for the item for use inthe target exchange; and a smart contract engine interfacing with thetoken generation system and configured to perform validation of at leastone of the contract terms through emulation of a smart contractgenerated for the item.

In embodiments, the smart contract engine is further configured toperform validation of at least one of a set of contract terms for asmart contract configured for the target exchange. Some embodimentsfurther include a set of characteristics harvesting functions configuredto facilitate harvesting the set of item characteristics from a digitalrepresentation of the item in the first exchange. Some embodimentsfurther include a set of robotic process automation services executing aset of computer-readable instructions on at least one processor, theinstructions causing the robotic process automation system to automateitem token generation through automated operation of the tokengeneration system. In embodiments, a system for item token generationincluding: a first token representing characteristics of an item in afirst electronic exchange; a set of target exchange characteristicsrules; a set of item characteristics harvesting services configured toextract one or more item characteristics from the first token; a tokengeneration system configured to receive the first token, to receive theset of target exchange characteristics rules and to generate a token forthe item for use in the target exchange by applying at least one of theset item characteristic harvesting services to harvest a set ofcharacteristics of an item represented by the first token; and a roboticprocess automation system executing a set of computer-readableinstructions on at least one processor, the instructions causing therobotic process automation system to automate item token generationthrough automated operation of the token generation system.

Intelligent Data Layer Summary Intelligent Data Layer System

In embodiments, an intelligent data layer system includes: acomputer-readable storage system that stores a layer configuration datastore that maintains: ingestion parameters including one or more datastructures that represent aspects of one or more of a plurality of datasources including a source location, an interface protocol, a sourcedata ontology, and an ingestion cost; parsing rules that facilitatedetermining one or more of structure, content, relationships among dataelements, intended meaning of the data elements, or relationships ofdata, structure, and intended meaning; and one or more analysisalgorithms; and a set of one or more processors that execute a set ofcomputer-readable instructions, where the set of one or more processorscollectively: receive an intelligence request from an intelligenceconsumer portal; determine at least one data source for derivingintelligence for the consumer portal based on the received request;configure an ingestion system based on the ingestion parameters andparsing rules in the layer configuration data store for the at least onedata source; configure an analysis system based on the analysisalgorithms in the layer configuration data store for the at least onedata source; configure an intelligence deriving system based oninformation in the request and available intelligence services in anintelligence service system; and operate the system to ingest data fromthe at least one data source using the ingestion system, analyze theingested data from the at least one data source using the analysissystem, derive a set of intelligence data from at least one of theingested data form the at least one data source and an outcome of usingthe analysis system, and communicating the set of intelligence data toat least one of the consumer portal or an intelligent data layer store.In embodiments, the computer-readable storage system stores anintelligent data layer store that maintains results of operations of oneor more systems of the intelligent data layer system. In embodiments,the one or more systems includes the ingestion system, the analysissystem, and the intelligence deriving system. In embodiments, the resultof operations includes intermediate results of at least one of the oneor more systems and at least one role-adapted final result variant ofthe intermediate results. In embodiments, to configure the analysissystem is further based on consumer intelligence objectives of therequest. In embodiments, to configure the analysis system is furtherbased on aspects of the request.

In embodiments, the set of one or more processors is configured in anintelligent data layer control tower that configures and operates theintelligent data layer system by communicating control sequences withthe ingestion system, the analysis system, and the intelligence derivingsystem. Some embodiments further include an algorithm portal of anintelligent data layer control tower of the system through which atleast one of the analysis algorithms is received. In embodiments, theingestion system parses content of data sources to determine structureof the content and relationships among elements in the data. Inembodiments, the ingestion system parsing a content of data sourcesresults in generating characterization data that includes an intendedmeaning of elements of the data and relationships among the data,structures of the data, and meaning of the data parsed from the content.In embodiments, the ingestion system assigns a relationship attribute toa pair of data values that are configured as parent/child in a hierarchyof the data source. In embodiments, the ingestion system is configuredto maintain a schedule of collection activity for one or more datasources.

In embodiments, the ingestion system is configured to parse source dataaccording to at least one of a specification of the source or a contextof a supply chain for an ingestion instance of the source data. Inembodiments, the ingestion system communicates ingested data, results ofingestion, and results of parsing, to an intelligent data layer controltower of the system. In embodiments, the location of the data source isa source address selected from a list of source addresses consisting ofa universal record locator, port number, stream identifier, publicationand/or broad channel, sensor output address. In embodiments, theanalysis system compares data from the data source against a target useof intelligence derived from a data source to determine a degree offitness for use of the data source by the intelligence deriving system.In embodiments, the analysis system analyzes ingestion system resultsfor meeting at least one consumption target requirement of the consumerportal request. In embodiments, the consumption target requirementincludes one or more of a validity time constraint, an accuracyconstraint, a frequency of update constraint, or relevance to aconsumption subject matter focus. In embodiments, the analysis systemconfigures data regarding the ingested data for one or more system usesfrom a list of uses including advertisements that characterize theingested data in terms of potential intelligence value, indexing schemesfor offering intelligence derived data in a marketplace, searchingintelligence derived data by identifying keywords, terms, and valuesassociated with the ingested data. In embodiments, the analysis systemestimates a value of intelligence data derived from the ingested datafor a range of consumer portals to enable setting costs for consumingthe intelligence data derived from the ingested data.

In embodiments, one or more systems of the intelligent data layer isconfigured as a micro-service architecture for isolated and independentoperation of instances of the one or more systems for a plurality ofdistinct consumer portals. In embodiments, the one or more systems ofthe intelligent data layer system is initiated as a virtualizedcontainer to perform system-specific intelligent data layer systemfunctions. In embodiments, the virtualized container is executed on acloud-processing architecture. In embodiments, the virtualized containeris configured with a consumer portal-specific instance of at least oneof the ingestion system, the analysis system, or the intelligencederiving system. In embodiments, the intelligent data layer systemingests data from a plurality of types of data sources including datachannels, on-demand data sources, and published data sources. Inembodiments, the intelligent data layer system derives intelligence withthe intelligence deriving system for a plurality of intelligenceconsumer portals. In embodiments, an intelligent data layer controltower adapts a configuration of the ingestion system based on a type ofdata source for a data source selected by the intelligent data layercontrol tower for each of a plurality of instances of ingestion. Inembodiments, an intelligent data layer control tower adapts aconfiguration of the ingestion system, the analysis system, and theintelligence deriving system. In embodiments, the intelligent data layeringests data differently from a single data source based on ingestionrequirements accessible through the request. In embodiments, theintelligent data layer system further includes a plurality ofsystem-focused probes that provide near real-time context of a range ofaspects of system services. In embodiments, the system-focused probesinclude probes that monitor source data for source data impactingactivity and that signal to an intelligent data layer control tower fortaking action within the system based on a projected impact of thesource data impacting activity. In embodiments, the system-focusedprobes monitor for time-related triggers for data sources, includingearly release of an update of source data, delayed release of an updateof source data, and an announcement of new sources of data. Inembodiments, the ingestion system monitors a port on a data network foran indication of data availability at a data source. In embodiments, thesystem develops a multi-dimensional understanding of source data valueby applying a value determination cross matrix that facilitates mappinga data source-relevant value of the source data to a consumerportal-relevant value of the source data.

In embodiments, a method of operating an intelligent data layerincludes: receiving an intelligence request from an intelligenceconsumer portal; determining at least one data source for derivingintelligence for the consumer portal based on the received request;configuring an ingestion system based on ingestion parameters andparsing rules in a layer configuration data store for the at least onedata source; configuring an analysis system based on one or moreanalysis algorithms in the layer configuration data store for the atleast one data source; configuring an intelligence deriving system basedon information in the request and available intelligence services in anintelligence service system; and operating the system to ingest datafrom the at least one data source using the ingestion system, analyzethe ingested data from the at least one data source using the analysissystem, derive a set of intelligence data from at least one of theingested data form the at least one data source and an outcome of usingthe analysis system, and communicating the set of intelligence data toat least one of the consumer portal or an intelligent data layer store.Some embodiments further include storing in a computer-readable storagesystem results of operations of one or more systems of the intelligentdata layer. In embodiments, the one or more systems includes theingestion system, the analysis system, and the intelligence derivingsystem. In embodiments, the results of operations include intermediateresults of at least one of the one or more systems and at least onerole-adapted final result variant of the intermediate results. Inembodiments, configuring the analysis system is further based onconsumer intelligence objectives of the request. In embodiments,configuring the analysis system is further based on aspects of therequest. Some embodiments further include operating an intelligent datalayer control tower that configures and operates the intelligent datalayer by communicating control sequences with the ingestion system, theanalysis system, and the intelligence deriving system. Some embodimentsfurther include receiving, through an algorithm portal of an intelligentdata layer control tower, at least one analysis algorithm used by theanalysis system. In embodiments, the ingestion system applies theparsing rules to content of data sources to determine structure of thecontent and relationships among elements in the data. In embodiments,the ingestion system applies the parsing rules to content of datasources thereby generating characterization data that includes anintended meaning of elements of the data and relationships among thedata, structures of the data, and meaning of the data parsed from thecontent. In embodiments, the ingestion system assigns a relationshipattribute to a pair of data values that are configured as parent/childin a hierarchy of the data source. In embodiments, the ingestion systemis configured to maintain a schedule of collection activity for one ormore data sources. In embodiments, the ingestion system is configured toparse source data according to at least one of a specification of thesource or a context of a supply chain for an ingestion instance of thesource data. In embodiments, the ingestion system communicates ingesteddata, results of ingestion, and results of parsing, to an intelligentdata layer control tower of the system.

In embodiments, a location of the data source is a source addressselected from a list of source address consisting of a universal recordlocator, port number, stream identifier, publication and/or broadchannel, sensor output address. In embodiments, the analysis systemcompares data from the data source against a target use of intelligencederived from a data source to determine a degree of fitness for use ofthe data source by the intelligence deriving system. In embodiments, theanalysis system analyzes ingestion system results for meeting at leastone consumption target requirement of the consumer portal request. Inembodiments, the consumption target requirement includes one or more ofa validity time constraint, an accuracy constraint, a frequency ofupdate constraint, or relevance to a consumption subject matter focus.

In embodiments, the analysis system configures data regarding theingested data for one or more system uses from a list of uses includingadvertisements that characterize the ingested data in terms of potentialintelligence value, indexing schemes for offering intelligence deriveddata in a marketplace, searching intelligence derived data byidentifying keywords, terms, and values associated with the ingesteddata. In embodiments, the analysis system estimates a value ofintelligence data derived from the ingested data for a range of consumerportals to enable setting costs for consuming the intelligence dataderived from the ingested data. In embodiments, one or more systems ofthe intelligent data layer is configured as a micro-service architecturefor isolated and independent operation of instances of the one or moresystems for a plurality of distinct consumer portals. In embodiments,the one or more systems of the intelligent data layer system isinitiated as a virtualized container to perform system-specificintelligent data layer system functions. In embodiments, the virtualizedcontainer is executed on a cloud-processing architecture. Inembodiments, the virtualized container is configured with a consumerportal-specific instance of at least one of the ingestion system, theanalysis system, or the intelligence deriving system. In embodiments,the intelligent data layer system ingests data from a plurality of typesof data sources including data channels, on-demand data sources, andpublished data sources. In embodiments, the intelligent data layerderives intelligence with the intelligence deriving system for aplurality of intelligence consumer portals.

In embodiments, an intelligent data layer control tower adapts aconfiguration of the ingestion system based on a type of data source fora data source selected by the intelligent data layer control tower foreach of a plurality of instances of ingestion. In embodiments, anintelligent data layer control tower adapts a configuration of theingestion system, the analysis system, and the intelligence derivingsystem. In embodiments, the intelligent data layer ingests datadifferently from a single data source based on ingestion requirementsaccessible through the request. In embodiments, the intelligent datalayer further includes a plurality of system-focused probes that providenear real-time context of a range of aspects of system services. Inembodiments, the system-focused probes include probes that monitorsource data for source data impacting activity and that signal to anintelligent data layer control tower for taking action within the systembased on a projected impact of the source data impacting activity. Inembodiments, the system-focused probes monitor for time-related triggersfor data sources, including early release of an update of source data,delayed release of an update of source data, and an announcement of newsources of data. In embodiments, the ingestion system monitors a port ona data network for an indication of data availability at a data source.In embodiments, the intelligent data layer develops a multi-dimensionalunderstanding of source data value by applying a value determinationcross matrix that facilitates mapping a data source-relevant value ofthe source data to a consumer portal-relevant value of the source data.

Network of Intelligent Data Layers

In embodiments, a system of intelligent data layer network elementsincludes: a first intelligent data layer element deriving a first degreeof source data intelligence from a first source of data; a secondintelligent data layer element deriving a second degree of source dataintelligence from a second source of data, the second source of datarepresenting a context of the first source of data; a third intelligentdata layer element forming an intelligent data layer network throughinterconnections with the first intelligent data layer element and thesecond intelligent data layer element and deriving compositeintelligence by processing data from a local source of data with thefirst degree of source data intelligence received through theinterconnections and the second degree of source data intelligencereceived through the interconnections; and a fourth intelligent datalayer element extending the intelligent data layer network throughinterconnection with the third intelligent data layer element andderiving a set of intelligence data structures by processing data from asecond local source with one or more of the first degree of source dataintelligence, the second degree of source data intelligence, or thecomposite intelligence, where deriving a set of intelligence datastructures is based on intelligent data structures requirements of anintelligent data layer consumer element of the set of intelligence datastructures. In embodiments, the first intelligent data layer derivesmarketplace bidding activity intelligence, the second intelligent datalayer derives marketplace settlement activity intelligence, and thethird intelligent data layer derives the composite intelligenceincluding relative impacts of changes in bidding activity on settlementterms. In embodiments, the data from the second local source ismonitored marketplace regulatory compliance and the set of intelligencedata structures includes analysis of regulatory compliance of at leastone of the bidding activity intelligence, the settlement activityintelligence, and the composite intelligence. In embodiments, the datafrom the second local source includes one or more of, raw transactiondata, analyzed transaction data, marketplace data, or financial data fora plurality of transactions in the monitored marketplace.

In embodiments, at least one intelligent data layer element includes anintelligent data layer control tower that configures and operates the atleast one intelligent data layer element by communicating controlsequences with an ingestion system that receives source data, ananalysis system that evaluates a data output of the ingestion system,and an intelligence deriving system that produces a corresponding one ofsource data intelligence, composite intelligence, and the set ofintelligence data structures. In embodiments, the ingestion systemparses content of source data to determine structure of source datacontent and relationships among elements in the source data. Inembodiments, the ingestion system parses a content of data sourcesresulting in generating characterization data that includes an intendedmeaning of data elements and relationships among the data elements,structures of the data, and the intended meaning of the data parsed fromthe content. In embodiments, the ingestion system assigns a relationshipattribute to a pair of data values that are configured as parent/childin a hierarchy of a corresponding source of data. In embodiments, theingestion system is configured to maintain a schedule of collectionactivity for one or more sources of data. In embodiments, the ingestionsystem is configured to parse source data according to at least one of aspecification of the source or a context of a supply chain for aningestion instance of the source data. In embodiments, the analysissystem compares data from the source data against a target use ofcorresponding sourced data intelligence to determine a degree of fitnessfor use of the source of data by the intelligence deriving system. Inembodiments, one or more intelligent data layer network elements in thesystem of intelligent data layer network elements is initiated as avirtualized container of system-specific intelligent data layer elementfunctions.

In embodiments, the virtualized container is executed on acloud-processing architecture. In embodiments, at least one of theintelligent data layer elements ingests data from a plurality of typesof sources of data including data channels, on-demand data sources, andpublished data sources. In embodiments, the ingestion system monitors aport on a data network for an indication of data availability at asource of data. In embodiments, the set of intelligence data structuresincludes a multi-dimensional representation of source data value byapplying a value determination cross matrix that facilitates mapping adata source-relevant value of the source data to a consumerportal-relevant value of the source data. In embodiments, a method ofnetworking intelligent data layer elements including: deriving a firstdegree of source data intelligence from a first source of data with afirst intelligent data layer element; deriving a second degree of sourcedata intelligence with a second intelligent data layer element from asecond source of data, the second source of data representing a contextof the first source of data; forming an intelligent data layer networkthrough interconnections of a third intelligent data layer element withthe first intelligent data layer element and the second intelligent datalayer element and deriving composite intelligence by processing datafrom a local source of data with the first degree of source dataintelligence received through the interconnections and the second degreeof source data intelligence received through the interconnections; andextending the intelligent data layer network through interconnection ofa fourth intelligent data layer element with the third intelligent datalayer element and deriving a set of intelligence data structures byprocessing data from a second local source with one or more of the firstdegree of source data intelligence, the second degree of source dataintelligence, or the composite intelligence, where deriving a set ofintelligence data structures is based on intelligent data structuresrequirements of an intelligent data layer consumer element of the set ofintelligence data structures.

In embodiments, the first intelligent data layer derives marketplacebidding activity intelligence, the second intelligent data layer derivesmarketplace settlement activity intelligence, and the third intelligentdata layer derives the composite intelligence including relative impactsof changes in bidding activity on settlement terms. In embodiments, thedata from the second local source is monitored marketplace regulatorycompliance data and the set of intelligence data structures includesanalysis of regulatory compliance of at least one of the biddingactivity intelligence, the settlement activity intelligence, and thecomposite intelligence. In embodiments, the data from the second localsource includes one or more of, raw transaction data, analyzedtransaction data, marketplace data, or financial data for a plurality oftransactions in the monitored marketplace. In embodiments, at least oneintelligent data layer element includes an intelligent data layercontrol tower that configures and operates the at least one intelligentdata layer element by communicating control sequences with an ingestionsystem that receives source data, an analysis system that evaluates adata output of the ingestion system, and an intelligence deriving systemthat produces a corresponding one of source data intelligence, compositeintelligence, and the set of intelligence data structures. Inembodiments, the ingestion system parses content of source data todetermine structure of source data content and relationships amongelements in the source data. In embodiments, the ingestion system parsesa content of data sources resulting in generating characterization datathat includes an intended meaning of data elements and relationshipsamong the data elements, structures of the data, and the intendedmeaning of the data parsed from the content. In embodiments, theingestion system assigns a relationship attribute to a pair of datavalues that are configured as parent/child in a hierarchy of acorresponding source of data. In embodiments, the ingestion system isconfigured to maintain a schedule of collection activity for one or moresources of data. In embodiments, the ingestion system is configured toparse source data according to at least one of a specification of thesource or a context of a supply chain for an ingestion instance of thesource data. In embodiments, the analysis system compares data from thesource data against a target use of corresponding sourced dataintelligence to determine a degree of fitness for use of the source ofdata by the intelligence deriving system.

In embodiments, one or more intelligent data layer network elements isinitiated as a virtualized container of system-specific intelligent datalayer element functions. In embodiments, the virtualized container isexecuted on a cloud-processing architecture. In embodiments, at leastone of the intelligent data layer elements ingests data from a pluralityof types of sources of data including data channels, on-demand datasources, and published data sources. In embodiments, the ingestionsystem monitors a port on a data network for an indication of dataavailability at a source of data. In embodiments, the set ofintelligence data structures includes a multi-dimensional representationof source data value by applying a value determination cross matrix thatfacilitates mapping a data source-relevant value of the source data to aconsumer portal-relevant value of the source data.

Source Discovery Using an Intelligent Data Layer

In embodiments, a system for discovering data sources for an intelligentdata layer includes: a computer-readable storage system that stores asource discovery data store that maintains a data store storinginformation about existing data sources; an ingestion system forcapturing data from candidate data sources; an analysis system forevaluating content ingested from the candidate data sources for meetingone or more aspects of a target source discovery criteria; a similarityengine that produces a degree of similarity signal indicative of adegree of similarity of the candidate data source to at least one of theexisting data sources; a relevance engine that produces a degree ofusefulness signal indicative of a utility of the candidate source forproducing at least one intelligence outcome; and an intelligent datalayer control tower that applies artificial intelligence techniques fordetermining at least one of ingestion actions for the ingestion systemand analysis actions for the analysis engine, and for making adetermination of use of the candidate data source. In embodiments, theintelligent data layer control tower applies machine learning to trainthe artificial intelligence techniques. In embodiments, the intelligentdata layer is integrated into a marketplace system of systems. Inembodiments, the marketplace system of systems is an automated marketorchestration system of systems. In embodiments, the intelligent datalayer control tower determines that at least one integration actionincludes capturing information from and about candidate sources. Inembodiments, the intelligent data layer control tower determines that atleast one integration action includes advertising for candidate sources.In embodiments, the intelligent data layer control tower determines thatat least one integration action includes contacting a plurality of knowncontent sources with sets of criteria that are descriptive of a type ofcontent. In embodiments, the ingestion system adapts at least a portionof a set of criteria for seeking a source of data by performing at leastone of adjusting a range of a value that is descriptive of target sourcedata, or broadening the set of criteria by abstracting at least one datarequirement.

In embodiments, the intelligent data layer control tower suggests sourcecontent discovery criteria based on analysis of existing sources, basedon requests for variation of intelligence from consumers of theintelligent data layer, and feedback relating to usefulness of existingsources. In embodiments, the ingestion system adapts an originalingestion profile for one or more existing data sources thereby causingingestion of content from the one or more existing data sources that isexcluded from ingestion under the original ingestion profile. Inembodiments, the ingestion system filters data from the candidate datasources based on compliance with at least a portion of a target contentingestion criteria and forwards data from the candidate data source thatis accepted through the filter to the analysis engine. In embodiments,the target content ingestion criteria includes requirements of a dataformat, a language, and a minimum precision. In embodiments, theingestion system provides source discovery status information to theintelligent data layer control tower for candidate sources of data. Inembodiments, the analysis system processes data forwarded by theingestion system to determine compliance with source discovery criteria.In embodiments, the source discovery criteria includes consistency ofsource terminology. In embodiments, the source discovery criteriaincludes consistency of terminology in the candidate data source toterminology of at least one of the existing data sources.

In embodiments, the analysis system applies a data stabilizationalgorithm to a portion of the data from the candidate source, a resultof which is compared to a data stability criteria of the sourcediscovery criteria. In embodiments, the similarity engine determines adegree of similarity of a portion of the candidate source data and atleast one existing source of data by comparing data values of theportion to data values of a portion of an existing source of data. Inembodiments, a degree of usefulness signal includes a predicted impacton intelligence derivable from the candidate source used by one or moreintelligence derivation algorithms.

In embodiments, the degree of usefulness signal includes an indicationthat a corresponding candidate source is to be added to a list ofapproved sources.

In embodiments, a method of discovering data sources for an intelligentdata layer includes: storing a source discovery data store thatmaintains a data store storing information about existing data sourcesin a computer-readable storage system; capturing data from candidatedata sources with an ingestion system; evaluating content ingested fromthe candidate data sources for meeting one or more aspects of a targetsource discovery criteria with an analysis system; producing a degree ofsimilarity signal indicative of a degree of similarity of the candidatedata source to at least one of the existing data sources with asimilarity engine; producing a degree of usefulness signal indicative ofa utility of the candidate source for producing at least oneintelligence outcome with a relevance engine; and applying artificialintelligence techniques with an intelligent data layer control tower fordetermining at least one of ingestion actions for the ingestion systemand analysis actions for the analysis engine, and for making adetermination of use of the candidate data source. In embodiments, theintelligent data layer control tower applies machine learning to trainthe artificial intelligence techniques. In embodiments, the intelligentdata layer is integrated into a marketplace system of systems. Inembodiments, the marketplace system of systems is an automated marketorchestration system of systems. In embodiments, the intelligent datalayer control tower determines that at least one integration actionincludes capturing information from and about candidate sources. Inembodiments, the intelligent data layer control tower determines that atleast one integration action includes advertising for candidate sources.In embodiments, the intelligent data layer control tower determines thatat least one integration action includes contacting a plurality of knowncontent sources with sets of criteria that are descriptive of a type ofcontent. In embodiments, the ingestion system adapts at least a portionof a set of criteria for seeking a source of data by performing at leastone of adjusting a range of a value that is descriptive of target sourcedata, or broadening the set of criteria by abstracting at least one datarequirement.

In embodiments, the intelligent data layer control tower suggests sourcecontent discovery criteria based on analysis of existing sources, basedon requests for variation of intelligence from consumers of theintelligent data layer, and feedback relating to usefulness of existingsources. In embodiments, the ingestion system adapts an originalingestion profile for one or more existing data sources thereby causingingestion of content from the one or more existing data sources that isexcluded from ingestion under the original ingestion profile. Inembodiments, the ingestion system filters data from the candidate datasources based on compliance with at least a portion of a target contentingestion criteria and forwards data from the candidate data source thatis accepted through the filter to the analysis engine. In embodiments,the target content ingestion criteria includes requirements of a dataformat, a language, and a minimum precision. In embodiments, theingestion system provides source discovery status information to theintelligent data layer control tower for candidate sources of data. Inembodiments, the analysis system processes data forwarded by theingestion system to determine compliance with source discovery criteria.In embodiments, the source discovery criteria includes consistency ofsource terminology. In embodiments, the source discovery criteriaincludes consistency of terminology in the candidate data source toterminology of at least one of the existing data sources. Inembodiments, the analysis system applies a data stabilization algorithmto a portion of the data from the candidate source, a result of which iscompared to a data stability criteria of the source discovery criteria.In embodiments, the similarity engine determines a degree of similarityof a portion of the candidate source data and at least one existingsource of data by comparing data values of the portion to data values ofa portion of an existing source of data. In embodiments, a degree ofusefulness signal includes a predicted impact on intelligence derivablefrom the candidate source used by one or more intelligence derivationalgorithms. In embodiments, the degree of usefulness signal includes anindication that a corresponding candidate source is to be added to alist of approved sources.

Data and Networking Pipeline for Market Orchestration

In embodiments, a method of adapting a route for delivery of asset datato a marketplace orchestration interface through a network pipeline isembodied as a set of computer-readable instructions that is executed bya set of one or more processors and including: identifying a set ofasset-centric network resources in the network pipeline, a portion ofthe set of asset-centric network resources providing an interface to anasset in a set of assets for which transactions are conducted in amarketplace, the interface to the asset further configured to facilitatedelivery of the asset data from the asset through the network pipeline;identifying a set of marketplace-centric network resources in thenetwork pipeline, a portion of the set of marketplace-centric networkresources providing access to a transaction orchestration systeminterface, the transaction orchestration system interface configured foran operator to orchestrate a set of parameters for a set of transactionworkflows of the marketplace involving the set of assets; adapting anetwork path within the network pipeline that enables delivery of theasset data from the asset in the set of assets through the portion ofthe set of asset-centric network resources and through the portion ofthe set of marketplace-centric network resources to the transactionorchestration system interface, where adapting the network path is basedon one or more characteristics of the asset data and at least oneperformance parameter of the network path; and delivering the asset datafrom the asset through a set of infrastructure resources in the adaptednetwork path to the transaction orchestration system interface.

In embodiments, the interface to an asset in a set of assetscommunicates with a native network interface of the asset. Inembodiments, the interface to the asset in the set of assetscommunicates with an asset management resource. In embodiments, theasset data is provided by the asset management resource. In embodiments,the interface to an asset in a set of assets communicates with a digitaltwin of the asset. In embodiments, the asset data is provided by thedigital twin. In embodiments, the set of assets include one or moreassets selected from a list of assets consisting of electronic devices,non-electronic devices, digital rights, services, humans, robots, andon-demand built items. In embodiments, the set of assets includes atleast one interface for a plurality of assets in the set of assets,where the asset data for the plurality of assets is provided to thenetwork pipeline through the at least one interface. In embodiments, theset of asset-centric network resources in the network pipeline includesasset interfacing resources, an asset-centric network resourcecontroller and an asset-localized network data store. In embodiments,the set of asset-centric network resources perform asset-centric datahandling. In embodiments, a portion of the set of asset-centric networkresources is configured by an asset resource controller to workcooperatively with an asset-centric data handling service for processingand storing the asset data in an asset-localized network data store. Inembodiments, the asset resource controller configures the portion of theset of asset-centric network resources based on a result of analysis ofthe asset data by the asset-centric handling service. In embodiments,the asset resource controller retrieves the result of analysis from theasset-localized network data store. In embodiments, the set ofmarketplace-centric network resources includes at least one resourceproviding a service selected from a list of services consisting ofelectronic wallet services, digital twin services, enterprise databaseservices, platform as a service platform services, computer aided designservices, and video game services. In embodiments, adapting the networkpath is based on one or more security characteristics of the asset data.In embodiments, adapting the network path based on the one or moresecurity characteristics of the data includes configuring a path throughthe network pipeline that avoids poor reputation network resources. Inembodiments, adapting the network path is based on one or morejurisdiction characteristics of the asset data. In embodiments, adaptingthe network path based on the one or more jurisdiction characteristicsof the data includes configuring a path through the network pipelinethat avoids network resources based on a jurisdiction of the networkresources.

In embodiments, the set of marketplace-centric network resourcesincludes smart contract-centric network resources that provide aninterface to a set of smart contracts. In embodiments, the set ofmarketplace-centric network resources includes workflow centricresources that provide an interface to a set workflow resources.

In embodiments, a system includes: a set of asset-centric networkresources in a network pipeline, a portion of the set of asset-centricnetwork resources providing an interface to an asset in a set of assetsfor which transactions are conducted in a marketplace, the interface tothe asset further configured to facilitate delivery of asset data fromthe asset through the network pipeline; a set of marketplace-centricnetwork resources in the network pipeline, a portion of the set ofmarketplace-centric network resources providing access to a transactionorchestration system interface, the transaction orchestration systeminterface configured for an operator to orchestrate a set of parametersfor a set of transaction workflows of the marketplace involving the setof assets; and a set of computer-readable instructions that is executedby a set of one or more processors to adapt a network path within thenetwork pipeline based on one or more characteristics of the asset dataand at least one performance parameter of the network path therebyenabling delivery of the asset data from the asset in the set of assetsthrough the portion of the set of asset-centric network resources andthrough the portion of the set of marketplace-centric network resourcesto the transaction orchestration system interface and to deliver theasset data from the asset through a set of infrastructure resources inthe adapted network path to the transaction orchestration systeminterface. In embodiments, the interface to an asset in a set of assetscommunicates with a native network interface of the asset. Inembodiments, the interface to the asset in the set of assetscommunicates with an asset management resource. In embodiments, theasset data is provided by the asset management resource. In embodiments,the interface to an asset in a set of assets communicates with a digitaltwin of the asset. In embodiments, the asset data is provided by thedigital twin. In embodiments, the set of assets includes one or moreassets selected from a list of assets consisting of electronic devices,non-electronic devices, digital rights, services, humans, robots, andon-demand built items. In embodiments, the set of assets includes atleast one interface for a plurality of assets in the set of assets,where the asset data for the plurality of assets is provided to thenetwork pipeline through the at least one interface. In embodiments, theset of asset-centric network resources in the network pipeline includesasset interfacing resources, an asset-centric network resourcecontroller and an asset-localized network data store. In embodiments,the set of asset-centric network resources perform asset-centric datahandling. In embodiments, a portion of the set of asset-centric networkresources is configured by an asset resource controller to workcooperatively with an asset-centric data handling service for processingand storing the asset data in an asset-localized network data store. Inembodiments, the asset resource controller configures the portion of theset of asset-centric network resources based on a result of analysis ofthe asset data by the asset-centric handling service. In embodiments,the asset resource controller retrieves the result of analysis from theasset-localized network data store. In embodiments, the set ofmarketplace-centric network resources includes at least one resourceproviding a service selected from a list of services consisting ofelectronic wallet services, digital twin services, enterprise databaseservices, platform as a service platform services, computer aided designservices, and video game services. In embodiments, to adapt the networkpath is based on one or more security characteristics of the asset data.In embodiments, to adapt the network path based on the one or moresecurity characteristics of the data includes configuring a path throughthe network pipeline that avoids poor reputation network resources. Inembodiments, to adapt the network path is based on one or morejurisdiction characteristics of the asset data. In embodiments, to adaptthe network path based on the one or more jurisdiction characteristicsof the data includes configuring a path through the network pipelinethat avoids network resources based on a jurisdiction of the networkresources. In embodiments, the set of marketplace-centric networkresources includes smart contract-centric network resources that providean interface to a set of smart contracts. In embodiments, the set ofmarketplace-centric network resources includes workflow centricresources that provide an interface to a set workflow resources.

In embodiments, a system includes: a network adaptation system thatautomatically constructs a network infrastructure path in a networkpipeline to deliver data from an asset to a market orchestrationrecipient, the constructed network infrastructure path is automaticallyadapted based on one or more characteristics of the data from the assetand at least one performance parameter for the network infrastructurepath; a network timing adaptation system that automatically adaptsnetwork infrastructure resources in a network pipeline that deliversdata from the asset to the market orchestration recipient fororchestration of a transaction of the asset, where the networkinfrastructure resources are adapted based on at least one of aparameter of the transaction of the asset and a performance parameter ofthe network pipeline; a set of asset-centric network resources thatfacilitate ingestion of the data from the asset into the networkpipeline; and a set of marketplace-centric network resources thatfacilitate delivery of the asset data from the adapted network pipelineto the market orchestration recipient. In embodiments, the networkpipeline delivers the data from the asset to the market orchestrationrecipient for orchestration of a transaction of the asset. Inembodiments, the network timing adaptation system adapts the networkinfrastructure resources in the network pipeline to satisfy a datadelivery timing requirement associated with a transaction workflow forthe asset. In embodiments, the market orchestration recipient is a smartcontract that includes terms, conditions, and parameters for a set oftransaction workflows involving the asset.

In embodiments, adapting the network infrastructure path is based on oneor more security characteristics of the asset data. In embodiments,adapting the network path based on the one or more securitycharacteristics of the data includes configuring a path through thenetwork pipeline that avoids poor reputation network resources. Inembodiments, constructing a network infrastructure path in a networkpipeline includes adjusting a communication protocol that avoidsexposing data from the asset in a context that gives meaning to thedata. In embodiments, adjusting the communication protocol includesdelivering a first portion of the asset data through a first networkpath and a second portion of the asset data through a second networkpath.

In embodiments, constructing a network infrastructure path in a networkpipeline includes adapting the network path for delivering the data fromthe asset so that the network path changes over time. In embodiments,constructing a network infrastructure path in a network pipelineincludes adapting the network path to include at least oneinfrastructure node that is different than infrastructure nodes usedpreviously to deliver the data from the asset. In embodiments,constructing a network infrastructure path in a network pipelineincludes adapting the network infrastructure path so that it isdifferent than prior network infrastructure paths used to deliver thedata from the asset that are recorded in a historical record of networkpaths for the asset data. In embodiments, adapting the networkinfrastructure path based on one or more characteristics of the datafrom the asset includes configuring a plurality of recipients for one ormore portions of the data from the asset, where the plurality ofrecipients is determined from a transaction workflow for the asset.

In embodiments, a method embodied as a set of computer-readableinstructions that is executed by a set of one or more processors andincludes: constructing a network infrastructure path in a networkpipeline with a network adaptation system to deliver data from an assetto a market orchestration recipient, the network infrastructure pathautomatically adapted based on one or more characteristics of the datafrom the asset and at least one performance parameter for the networkinfrastructure path; adapting network infrastructure resources in thenetwork pipeline, with a network timing adaptation system, that deliversdata from the asset to the market orchestration recipient, where thenetwork infrastructure resources are adapted based on at least one of aparameter of a transaction of the asset and a performance parameter ofthe network pipeline; ingesting the data from the asset into the networkpipeline with a set of asset-centric network resources; and deliveringthe asset data from the adapted network pipeline to the marketorchestration recipient with a set of marketplace-centric networkresources. In embodiments, the adapted network pipeline delivers thedata from the asset to the market orchestration recipient fororchestration of a transaction of the asset. In embodiments, the networktiming adaptation system adapts the network infrastructure resources inthe network pipeline to satisfy a data delivery timing requirementassociated with a transaction workflow for the asset. In embodiments,the market orchestration recipient is a smart contract that includesterms, conditions, and parameters for a set of transaction workflowsinvolving the asset.

In embodiments, adapting the network path is based on one or moresecurity characteristics of the asset data. In embodiments, adapting thenetwork path based on the one or more security characteristics of thedata includes configuring a path through the network pipeline thatavoids poor reputation network resources. In embodiments, constructing anetwork infrastructure path in a network pipeline includes adjusting acommunication protocol that avoids exposing data from the asset in acontext that gives meaning to the data. In embodiments, adjusting thecommunication protocol includes delivering a first portion of the assetdata through a first network path and a second portion of the asset datathrough a second network path. In embodiments, constructing a networkinfrastructure path in a network pipeline includes adapting the networkpath for delivering the data from the asset so that the network pathchanges over time. In embodiments, constructing a network infrastructurepath in a network pipeline includes adapting the network path to includeat least one infrastructure node that is different than infrastructurenodes used previously to deliver the data from the asset. Inembodiments, constructing a network infrastructure path in a networkpipeline includes adapting the network infrastructure path so that it isdifferent than prior network infrastructure paths used to deliver thedata from the asset that are recorded in a historical record of networkpaths for the asset data. In embodiments, adapting the networkinfrastructure path based on one or more characteristics of the datafrom the asset includes configuring a plurality of recipients for one ormore portions of the data from the asset, where the plurality ofrecipients is determined from a transaction workflow for the asset.

Asset-Centric Network Pipeline Infrastructure Resources (OperatorInterface))

In embodiments, a system of network infrastructure resources includes: afirst network interface connecting a set of the network infrastructureresources to an asset network resource; a second network interfaceconnecting the set of network infrastructure resources to a second setof network infrastructure resources forming a portion of a network pathfor delivering data from the asset to a marketplace orchestration systeminterface, the network path automatically adapted to deliver the datafrom the asset to the marketplace orchestration system interface basedon one or more characteristics of the data from the asset and at leastone performance parameter for the network path; an asset-centriccontroller communicating with the asset through the first networkinterface and controlling delivery of the data from the asset over theadapted network path; an asset-centric data handling systemcommunicating with the asset through the first network interface andprocessing the data from the asset in support of delivery of the datafrom the asset over the adapted network path; and an asset-centric datastorage facility controlled by the asset-centric controller to receivedata processed by the asset-centric data handling system, where datastored in the asset-centric data storage facility is accessible throughthe second interface by a portion of the second set of networkinfrastructure resources for delivering data from the asset to themarketplace orchestration system interface. In embodiments, the networkpath is further automatically adapted to adjust timing of delivery ofdata from the asset to the marketplace orchestration system interfacebased on at least one of a transaction parameter and a networkperformance parameter. In embodiments, the first network interfacecommunicates with a native network interface of the asset. Inembodiments, the first network interface communicates with an assetmanagement resource. In embodiments, the asset data is provided by theasset management resource. In embodiments, the first network interfacecommunicates with a digital twin of the asset. In embodiments, the datafrom the asset is provided by the digital twin. In embodiments, theasset-centric controller configures the first network interface based ona result of analysis of the data from the asset by the asset-centricdata handling system. In embodiments, the asset-centric controllerretrieves the result of analysis from the asset-centric data storagefacility. In embodiments, the network path is further automaticallyadapted based on one or more security characteristics of the data fromthe asset. In embodiments, further automatically adapting the networkpath based on the one or more security characteristics of the dataincludes configuring the network path to avoid poor reputation networkresources. In embodiments, the automatically adapted network pathincludes an adapted communication protocol that avoids exposing datafrom the asset in a context that gives meaning to the data. Inembodiments, adjusting the adapted communication protocol delivers afirst portion of the data from the asset through a first network pathand a second portion of the data from the asset through a second networkpath. In embodiments, the automatically adapted network path fordelivering the data from the asset changes over time. In embodiments,the automatically adapted network path is different than prior networkinfrastructure paths used to deliver the data from the asset that arerecorded in a historical record of network paths for the data from theasset. In embodiments, the asset-centric controller configures the firstnetwork interface and the asset-centric data handling system so deliveryof the data from the asset over the adapted network path is independentof how data from the asset is received from the asset by the firstnetwork communication interface.

In embodiments, the marketplace orchestration system interface is a setof smart contracts that includes terms, conditions, and parameters for aset of transaction workflows involving the asset. In embodiments, themarketplace orchestration system interface is an interface through whichan operator orchestrates parameters of a set of transaction workflowsassociated with transactions for the assets. In embodiments, theoperator orchestrates parameters of a set of transaction workflows basedon the data from the asset. In embodiments, the marketplaceorchestration system interface is adapted to facilitate orchestratingparameters of a set of transaction workflows involving the assets.

In embodiments, a method includes: connecting via a first networkinterface a set of network infrastructure resources to an asset;connecting via a second network interface the set of networkinfrastructure resources to a second set of network infrastructureresources forming a portion of a network path for delivering data fromthe asset to a marketplace orchestration system interface, the networkpath automatically adapted to deliver the data from the asset to themarketplace orchestration system interface based on one or morecharacteristics of the data from the asset and at least one performanceparameter for the network path; controlling delivery of the data fromthe asset over the adapted network path with an asset-centric controllerdisposed to communicate with the asset through the first networkinterface; processing the data from the asset in support of delivery ofthe data from the asset over the adapted network path with anasset-centric data handling system disposed to communicate with theasset through the first network interface; and storing data processed bythe asset-centric data handling system in an asset-centric data storagefacility controlled by the asset-centric controller, where data storedin the asset-centric data storage facility is accessible through thesecond interface by a portion of the second set of networkinfrastructure resources for delivering data from the asset to themarketplace orchestration system interface.

In embodiments, the network path is further automatically adapted toadjust timing of delivery of data from the asset to the marketplaceorchestration system interface based on at least one of a transactionparameter and a network performance parameter. In embodiments, the firstnetwork interface communicates with a native network interface of theasset. In embodiments, the first network interface communicates with anasset management resource. In embodiments, the asset data is provided bythe asset management resource. In embodiments, the first networkinterface communicates with a digital twin of the asset. In embodiments,the data from the asset is provided by the digital twin. In embodiments,the asset-centric controller configures the first network interfacebased on a result of analysis of the data from the asset by theasset-centric data handling system. In embodiments, the asset-centriccontroller retrieves the result of analysis from the asset-centric datastorage facility. In embodiments, the network path is furtherautomatically adapted based on one or more security characteristics ofthe data from the asset. In embodiments, further automatically adaptingthe network path based on the one or more security characteristics ofthe data includes configuring the network path to avoid poor reputationnetwork resources. In embodiments, the automatically adapted networkpath includes an adapted communication protocol that avoids exposingdata from the asset in a context that gives meaning to the data. Inembodiments, adjusting the adapted communication protocol delivers afirst portion of the data from the asset through a first network pathand a second portion of the data from the asset through a second networkpath. In embodiments, the automatically adapted network path fordelivering the data from the asset changes over time. In embodiments,the automatically adapted network path is different than prior networkinfrastructure paths used to deliver the data from the asset that arerecorded in a historical record of network paths for the data from theasset. In embodiments, the asset-centric controller configures the firstnetwork interface and the asset-centric data handling system so deliveryof the data from the asset over the adapted network path is independentof how data from the asset is received from the asset by the firstnetwork communication interface.

In embodiments, the marketplace orchestration system interface is a setof smart contracts that includes terms, conditions, and parameters for aset of transaction workflows involving the asset. In embodiments, themarketplace orchestration system interface is an interface through whichan operator orchestrates parameters of a set of transaction workflowsassociated with transactions for the assets. In embodiments, theoperator orchestrates parameters of a set of transaction workflows basedon the data from the asset. In embodiments, the marketplaceorchestration system interface is adapted to facilitate orchestratingparameters of a set of transaction workflows involving the assets.

Adapted Path in a Network Pipeline with Integrated Marketplace APIs forOrchestration by an Operator

In embodiments, a system includes: a network adaptation system thatautomatically adapts a network infrastructure path from an asset to amarket orchestration recipient in a network pipeline that delivers assetdata from the asset to the recipient, the network infrastructure pathautomatically adapted based on one or more characteristics of the assetdata and at least one performance parameter for the network path; a setof asset-centric network resources that facilitate ingestion of the datafrom the asset into the adapted network pipeline; a set ofmarketplace-centric network resources that facilitate delivery of theasset data of the adapted network pipeline to the market orchestrationrecipient; and a set of application programming interfaces for amarketplace that executes transaction workflows for conducting atransaction for the asset based on workflow parameters determined by themarket orchestration recipient, the set of application programminginterfaces integrated into an auxiliary system that includes a set ofinterfaces for activating a function of the auxiliary system that whenactivated sends a transaction workflow activation signal to themarketplace through the set of integrated application programminginterfaces, where a portion of the transaction workflows is activated.

In embodiments, the auxiliary system is an electronic wallet platformand the function is a transaction settlement function that, whenactivated causes activation of the portion of the transaction workflowsin the marketplace. In embodiments, the transaction settlement functionsignals to the marketplace to activate the portion of the transactionworkflows through the integrated set of application programminginterfaces. In embodiments, the auxiliary system is a digital twinplatform and the function is a digital twin of a function of the assetthat, when activated causes activation of the portion of the transactionworkflows in the marketplace. In embodiments, the digital twin of thefunction of the asset signals to the marketplace to activate the portionof the transaction workflows through the integrated set of applicationprogramming interfaces. In embodiments, the auxiliary system is anenterprise database platform and the function is a database updatedetection function that, when activated causes activation of the portionof the transaction workflows in the marketplace. In embodiments, thedatabase update detection function signals to the marketplace toactivate the portion of the transaction workflows through the integratedset of application programming interfaces. In embodiments, the auxiliarysystem is a platform as-a-service platform and the function monitors astatus of a service that, when activated causes activation of theportion of the transaction workflows in the marketplace. In embodiments,the function that monitors a status of a service signals to themarketplace to activate the portion of the transaction workflows throughthe integrated set of application programming interfaces. Inembodiments, the auxiliary system is a computer aided design platformand the function is an automated design function that, when activatedcauses activation of the portion of the transaction workflows in themarketplace. In embodiments, the automated design function signals tothe marketplace to activate the portion of the transaction workflowsthrough the integrated set of application programming interfaces. Inembodiments, the auxiliary system is a video game platform and thefunction reflects an action by a user of the video game that, whenactivated causes activation of the portion of the transaction workflowsin the marketplace. In embodiments, the function reflects an action by auser of the video game signals to the marketplace to activate theportion of the transaction workflows through the integrated set ofapplication programming interfaces.

Adapted Path in a Network Pipeline with Integrated Marketplace APIs forOrchestration by for a Smart Contract)

In embodiments, a method includes: adapting a network infrastructurepath from an asset to a smart contract in a network pipeline with anetwork adaptation system, the adapted network pipeline delivering assetdata from the asset to the smart contract, the network infrastructurepath automatically adapted based on one or more characteristics of theasset data and at least one performance parameter for the network path;ingesting the data from the asset into the adapted network pipeline witha set of asset-centric network resources; delivering the ingested assetdata of the adapted network pipeline to the smart contract with a set ofmarketplace-centric network resources; and activating a portion of a setof transaction workflows with a set of application programminginterfaces for a marketplace that executes transaction workflows forconducting a transaction for the asset based on workflow parametersdetermined by the smart contract, the set of application programminginterfaces integrated into an auxiliary system that includes a set ofinterfaces for activating a function of the auxiliary system that whenactivated sends a transaction workflow activation signal to themarketplace through the set of integrated application programminginterfaces, where the portion of the transaction workflows is activated.

In embodiments, the auxiliary system is an electronic wallet platformand the function is a transaction settlement function that, whenactivated causes activation of the portion of the transaction workflowsin the marketplace. In embodiments, the transaction settlement functionsignals to the marketplace to activate the portion of the transactionworkflows through the integrated set of application programminginterfaces. In embodiments, the auxiliary system is a digital twinplatform and the function is a digital twin of a function of the assetthat, when activated causes activation of the portion of the transactionworkflows in the marketplace. In embodiments, the digital twin of thefunction of the asset signals to the marketplace to activate the portionof the transaction workflows through the integrated set of applicationprogramming interfaces. In embodiments, the auxiliary system is anenterprise database platform and the function is a database updatedetection function that, when activated causes activation of the portionof the transaction workflows in the marketplace. In embodiments, thedatabase update detection function signals to the marketplace toactivate the portion of the transaction workflows through the integratedset of application programming interfaces. In embodiments, the auxiliarysystem is a platform as-a-service platform and the function monitors astatus of a service that, when activated causes activation of theportion of the transaction workflows in the marketplace. In embodiments,the function that monitors a status of a service signals to themarketplace to activate the portion of the transaction workflows throughthe integrated set of application programming interfaces. Inembodiments, the auxiliary system is a computer aided design platformand the function is an automated design function that, when activatedcauses activation of the portion of the transaction workflows in themarketplace. In embodiments, the automated design function signals tothe marketplace to activate the portion of the transaction workflowsthrough the integrated set of application programming interfaces. Inembodiments, the auxiliary system is a video game platform and thefunction reflects an action by a user of the video game that, whenactivated causes activation of the portion of the transaction workflowsin the marketplace. In embodiments, the function reflects an action by auser of the video game signals to the marketplace to activate theportion of the transaction workflows through the integrated set ofapplication programming interfaces.

Adapted Timing of a Path in a Network Pipeline with IntegratedMarketplace APIs

In embodiments, a system includes: a network timing adaptation systemthat automatically adapts timing of data transfer through a networkpipeline that delivers data from an asset to a market orchestrationrecipient by adapting one or more network resources of the networkpipeline to control data transfer within the network pipeline, thetiming of data transfer associated with a time requirement for atransaction of the asset, where the one or more network resources areadapted based on at least one of a parameter of the transaction of theasset and a performance parameter of the network pipeline; a set ofasset-centric network resources that facilitate ingestion of the datafrom the asset into the network pipeline; a set of marketplace-centricnetwork resources that facilitate delivery of the ingested asset data ofthe adapted network pipeline to the market orchestration recipient; aset of application programming interfaces for a marketplace thatexecutes transaction workflows for conducting a transaction for theasset based on workflow parameters determined by the marketorchestration recipient according to data from the asset that isdelivered through the adapted one or more network infrastructureresources, the set of application programming interfaces integrated intoan auxiliary system that includes a set of interfaces for activating afunction of the auxiliary system that when activated sends a transactionworkflow activation signal to the marketplace through the integratedapplication programming interfaces whereby a portion of the transactionworkflows is activated.

In embodiments, the auxiliary system is an electronic wallet platformand the function is a transaction settlement function that, whenactivated causes activation of the portion of the transaction workflowsin the marketplace. In embodiments, the transaction settlement functionsignals to the marketplace to activate the portion of the transactionworkflows through the integrated set of application programminginterfaces. In embodiments, the auxiliary system is a digital twinplatform and the function is a digital twin of a function of the assetthat, when activated causes activation of the portion of the transactionworkflows in the marketplace. In embodiments, the digital twin of thefunction of the asset signals to the marketplace to activate the portionof the transaction workflows through the integrated set of applicationprogramming interfaces. In embodiments, the auxiliary system is anenterprise database platform and the function is a database updatedetection function that, when activated causes activation of the portionof the transaction workflows in the marketplace. In embodiments, thedatabase update detection function signals to the marketplace toactivate the portion of the transaction workflows through the integratedset of application programming interfaces. In embodiments, the auxiliarysystem is a platform as-a-service platform and the function monitors astatus of a service that, when activated causes activation of theportion of the transaction workflows in the marketplace. In embodiments,the function that monitors a status of a service signals to themarketplace to activate the portion of the transaction workflows throughthe integrated set of application programming interfaces. Inembodiments, the auxiliary system is a computer aided design platformand the function is an automated design function that, when activatedcauses activation of the portion of the transaction workflows in themarketplace. In embodiments, the automated design function signals tothe marketplace to activate the portion of the transaction workflowsthrough the set of application programming interfaces. In embodiments,the auxiliary system is a video game platform and the function reflectsan action by a user of the video game that, when activated causesactivation of the portion of the transaction workflows in themarketplace. In embodiments, the function reflects an action by a userof the video game signals to the marketplace to activate the portion ofthe transaction workflows through the set of application programminginterfaces.

Adapted Timing of a Path in a Network Pipeline with IntegratedMarketplace APIs for a Smart Contact)

In embodiments, a method includes: adapting timing of data transferthrough a network pipeline that delivers data from an asset to a smartcontract with a network timing adaptation system by adapting one or morenetwork resources of the network pipeline to control data transferwithin the network pipeline, the timing of data transfer associated witha time requirement for a transaction of the asset determined by thesmart contract, where the one or more network resources are adaptedbased on at least one of a parameter of the transaction of the asset anda performance parameter of the network pipeline; ingesting the data fromthe asset into the network pipeline with a set of asset-centric networkresources; delivering the ingested asset data of the adapted networkpipeline to the smart contract with a set of marketplace-centric networkresources; activating a portion of a set of transaction workflows of thetransaction of the asset determined by the smart contract with a set ofapplication programming interfaces for a marketplace that executes theset of transaction workflows based on workflow parameters determined bythe smart contract according to data from the asset that is deliveredthrough the adapted one or more network infrastructure resources, theset of application programming interfaces integrated into an auxiliarysystem that includes a set of interfaces for activating a function ofthe auxiliary system that when activated sends a transaction workflowactivation signal to the marketplace through the integrated applicationprogramming interfaces whereby the portion of the set of transactionworkflows is activated.

In embodiments, the auxiliary system is an electronic wallet platformand the function is a transaction settlement function that, whenactivated causes activation of the portion of the transaction workflowsin the marketplace. In embodiments, the transaction settlement functionsignals to the marketplace to activate the portion of the transactionworkflows through the integrated set of application programminginterfaces. In embodiments, the auxiliary system is a digital twinplatform and the function is a digital twin of a function of the assetthat, when activated causes activation of the portion of the transactionworkflows in the marketplace. In embodiments, the digital twin of thefunction of the asset signals to the marketplace to activate the portionof the transaction workflows through the integrated set of applicationprogramming interfaces. In embodiments, the auxiliary system is anenterprise database platform and the function is a database updatedetection function that, when activated causes activation of the portionof the transaction workflows in the marketplace. In embodiments, thedatabase update detection function signals to the marketplace toactivate the portion of the transaction workflows through the integratedset of application programming interfaces. In embodiments, the auxiliarysystem is a platform as-a-service platform and the function monitors astatus of a service that, when activated causes activation of theportion of the transaction workflows in the marketplace. In embodiments,the function that monitors a status of a service signals to themarketplace to activate the portion of the transaction workflows throughthe integrated set of application programming interfaces. Inembodiments, the auxiliary system is a computer aided design platformand the function is an automated design function that, when activatedcauses activation of the portion of the transaction workflows in themarketplace. In embodiments, the automated design function signals tothe marketplace to activate the portion of the transaction workflowsthrough the integrated set of application programming interfaces. Inembodiments, the auxiliary system is a video game platform and thefunction reflects an action by a user of the video game that, whenactivated causes activation of the portion of the transaction workflowsin the marketplace. In embodiments, the function reflects an action by auser of the video game signals to the marketplace to activate theportion of the transaction workflows through the integrated set ofapplication programming interfaces.

Market Prediction

In embodiments, a market prediction system includes: a machine learningsystem that trains a set of machine-learned models to generate a marketprediction using training data including demand features and outcomes;an artificial intelligence system that receives a request to generate amarket prediction and outputs a market prediction based on themachine-learned models and the request. In embodiments, the marketprediction is a prediction for a parameter of demand in a forward marketfor an asset. In embodiments, the market prediction is a prediction fora parameter of supply in a forward market for an asset. In embodiments,the market prediction is a prediction of a set of terms and/orconditions for a smart contract. In embodiments, the market predictionis based at least in part on crowdsourced data. In embodiments, themarket prediction is based at least in part on behavioral data collectedfrom a set of IoT systems monitoring a set of entities in a set ofenvironments. In embodiments, the artificial intelligence systemincludes a recurrent neural network. In embodiments, the artificialintelligence system includes a convolutional neural network. Inembodiments, the artificial intelligence system includes a combinationof a recurrent neural network and a convolutional neural network. Inembodiments, the set of Internet of Things systems includes a set ofsmart home Internet of Things devices. In embodiments, the set ofInternet of Things systems includes a set of workplace Internet ofThings devices. In embodiments, the set of Internet of Things systemsincludes a set of Internet of Things device to monitor a set of consumergoods stores. In embodiments, the set of entities comprises one or moreof: products, suppliers, producers, manufacturers, retailers,businesses, owners, operators, operating facilities, customers,consumers, workers, mobile devices, wearable devices, distributors,resellers, supply chain infrastructure facilities, supply chainprocesses, logistics processes, reverse logistics processes, demandprediction processes, demand management processes, demand aggregationprocesses, machines, ships, barges, warehouses, maritime ports,airports, airways, waterways, roadways, railways, bridges, tunnels,online retailers, ecommerce sites, demand factors, supply factors,delivery systems, floating assets, points of origin, points ofdestination, points of storage, points of use, networks, informationtechnology systems, software platforms, distribution centers,fulfillment centers, containers, container handling facilities, customs,export control, border control, drones, robots, autonomous vehicles,hauling facilities, drones/robots/AVs, waterways, and portinfrastructure facilities.

In embodiments, the set of environments comprises one or more of: homeof a consumer, retail facilities, manufacturing facilities, supply chainfacilities, ship containers, ship, boat, barge, maritime port, crane,container, container handling facilities, shipyard, maritime dock,warehouse, distribution facilities, fulfillment facilities, fuelingfacilities, refueling facilities, nuclear refueling facilities, wasteremoval facilities, food supply facilities, beverage supply facilities,drone facilities, robot facilities, autonomous vehicle, aircraft,automotive, truck, train, lift, forklift, hauling facilities, conveyor,loading dock, waterway, bridge, tunnel, airport, depot, vehicle station,train station, weigh station, inspection station or point, roadway,railway, highway, customs house, and border control facilities.

In embodiments, a market prediction system includes: A quantum computingsystem that receives a request to generate a market prediction andoutputs a market prediction based on the request. In embodiments, themarket prediction is a prediction for a parameter of demand in a forwardmarket for an asset. In embodiments, the market prediction is aprediction for a parameter of supply in a forward market for an asset.In embodiments, the market prediction is a prediction of a set of termsand/or conditions for a smart contract. In embodiments, the marketprediction is based at least in part on crowdsourced data. Inembodiments, the market prediction is based at least in part onbehavioral data collected from a set of IoT systems monitoring a set ofentities in a set of environments. In embodiments, the set of Internetof Things systems includes a set of smart home Internet of Thingsdevices. In embodiments, the set of Internet of Things systems includesa set of workplace Internet of Things devices. In embodiments, the setof Internet of Things systems includes a set of Internet of Thingsdevice to monitor a set of consumer goods stores. In embodiments, theset of entities comprises one or more of: products, suppliers,producers, manufacturers, retailers, businesses, owners, operators,operating facilities, customers, consumers, workers, mobile devices,wearable devices, distributors, resellers, supply chain infrastructurefacilities, supply chain processes, logistics processes, reverselogistics processes, demand prediction processes, demand managementprocesses, demand aggregation processes, machines, ships, barges,warehouses, maritime ports, airports, airways, waterways, roadways,railways, bridges, tunnels, online retailers, ecommerce sites, demandfactors, supply factors, delivery systems, floating assets, points oforigin, points of destination, points of storage, points of use,networks, information technology systems, software platforms,distribution centers, fulfillment centers, containers, containerhandling facilities, customs, export control, border control, drones,robots, autonomous vehicles, hauling facilities, drones/robots/AVs,waterways, and port infrastructure facilities. In embodiments, the setof environments comprises one or more of: home of a consumer, retailfacilities, manufacturing facilities, supply chain facilities, shipcontainers, ship, boat, barge, maritime port, crane, container,container handling facilities, shipyard, maritime dock, warehouse,distribution facilities, fulfillment facilities, fueling facilities,refueling facilities, nuclear refueling facilities, waste removalfacilities, food supply facilities, beverage supply facilities, dronefacilities, robot facilities, autonomous vehicle, aircraft, automotive,truck, train, lift, forklift, hauling facilities, conveyor, loadingdock, waterway, bridge, tunnel, airport, depot, vehicle station, trainstation, weigh station, inspection station or point, roadway, railway,highway, customs house, and border control facilities.

Market Orchestration and Alternative Lending Platform

A market orchestration platform includes: a machine learning system thattrains a set of machine-learned models to cluster a set of smartcontracts by attribute similarity using training data including smartcontract features and outcomes; an artificial intelligence system thatreceives a request to cluster a set of smart contracts by attributesimilarity and outputs a clustering of a set of smart contracts byattribute similarity based on the machine-learned models and therequest; and a lending platform including: an Internet of Things datacollection platform for monitoring at least one of a set of assets and aset of collateral for a loan, a bond, or a debt transaction.

Some embodiments further include a security monitoring system formonitoring assets and/or collateral based on the data collected by theInternet of Things data collection platform. In embodiments, thesecurity monitoring system uses machine-learned models to determine thecondition or value of items based on data collected by the Internet ofThings data collection platform. In embodiments, the data collected bythe Internet of Things data collection platform is image data, sensordata, or location data. Some embodiments further include a loanmanagement system that enables a loan manager to access information fromthe Internet of Things data collection platform and the securitymonitoring system. In embodiments, the set of machine-learned modelsemploy a convolutional neural network, a recurrent neural network, afeed forward neural network, a long-term/short-term memory (LTSM) neuralnetwork, a self-organizing neural network, and hybrids and combinationsof the foregoing. In embodiments, the set of machine-learned modelsemploy a convolutional neural network, a recurrent neural network, afeed forward neural network, a long-term/short-term memory (LTSM) neuralnetwork, a self-organizing neural network, and hybrids and combinationsof the foregoing.

Market Prediction System and a Lending Platform

In embodiments, a market prediction system includes: a machine learningsystem that trains a set of machine-learned models to generate a marketprediction using training data including market features and outcomes;an artificial intelligence system that receives a request to generate amarket prediction and outputs a market prediction based on themachine-learned models and the request; and a lending platformincluding: an Internet of Things data collection platform for monitoringat least one of a set of assets and a set of collateral for a loan, abond, or a debt transaction.

In embodiments, the market prediction is a prediction for a parameter ofdemand in a forward market for an asset. In embodiments, the marketprediction is a prediction for a parameter of supply in a forward marketfor an asset. In embodiments, the market prediction is a prediction of aset of terms and/or conditions for a smart contract. In embodiments, themarket prediction is based at least in part on crowdsourced data. Inembodiments, the market prediction is based at least in part onbehavioral data collected from a set of IoT systems monitoring a set ofentities in a set of environments. In embodiments, the artificialintelligence system includes a recurrent neural network. In embodiments,the artificial intelligence system includes a convolutional neuralnetwork. In embodiments, the artificial intelligence system includes acombination of a recurrent neural network and a convolutional neuralnetwork. In embodiments, the set of Internet of Things systems includesa set of smart home Internet of Things devices. In embodiments, the setof Internet of Things systems includes a set of workplace Internet ofThings devices. In embodiments, the set of Internet of Things systemsincludes a set of Internet of Things device to monitor a set of consumergoods stores. In embodiments, the set of entities comprises one or moreof: products, suppliers, producers, manufacturers, retailers,businesses, owners, operators, operating facilities, customers,consumers, workers, mobile devices, wearable devices, distributors,resellers, supply chain infrastructure facilities, supply chainprocesses, logistics processes, reverse logistics processes, demandprediction processes, demand management processes, demand aggregationprocesses, machines, ships, barges, warehouses, maritime ports,airports, airways, waterways, roadways, railways, bridges, tunnels,online retailers, ecommerce sites, demand factors, supply factors,delivery systems, floating assets, points of origin, points ofdestination, points of storage, points of use, networks, informationtechnology systems, software platforms, distribution centers,fulfillment centers, containers, container handling facilities, customs,export control, border control, drones, robots, autonomous vehicles,hauling facilities, drones/robots/AVs, waterways, and portinfrastructure facilities. In embodiments, the set of environmentscomprises one or more of: home of a consumer, retail facilities,manufacturing facilities, supply chain facilities, ship containers,ship, boat, barge, maritime port, crane, container, container handlingfacilities, shipyard, maritime dock, warehouse, distribution facilities,fulfillment facilities, fueling facilities, refueling facilities,nuclear refueling facilities, waste removal facilities, food supplyfacilities, beverage supply facilities, drone facilities, robotfacilities, autonomous vehicle, aircraft, automotive, truck, train,lift, forklift, hauling facilities, conveyor, loading dock, waterway,bridge, tunnel, airport, depot, vehicle station, train station, weighstation, inspection station or point, roadway, railway, highway, customshouse, and border control facilities.

Some embodiments further include a security monitoring system formonitoring assets and/or collateral based on the data collected by theInternet of Things data collection platform. In embodiments, thesecurity monitoring system uses machine-learned models to determine thecondition or value of items based on data collected by the Internet ofThings data collection platform. In embodiments, the data collected bythe Internet of Things data collection platform is image data, sensordata, or location data. Some embodiments further include a loanmanagement system that enables a loan manager to access information fromthe Internet of Things data collection platform and the securitymonitoring system. In embodiments, the set of machine-learned modelsemploy a convolutional neural network, a recurrent neural network, afeed forward neural network, a long-term/short-term memory (LTSM) neuralnetwork, a self-organizing neural network, and hybrids and combinationsof the foregoing.

Enterprise Access Layer and Market Prediction System

In embodiments, a system includes: a machine learning system that trainsa set of machine-learned models to generate a market prediction usingtraining data including market features and outcomes; an artificialintelligence system that receives a request to generate a marketprediction and outputs a market prediction based on the machine-learnedmodels and the request; a network access layer including a processor andstorage hardware in communication with the processor, where the storagehardware includes instructions that when executed by the processorperform operations, and where the operations include: monitoring aplurality of public market participants via an interface system of anetwork access layer, where the network access layer is controlled by anenterprise and corresponds to an intelligence system that hostsexchangeable enterprise digital assets; receiving, at the network accesslayer via the interface system, an indication that a monitored publicmarket participant requests a digital asset candidate; determining, bythe intelligence system of the network access layer, whether the digitalasset candidate matches an asset available in a digital wallet systemassociated with the network access layer; and in response to the digitalasset candidate matching the asset available in the digital walletsystem: identifying a set of asset controls managed by a permissionsystem of the network asset layer, where the permission system isconfigured to assign the set of asset controls to exchangeableenterprise digital assets in the digital wallet system; determiningwhether a transaction with the monitored public market participant thatinvolves the asset available in the digital wallet system satisfies anasset control criteria corresponding to the asset available, where theasset control criteria indicates that a threshold number of the set ofasset controls have been violated; and in response to determining thatthe transaction with the monitored public market participant thatinvolves the asset available in the digital wallet system satisfies theasset control criteria, generating a message data packet requesting anactual transaction with the monitored public market participantinvolving the asset available, where the message data packet isconfigured for communication via the interface system.

In embodiments, the asset is available in a hot wallet of the digitalwallet system. In embodiments, the asset is available in a cold walletof the digital wallet system. In embodiments, the asset is available ina custodial wallet of the digital wallet system. In embodiments, theoperations further comprise: receiving a response message from themonitored public market participant; and determining that the responsemessage indicates an acknowledgement to fulfill the request for theactual transaction; and

facilitating fulfillment of the actual transaction. In embodiments,facilitating fulfillment of the actual transaction includes storing adigital form of the asset in a public append-only data structure torepresent execution of the actual transaction. In embodiments,facilitating fulfillment of the asset request includes: signing theactual transaction involving the asset on a cold wallet; and relayingthe signed transaction using a hot wallet of the digital wallet systemthat is associated with the cold wallet. In embodiments, storing thedigital form of the asset to a public append-only data structurefacilitating uses at least one key from a hot wallet of the digitalwallet system. In embodiments, storing the digital form of the asset toa public append-only data structure facilitating uses at least one keyfrom a cold wallet of the digital wallet system. In embodiments, themarket prediction is a prediction for a parameter of demand in a forwardmarket for an asset. In embodiments, the market prediction is aprediction for a parameter of supply in a forward market for an asset.In embodiments, the market prediction is a prediction of a set of termsand/or conditions for a smart contract. In embodiments, the marketprediction is based at least in part on crowdsourced data. Inembodiments, the market prediction is based at least in part onbehavioral data collected from a set of IoT systems monitoring a set ofentities in a set of environments. In embodiments, the artificialintelligence system includes a recurrent neural network. In embodiments,the artificial intelligence system includes a convolutional neuralnetwork. In embodiments, the artificial intelligence system includes acombination of a recurrent neural network and a convolutional neuralnetwork. In embodiments, the set of Internet of Things systems includesa set of smart home Internet of Things devices. In embodiments, the setof Internet of Things systems includes a set of workplace Internet ofThings devices. In embodiments, the set of Internet of Things systemsincludes a set of Internet of Things device to monitor a set of consumergoods stores.

In embodiments, the set of entities comprises one or more of: products,suppliers, producers, manufacturers, retailers, businesses, owners,operators, operating facilities, customers, consumers, workers, mobiledevices, wearable devices, distributors, resellers, supply chaininfrastructure facilities, supply chain processes, logistics processes,reverse logistics processes, demand prediction processes, demandmanagement processes, demand aggregation processes, machines, ships,barges, warehouses, maritime ports, airports, airways, waterways,roadways, railways, bridges, tunnels, online retailers, ecommerce sites,demand factors, supply factors, delivery systems, floating assets,points of origin, points of destination, points of storage, points ofuse, networks, information technology systems, software platforms,distribution centers, fulfillment centers, containers, containerhandling facilities, customs, export control, border control, drones,robots, autonomous vehicles, hauling facilities, drones/robots/AVs,waterways, and port infrastructure facilities. In embodiments, the setof environments comprises one or more of: home of a consumer, retailfacilities, manufacturing facilities, supply chain facilities, shipcontainers, ship, boat, barge, maritime port, crane, container,container handling facilities, shipyard, maritime dock, warehouse,distribution facilities, fulfillment facilities, fueling facilities,refueling facilities, nuclear refueling facilities, waste removalfacilities, food supply facilities, beverage supply facilities, dronefacilities, robot facilities, autonomous vehicle, aircraft, automotive,truck, train, lift, forklift, hauling facilities, conveyor, loadingdock, waterway, bridge, tunnel, airport, depot, vehicle station, trainstation, weigh station, inspection station or point, roadway, railway,highway, customs house, and border control facilities.

Market Orchestration and Enterprise Access Layer

In embodiments, a system includes: a machine learning system that trainsa set of machine-learned models to cluster a set of smart contracts byattribute similarity using training data including smart contractfeatures and outcomes; an artificial intelligence system that receives arequest to cluster a set of smart contracts by attribute similarity andoutputs a clustering of a set of smart contracts by attribute similaritybased on the machine-learned models and the request; a network accesslayer including a processor and storage hardware in communication withthe processor, where the storage hardware includes instructions thatwhen executed by the processor perform operations, and where theoperations include: monitoring a plurality of public market participantsvia an interface system of a network access layer, where the networkaccess layer is controlled by an enterprise and corresponds to anintelligence system that hosts exchangeable enterprise digital assets;receiving, at the network access layer via the interface system, anindication that a monitored public market participant requests a digitalasset candidate; determining, by the intelligence system of the networkaccess layer, whether the digital asset candidate matches an assetavailable in a digital wallet system associated with the network accesslayer; and in response to the digital asset candidate matching the assetavailable in the digital wallet system: identifying a set of assetcontrols managed by a permission system of the network asset layer,where the permission system is configured to assign the set of assetcontrols to exchangeable enterprise digital assets in the digital walletsystem; determining whether a transaction with the monitored publicmarket participant that involves the asset available in the digitalwallet system satisfies an asset control criteria corresponding to theasset available, where the asset control criteria indicates that athreshold number of the set of asset controls have been violated; and inresponse to determining that the transaction with the monitored publicmarket participant that involves the asset available in the digitalwallet system satisfies the asset control criteria, generating a messagedata packet requesting an actual transaction with the monitored publicmarket participant involving the asset available, where the message datapacket is configured for communication via the interface system.

In embodiments, the asset is available in a hot wallet of the digitalwallet system. In embodiments, the asset is available in a cold walletof the digital wallet system. In embodiments, the asset is available ina custodial wallet of the digital wallet system. In embodiments, theoperations further comprise: receiving a response message from themonitored public market participant; and determining that the responsemessage indicates an acknowledgement to fulfill the request for theactual transaction; and

facilitating fulfillment of the actual transaction. In embodiments,facilitating fulfillment of the actual transaction includes storing adigital form of the asset in a public append-only data structure torepresent execution of the actual transaction. In embodiments,facilitating fulfillment of the asset request includes: signing theactual transaction involving the asset on a cold wallet; and relayingthe signed transaction using a hot wallet of the digital wallet systemthat is associated with the cold wallet. In embodiments, storing thedigital form of the asset to a public append-only data structurefacilitating uses at least one key from a hot wallet of the digitalwallet system. In embodiments, storing the digital form of the asset toa public append-only data structure facilitating uses at least one keyfrom a cold wallet of the digital wallet system. Some embodimentsfurther include a security monitoring system for monitoring assetsand/or collateral based on the data collected by the Internet of Thingsdata collection platform. In embodiments, the security monitoring systemuses machine-learned models to determine the condition or value of itemsbased on data collected by the Internet of Things data collectionplatform. In embodiments, the data collected by the Internet of Thingsdata collection platform is image data, sensor data, or location data.

Some embodiments further include a loan management system that enables aloan manager to access information from the Internet of Things datacollection platform and the security monitoring system. In embodiments,the set of machine-learned models employ a convolutional neural network,a recurrent neural network, a feed forward neural network, along-term/short-term memory (LTSM) neural network, a self-organizingneural network, and hybrids and combinations of the foregoing.

Market Orchestration and Market Prediction

In embodiments, a system includes: a machine learning system that trainsa set of machine-learned models to generate a market prediction usingtraining data including market features and outcomes; an artificialintelligence system that receives a request to generate a marketprediction and outputs a market prediction based on the machine-learnedmodels and the request; a machine learning system that trains a set ofmachine-learned models to cluster a set of smart contracts by attributesimilarity using training data including smart contract features andoutcomes; and an artificial intelligence system that receives a requestto cluster a set of smart contracts by attribute similarity and outputsa clustering of a set of smart contracts by attribute similarity basedon the machine-learned models and the request.

In embodiments, the market prediction is a prediction for a parameter ofdemand in a forward market for an asset. In embodiments, the marketprediction is a prediction for a parameter of supply in a forward marketfor an asset. In embodiments, the market prediction is a prediction of aset of terms and/or conditions for a smart contract. In embodiments, themarket prediction is based at least in part on crowdsourced data. Inembodiments, the market prediction is based at least in part onbehavioral data collected from a set of IoT systems monitoring a set ofentities in a set of environments. In embodiments, the artificialintelligence system includes a recurrent neural network. In embodiments,the artificial intelligence system includes a convolutional neuralnetwork. In embodiments, the artificial intelligence system includes acombination of a recurrent neural network and a convolutional neuralnetwork. In embodiments, the set of Internet of Things systems includesa set of smart home Internet of Things devices. In embodiments, the setof Internet of Things systems includes a set of workplace Internet ofThings devices. In embodiments, the set of Internet of Things systemsincludes a set of Internet of Things device to monitor a set of consumergoods stores.

In embodiments, the set of entities comprises one or more of: products,suppliers, producers, manufacturers, retailers, businesses, owners,operators, operating facilities, customers, consumers, workers, mobiledevices, wearable devices, distributors, resellers, supply chaininfrastructure facilities, supply chain processes, logistics processes,reverse logistics processes, demand prediction processes, demandmanagement processes, demand aggregation processes, machines, ships,barges, warehouses, maritime ports, airports, airways, waterways,roadways, railways, bridges, tunnels, online retailers, ecommerce sites,demand factors, supply factors, delivery systems, floating assets,points of origin, points of destination, points of storage, points ofuse, networks, information technology systems, software platforms,distribution centers, fulfillment centers, containers, containerhandling facilities, customs, export control, border control, drones,robots, autonomous vehicles, hauling facilities, drones/robots/AVs,waterways, and port infrastructure facilities.

In embodiments, the set of environments comprises one or more of: homeof a consumer, retail facilities, manufacturing facilities, supply chainfacilities, ship containers, ship, boat, barge, maritime port, crane,container, container handling facilities, shipyard, maritime dock,warehouse, distribution facilities, fulfillment facilities, fuelingfacilities, refueling facilities, nuclear refueling facilities, wasteremoval facilities, food supply facilities, beverage supply facilities,drone facilities, robot facilities, autonomous vehicle, aircraft,automotive, truck, train, lift, forklift, hauling facilities, conveyor,loading dock, waterway, bridge, tunnel, airport, depot, vehicle station,train station, weigh station, inspection station or point, roadway,railway, highway, customs house, and border control facilities.

Some embodiments further include a security monitoring system formonitoring assets and/or collateral based on the data collected by theInternet of Things data collection platform.

In embodiments, the security monitoring system uses machine-learnedmodels to determine the condition or value of items based on datacollected by the Internet of Things data collection platform. Inembodiments, the data collected by the Internet of Things datacollection platform is image data, sensor data, or location data. Someembodiments further include a loan management system that enables a loanmanager to access information from the Internet of Things datacollection platform and the security monitoring system. In embodiments,the set of machine-learned models employ a convolutional neural network,a recurrent neural network, a feed forward neural network, along-term/short-term memory (LTSM) neural network, a self-organizingneural network, and hybrids and combinations of the foregoing.

Data and Network Infrastructure Pipeline with Market Orchestration

In embodiments, a system includes: a network adaptation system thatautomatically constructs a network infrastructure path in a networkpipeline to deliver data from an asset to a market orchestrationrecipient, the constructed network infrastructure path is automaticallyadapted based on one or more characteristics of the data from the assetand at least one performance parameter for the network infrastructurepath; a network timing adaptation system that automatically adaptsnetwork infrastructure resources in a network pipeline that deliversdata from the asset to the market orchestration recipient fororchestration of a transaction of the asset, where the networkinfrastructure resources are adapted based on at least one of aparameter of the transaction of the asset and a performance parameter ofthe network pipeline; a set of asset-centric network resources thatfacilitate ingestion of the data from the asset into the networkpipeline; a set of marketplace-centric network resources that facilitatedelivery of the asset data from the adapted network pipeline to themarket orchestration recipient; a machine learning system that trains aset of machine-learned models to cluster a set of smart contracts byattribute similarity using training data including smart contractfeatures and outcomes; and

an artificial intelligence system that receives a request to cluster aset of smart contracts by attribute similarity and outputs a clusteringof a set of smart contracts by attribute similarity based on themachine-learned models and the request.

In embodiments, the network pipeline delivers the data from the asset tothe market orchestration recipient for orchestration of a transaction ofthe asset. In embodiments, the network timing adaptation system adaptsthe network infrastructure resources in the network pipeline to satisfya data delivery timing requirement associated with a transactionworkflow for the asset. In embodiments, the market orchestrationrecipient is a smart contract that includes terms, conditions, andparameters for a set of transaction workflows involving the asset.

In embodiments, adapting the network infrastructure path is based on oneor more security characteristics of the asset data. In embodiments,adapting the network path based on the one or more securitycharacteristics of the data includes configuring a path through thenetwork pipeline that avoids poor reputation network resources. Inembodiments, constructing a network infrastructure path in a networkpipeline includes adjusting a communication protocol that avoidsexposing data from the asset in a context that gives meaning to thedata. In embodiments, adjusting the communication protocol includesdelivering a first portion of the asset data through a first networkpath and a second portion of the asset data through a second networkpath. In embodiments, constructing a network infrastructure path in anetwork pipeline includes adapting the network path for delivering thedata from the asset so that the network path changes over time. Inembodiments, constructing a network infrastructure path in a networkpipeline includes adapting the network path to include at least oneinfrastructure node that is different than infrastructure nodes usedpreviously to deliver the data from the asset. In embodiments,constructing a network infrastructure path in a network pipelineincludes adapting the network infrastructure path so that it isdifferent than prior network infrastructure paths used to deliver thedata from the asset that are recorded in a historical record of networkpaths for the asset data. In embodiments, adapting the networkinfrastructure path based on one or more characteristics of the datafrom the asset includes configuring a plurality of recipients for one ormore portions of the data from the asset, where the plurality ofrecipients is determined from a transaction workflow for the asset.

Trust Networks and Enterprise Access Layers

In embodiments, a system includes: a computer-readable medium thatstores a set of executable instructions; a processing system thatexecutes an enterprise access layer that executes transactions on behalfof an enterprise having a plurality of different users, where theenterprise access layer includes a wallet system, a workflow system, anda permissions system, where: the wallet system manages a plurality ofdigital wallets associated with the enterprise and is configured to:receive a transaction request initiated by a user device associated witha user of the plurality of users, the transaction request requesting atransaction to be executed by the wallet system and having a set ofattributes corresponding to the transaction; select a wallet of theplurality of wallets to execute the transaction based on the set ofattributes; and initiating a transaction workflow from a set ofworkflows based on the selected wallet; when the transaction is atrustless transaction performed on a distributed ledger, the workflowsystem is configured to: obtain a distributed ledger address of acounterparty to the trustless transaction; obtain a trust score based onthe distributed ledger address of the counterparty; determine whether toexecute the transaction based on the trust score corresponding to thecounterparty and a role of the user within the enterprise; and inresponse to determining to allow the trustless transaction, instructingthe wallet system to perform the trustless transaction from the selectedwallet.

In embodiments, the workflow system provides a request to thepermissions system to verify that the user is authorized to perform thetransaction based on the role of the user and one or more transactionattributes. In embodiments, the workflow system provides a trust scorerequest to a trust system, where the request indicates the distributedledger address of the counterparty and the trust system returns thetrust score. In embodiments, the trust system comprises a decentralizednetwork of node computing devices, where each respective node computingdevice independently determines a local trust score for the distributedledger address based on distributed ledger data available to therespective node computing device. In embodiments, the trust score is aconsensus trust score that is based on the local trust scores determinedby the respective node computing devices. In embodiments, the trustsystem comprises a centralized system that monitors a distributednetwork. In embodiments, the transaction workflow determines to executethe transaction in response to verifying that the user is authorized toperform the transaction and the trust score exceeding a trust threshold.In embodiments, in response to performing the trustless transaction, thewallet system generates a transaction record and stores the transactionrecord on a second distributed ledger. In embodiments, the seconddistributed ledger is a private distributed ledger maintained by theenterprise. In embodiments, the wallet system manages a set of privateand public keys on behalf of the entity.

In embodiments, a method for executing user-initiated transactions onbehalf of an enterprise having a plurality of different users includes:receiving, by a wallet system, a transaction request initiated by a userdevice associated with a user of the plurality of users, the transactionrequest requesting a transaction to be executed by the wallet system andhaving a set of attributes corresponding to the transaction; selecting,by the wallet system, a wallet of a plurality of digital walletsassociated with the enterprise to execute the transaction based on theset of attributes; and initiating a transaction workflow from a set ofworkflows based on the selected wallet, where when the transaction is atrustless transaction performed on a distributed ledger, the selectedworkflow executes: obtaining a distributed ledger address of acounterparty to the trustless transaction; obtaining a trust score basedon the distributed ledger address of the counterparty; determiningwhether to execute the transaction based on the trust scorecorresponding to the counterparty and a role of the user within theenterprise; and in response to determining to allow the trustlesstransaction, instructing the wallet system to perform the trustlesstransaction from the selected wallet.

In embodiments, the workflow system provides a request to thepermissions system to verify that the user is authorized to perform thetransaction based on the role of the user and one or more transactionattributes. In embodiments, the workflow system provides a trust scorerequest to a trust system, where the request indicates the distributedledger address of the counterparty and the trust system returns thetrust score. In embodiments, the trust system comprises a decentralizednetwork of node computing devices, where each respective node computingdevice independently determines a local trust score for the distributedledger address based on distributed ledger data available to therespective node computing device. In embodiments, the trust score is aconsensus trust score that is based on the local trust scores determinedby the respective node computing devices. In embodiments, the trustsystem comprises a centralized system that monitors a distributednetwork. In embodiments, the transaction workflow determines to executethe transaction in response to verifying that the user is authorized toperform the transaction and the trust score exceeds a trust threshold.In embodiments, in response to performing the trustless transaction, thewallet system generates a transaction record and stores the transactionrecord on a second distributed ledger. In embodiments, the seconddistributed ledger is a private distributed ledger maintained by theenterprise. In embodiments, the wallet system manages a set of privateand public keys on behalf of the entity.

Data and Network Infrastructure Pipeline and Intelligent Data Layers

An intelligent data layer system includes: a computer-readable storagesystem that stores a layer configuration data store that maintains:ingestion parameters including one or more data structures thatrepresent aspects of one or more of a plurality of data sourcesincluding a source location, an interface protocol, a source dataontology, and an ingestion cost; parsing rules that facilitatedetermining one or more of structure, content, relationships among dataelements, intended meaning of the data elements, or relationships ofdata, structure, and intended meaning; and one or more analysisalgorithms; and a set of one or more processors that execute a set ofcomputer-readable instructions, where the set of one or more processorscollectively: receive an intelligence request pertaining to an asset ina set of assets from an intelligence consumer portal a set ofmarketplace-centric network resources that provide access to atransaction orchestration system interface; determine at least one datasource for deriving intelligence for use by the transactionorchestration system interface based on the received request, the atleast one data source being accessible on a computing network via a setof data source-centric network resources; configure an ingestion systembased on the ingestion parameters and parsing rules in the layerconfiguration data store for ingesting data pertaining to the asset fromthe at least one data source via the set of data source-centric networkresources; configure an analysis system based on the analysis algorithmsin the layer configuration data store for the at least one data source;configure an intelligence deriving system based on information in therequest and available intelligence services in an intelligence servicesystem; operate the system to ingest data from the at least one datasource using the ingestion system, analyze the ingested data from the atleast one data source using the analysis system, and derive a set ofintelligence data from at least one of the ingested data from the atleast one data source or from an outcome of using the analysis system;adapt a network path from the intelligence deriving system through anetwork pipeline that enables delivery of the set of intelligence datato the transaction orchestration system interface based on one or morecharacteristics of the asset ingested from the at least one source andat least one performance parameter of the network path; andcommunicating the set of intelligence data through a set ofinfrastructure resources in the adapted network path to the transactionorchestration system interface.

An intelligent data layer system includes: a set of one or moreprocessors that execute a set of computer-readable instructions, wherethe set of one or more processors collectively: receive an intelligencerequest pertaining to an asset in a set of assets from an intelligenceconsumer portal a set of marketplace-centric network resources thatprovide access to a transaction orchestration system interface;determine at least one data source pertaining to the asset and forderiving intelligence for use by the transaction orchestration systeminterface based on the received request, the at least one data sourcebeing accessible on a computing network via a set of asset-datasource-centric network resources; configure an ingestion system based ona set of ingestion parameters and parsing rules from an ingestion systemconfiguration data store, the ingestion system for ingesting datapertaining to the asset from the at least one data source via the set ofasset-data source-centric network resources; configure an intelligencederiving system based on information in the request and availableintelligence services in an intelligence service system to derive a setof intelligence data from at least one of the ingested data from the atleast one data source; adapt a network path from the intelligencederiving system through a network pipeline that enables delivery of theset of intelligence data to the transaction orchestration systeminterface, where to adapt the network path is based on one or morecharacteristics of the asset captured from the at least one source andat least one performance parameter of the network path; and communicatethe set of intelligence data through a set of infrastructure resourcesin the adapted network path to the transaction orchestration systeminterface.

In embodiments, the set of asset-data source-centric network resourcesis an asset management resource. In embodiments, the data pertaining tothe asset is ingested from the asset management resource. Inembodiments, the at least one data source pertaining to the asset is adigital twin of the asset. In embodiments, the data pertaining to theasset is provided by the digital twin. In embodiments, the set ofasset-data source-centric network resources includes asset interfacingresources, an asset-centric network resource controller and anasset-localized network data store. In embodiments, the asset resourcecontroller configures a portion of the set of asset-data source-centricnetwork resources based on a result of analysis of the data pertainingto the asset data by the asset resource controller. In embodiments, theat least one data source pertaining to the asset is the asset. Inembodiments, the network path is adapted based on one or more securitycharacteristics of the data pertaining to the asset ingested from the atleast one data source. In embodiments, the network path is adapted basedon the one or more security characteristics of the data includesconfiguring a path through the network pipeline that avoids poorreputation network resources. In embodiments, the network path isadapted based on one or more jurisdiction characteristics of the assetdata. In embodiments, the network path is adapted based on the one ormore jurisdiction characteristics of the data includes configuring apath through the network pipeline that avoids network resources based ona jurisdiction of the network resources.

In embodiments, a method of providing intelligence for a transactionorchestration system through an intelligent data layer system includes:receiving an intelligence request pertaining to an asset in a set ofassets from a set of marketplace-centric network resources that provideaccess to a transaction orchestration system interface; determining atleast one data source pertaining to the asset and for derivingintelligence for use by the transaction orchestration system interfacebased on the received request, the at least one data source beingaccessible on a computing network via a set of asset-data source-centricnetwork resources; configuring an ingestion system based on a set ofingestion parameters and parsing rules from an ingestion systemconfiguration data store, the ingestion system for ingesting datapertaining to the asset from the at least one data source via the set ofasset-data source-centric network resources; configuring an intelligencederiving system based on information in the request and availableintelligence services in an intelligence service system to derive a setof intelligence data from at least one of the ingested data from the atleast one data source; adapting a network path from the intelligencederiving system through a network pipeline that enables delivery of theset of intelligence data to the transaction orchestration systeminterface, where adapting the network path is based on one or morecharacteristics of the asset captured from the at least one source andat least one performance parameter of the network path; andcommunicating the set of intelligence data through a set ofinfrastructure resources in the adapted network path to the transactionorchestration system interface.

In embodiments, the set of asset-data source-centric network resourcesis an asset management resource. In embodiments, the data pertaining tothe asset is ingested from the asset management resource. Inembodiments, the at least one data source pertaining to the asset is adigital twin of the asset. In embodiments, the data pertaining to theasset is provided by the digital twin. In embodiments, the set ofasset-data source-centric network resources includes asset interfacingresources, an asset-centric network resource controller and anasset-localized network data store. In embodiments, the asset resourcecontroller configures a portion of the set of asset-data source-centricnetwork resources based on a result of analysis of the data pertainingto the asset data by the asset resource controller. In embodiments, theat least one data source pertaining to the asset is the asset. Inembodiments, the network path is adapted based on one or more securitycharacteristics of the data pertaining to the asset ingested from the atleast one data source.

Enterprise Access Layers and Data Pipeline

A computer-implemented method includes: monitoring a plurality of publicmarket participants via an interface system of a network access layer,where the network access layer is controlled by an enterprise andcorresponds to an intelligence system that hosts exchangeable enterprisedigital assets; receiving, at the network access layer via the interfacesystem, an indication that a monitored public market participantrequests a digital asset candidate; identifying a digital wallet systemassociated with the network access layer, the digital wallet systemmaking available a digital asset of a set of assets for whichtransactions are conducted in a marketplace, the digital wallet furtherconfigured to facilitate delivery of the digital asset through a networkpipeline associated with the network access layer to an interface of themarketplace; determining, by the intelligence system of the networkaccess layer, whether the digital asset candidate matches the digitalasset available in the digital wallet system; and

in response to the digital asset candidate matching the asset availablein the digital wallet system: identifying a set of asset controlsmanaged by a permission system of the network asset layer, where thepermission system is configured to assign the set of asset controls toexchangeable enterprise digital assets in the digital wallet system;determining whether a transaction targeted for the marketplace based ona set of parameters for a set of digital asset transaction workflows ofthe marketplace that are orchestrated by an operator through atransaction orchestration interface the transaction with the monitoredpublic market participant and involving the asset available in thedigital wallet system satisfies an asset control criteria correspondingto the asset available, where the asset control criteria indicates thata threshold number of the set of asset controls have been violated; andin response to determining that the transaction with the monitoredpublic market participant that involves the asset available in thedigital wallet system satisfies the asset control criteria: adapting anetwork path within the network pipeline that enables delivery of thedigital asset to the transaction orchestration system interface, whereadapting the network path is based on one or more characteristics of thedigital asset and at least one performance parameter of the networkpath; and delivering the digital asset and a message requesting anactual transaction with the monitored public market participantinvolving the asset available from the interface system to thetransaction orchestration system interface via the adapted network path.

Some embodiments further include: receiving a message from the monitoredpublic market participant that indicates an acknowledgement to fulfillthe request for the actual transaction; and facilitating fulfillment ofthe actual transaction in the marketplace. In embodiments, facilitatingfulfillment of the actual transaction includes storing a digital form ofthe digital asset in a public append-only data structure to representexecution of the actual transaction. In embodiments, facilitatingfulfillment of the actual transaction includes: signing the actualtransaction involving the asset on a cold wallet; and

relaying the signed transaction using a hot wallet of the digital walletsystem that is associated with the cold wallet. In embodiments, storingthe digital form of the asset to a public append-only data structurefacilitating uses at least one key from a hot wallet of the digitalwallet system. In embodiments, storing the digital form of the asset toa public append-only data structure facilitating uses at least one keyfrom a cold wallet of the digital wallet system. In embodiments, the setof asset controls includes an asset control that matches an accesscontrol for an enterprise entity that submitted the asset to the digitalwallet system. In embodiments, the set of asset controls includes anasset control that indicates a security clearance level for the asset.In embodiments, the set of asset controls transactional detailrequirements for the asset. In embodiments, the digital walletcommunicates with a digital twin of the digital asset. In embodiments,determining whether a transaction targeted for the marketplace with themonitored public market participant and involving the asset available inthe digital wallet system satisfies an asset control criteriacorresponding to the asset available is based on data of the digitalasset provided by the digital twin. In embodiments, adapting the networkpath is based on one or more security characteristics of the digitalasset. In embodiments, adapting the network path based on the one ormore security characteristics of the digital asset includes configuringa path through the network pipeline that avoids poor reputation networkresources. In embodiments, adapting the network path is based on one ormore jurisdiction characteristics of the digital asset. In embodiments,adapting the network path based on the one or more jurisdictioncharacteristics of the digital asset includes configuring a path throughthe network pipeline that avoids network resources based on ajurisdiction of the network resources.

Enterprise Access Layer and Intelligent Data Layers

An intelligent data enterprise network access layer system includes: acomputer-readable storage system that stores a network access layerconfiguration data store that maintains: ingestion parameters includingone or more data structures that represent aspects of one or more of aplurality of data sources including a source location, an interfaceprotocol, a source data ontology, and an ingestion cost; parsing rulesthat facilitate determining one or more of structure, content,relationships among data elements, intended meaning of the dataelements, or relationships of data, structure, and intended meaning; andone or more analysis algorithms; and a set of one or more processors ofthe network access layer that is controlled by an enterprise, the set ofone or more processors that execute a set of computer-readableinstructions, where the set of one or more processors collectively:monitor a plurality of public market participants via an interfacesystem of the network access layer; receive an indication that amonitored public market participant requests a digital asset candidate;determine at least one digital wallet system associated with the networkaccess layer with available assets based on the received request;configure an ingestion system based on the ingestion parameters andparsing rules in the network access layer configuration data store forthe at least one digital wallet; configure an analysis system based onthe analysis algorithms in the network access layer configuration datastore for the at least one digital wallet; configure an intelligencederiving system based on information in the digital asset candidaterequest and available intelligence services in an intelligence servicesystem associated with the network access layer; ingest data from the atleast one digital wallet pertaining to available assets using theingestion system; analyze the ingested data from the at least onedigital wallet using the analysis system; derive a set of intelligencedata from at least one of the ingested data from the at least one datasource or an outcome of using the analysis system; determine based onthe derived set of intelligence data and the request whether the digitalasset candidate matches an available asset; identify a set of assetcontrols managed by a permission system of the network access layer,where the permission system is configured to assign the set of assetcontrols to exchangeable enterprise digital assets in the digital walletsystem; determine whether a transaction with the monitored public marketparticipant that involves a matching available asset satisfies acriteria of the set of asset controls assigned to the matching availableasset, where the criteria indicates that a threshold number of the setof asset controls have been violated; and in response to determiningthat the transaction with the monitored public market participant thatinvolves the matching available asset satisfies the assigned assetcontrol criteria, generate a message data packet requesting an actualtransaction with the monitored public market participant involving theasset available, the message including a portion of the set ofintelligence data, where the message data packet is configured forcommunication via the interface system.

In embodiments, the computer-readable storage system stores anintelligent data layer store that maintains results of operations of oneor more systems of the intelligent data enterprise network access layersystem. In embodiments, the one or more systems includes the ingestionsystem, the analysis system, the permissions system, the interfacesystem, and the intelligence deriving system. In embodiments, the resultof operations includes intermediate results of at least one of the oneor more systems and at least one role-adapted final result variant ofthe intermediate results. In embodiments, to configure the analysissystem is further based on public market participants objectives of therequest. In embodiments, re to configure the analysis system is furtherbased on aspects of the request for a digital asset candidate. Inembodiments, the set of one or more processors is configured in anintelligent data enterprise network access layer control tower thatconfigures and operates the intelligent data enterprise network accesslayer system by communicating control sequences with the ingestionsystem, the analysis system, the permission system, the interfacesystem, and the intelligence deriving system. Some embodiments includean algorithm portal of an intelligent data enterprise network accesslayer control tower of the system through which at least one of theanalysis algorithms is received. In embodiments, the ingestion systemparses content of digital wallets to determine structure of the contentand relationships among elements in the data. In embodiments, thematching asset is available in a hot wallet of the digital walletsystem. In embodiments, the matching asset is available in a cold walletof the digital wallet system. In embodiments, matching asset isavailable in a custodial wallet of the digital wallet system.

Some embodiments include: receiving a response message from themonitored public market participant; and determining that the responsemessage indicates an acknowledgement to fulfill the request for theactual transaction; and facilitating fulfillment of the actualtransaction. In embodiments, facilitating fulfillment of the actualtransaction includes storing a digital form of the asset in a publicappend-only data structure to represent execution of the actualtransaction. In embodiments, facilitating fulfillment of the assetrequest includes: signing the actual transaction involving the asset ona cold wallet; and relaying the signed transaction using a hot wallet ofthe digital wallet system that is associated with the cold wallet. Inembodiments, storing the digital form of the asset to a publicappend-only data structure facilitates use of at least one key from ahot wallet of the digital wallet system. In embodiments, storing thedigital form of the asset to a public append-only data structurefacilitates use of at least one key from a cold wallet of the digitalwallet system. In embodiments, the set of asset controls includes anasset control that matches an access control for an enterprise entitythat submitted the asset to the digital wallet system. In embodiments,the set of asset controls includes an asset control that indicates asecurity clearance level for the asset. In embodiments, the set of assetcontrols transactional detail requirements for the asset.

Cross-Market Transactions with Enterprise Access Layers

A computer-implemented method includes: monitoring a plurality of publicmarket participants in a plurality of markets via an interface system ofa network access layer, where the network access layer is controlled byan enterprise and corresponds to an intelligence system that hostsexchangeable enterprise digital assets; receiving, at the network accesslayer via the interface system, market data from a plurality of datasource feeds, each of the data source feeds corresponding to one or moreof the plurality of markets, the market data including an indicationthat a monitored public market participant requests a digital assetcandidate; processing the market data by performing one or more offiltering, normalizing, deduplicating, organizing, summarizing, andcompressing, the market data; creating a distributed ledger, thedistributed ledger being based on a blockchain; storing the processeddata via the distributed ledger, the processed data being stored via oneor more blocks of the distributed ledger; determining, by theintelligence system of the network access layer, whether the digitalasset candidate matches an asset available in a digital wallet systemassociated with the network access layer; and in response to the digitalasset candidate matching the asset available in the digital wallet

system: identifying a set of asset controls managed by a permissionsystem of the network access layer, where the permission system isconfigured to assign the set of asset controls to exchangeableenterprise digital assets in the digital wallet system; determiningwhether a prospective transaction with the monitored public marketparticipant that involves the asset available in the digital walletsystem satisfies an asset control criteria corresponding to the assetavailable, the prospective transaction determined via a machine learnedmodel, where the asset control criteria is configured in a smartcontract, the smart contract having triggering conditions based on athreshold number of the set of asset control criteria of the prospectivetransaction have been violated and storing the smart contract via thedistributed ledger, the smart contract being stored via one or moreblocks of the distributed ledger; and in response to the smart contractdetermining that the prospective transaction with the monitored publicmarket participant that involves the asset available in the digitalwallet system satisfies the asset control criteria, generating a messagedata packet requesting an actual transaction with the set of assetcontrol criteria of the prospective transaction with the monitoredpublic market participant involving the asset available, where themessage data packet is configured for communication via the interfacesystem.

A computer-implemented method includes: monitoring a plurality of publicmarket participants in a plurality of markets via an interface system ofa network access layer, where the network access layer is controlled byan enterprise and corresponds to an intelligence system that hostsexchangeable enterprise digital assets; receiving, at the network accesslayer via the interface system, market data from the plurality ofmarkets, the market data including an indication that a monitored publicmarket participant requests a digital asset candidate; determining, bythe intelligence system of the network access layer, whether the digitalasset candidate matches an asset available in a digital wallet systemassociated with the network access layer; and in response to the digitalasset candidate matching the asset available in the digital walletsystem: identifying a set of asset controls managed by a permissionsystem of the network access layer, where the permission system isconfigured to assign the set of asset controls to exchangeableenterprise digital assets in the digital wallet system; determiningwhether a prospective transaction with the monitored public marketparticipant that involves the asset available in the digital walletsystem satisfies an asset control criteria corresponding to the assetavailable, the prospective transaction determined via a machine learnedmodel, where the asset control criteria is configured in a smartcontract, the smart contract having triggering conditions based on athreshold number of the set of asset control criteria of the prospectivetransaction have been violated and storing the smart contract via adistributed ledger being based on a blockchain, the smart contract beingstored via one or more blocks of the distributed ledger; and in responseto the smart contract determining that the prospective transaction withthe monitored public market participant that involves the assetavailable in the digital wallet system satisfies the asset controlcriteria, generating a message data packet requesting an actualtransaction that includes the set of asset control criteria of theprospective transaction with the monitored public market participantinvolving the asset available, where the message data packet isconfigured for communication via the interface system.

In embodiments, the asset is available in a hot wallet of the digitalwallet system. In embodiments, the asset is available in a cold walletof the digital wallet system. In embodiments, the asset is available ina custodial wallet of the digital wallet system. Some embodimentsinclude: receiving a response message from the monitored public marketparticipant; determining that the response message indicates anacknowledgement to fulfill the request for the actual transaction; andfacilitating fulfillment of the actual transaction according to the setof asset control criteria of the prospective transaction configured intothe smart contract. In embodiments, facilitating fulfillment of theactual transaction includes storing a digital form of the asset in apublic append-only data structure to represent execution of the actualtransaction, where the public append-only data structure is thedistributed ledger, and storing a digital form of the asset includesbeing stored via one or more blocks of the distributed ledger. Inembodiments, facilitating fulfillment of the asset transaction requestincludes: signing the actual transaction involving the asset on a coldwallet; and relaying the signed transaction using a hot wallet of thedigital wallet system that is associated with the cold wallet. Inembodiments, storing the digital form of the asset to a publicappend-only data structure facilitates using at least one key from a hotwallet of the digital wallet system. In embodiments, storing the digitalform of the asset to a public append-only data structure facilitatesusing at least one key from a cold wallet of the digital wallet system.In embodiments, the set of asset controls includes an asset control thatmatches an access control for an enterprise entity that submitted theasset to the digital wallet system. In embodiments, the set of assetcontrols includes an asset control that indicates a security clearancelevel for the asset. In embodiments, the set of asset controls definetransactional detail requirements for the asset that are configured intothe smart contract.

In embodiments, a system includes: a network access layer including aprocessor and storage hardware in communication with the processor,where the storage hardware includes instructions that when executed bythe processor perform operations, and where the operations include:monitoring a plurality of public market participants in a plurality ofmarkets via an interface system of a network access layer, where thenetwork access layer is controlled by an enterprise and corresponds toan intelligence system that hosts exchangeable enterprise digitalassets; receiving, at the network access layer via the interface system,market data from the plurality of markets, the market data including anindication that a monitored public market participant requests a digitalasset candidate; and determining, by the intelligence system of thenetwork access layer, whether the digital asset candidate matches anasset available in a digital wallet system associated with the networkaccess layer; and in response to the digital asset candidate matchingthe asset available in the digital wallet system: identifying a set ofasset controls managed by a permission system of the network accesslayer, where the permission system is configured to assign the set ofasset controls to exchangeable enterprise digital assets in the digitalwallet system; determining whether a prospective transaction with themonitored public market participant that involves the asset available inthe digital wallet system satisfies an asset control criteriacorresponding to the asset available, the prospective transactiondetermined via a machine learned model, where the asset control criteriais configured in a smart contract, the smart contract having triggeringconditions based on a threshold number of the set of asset controlcriteria of the prospective transaction have been violated and storingthe smart contract via a distributed ledger being based on a blockchain,the smart contract being stored via one or more blocks of thedistributed ledger; and in response to the smart contract determiningthat the prospective transaction with the monitored public marketparticipant that involves the asset available in the digital walletsystem satisfies the asset control criteria, generating a message datapacket requesting an actual transaction that includes the set of assetcontrol criteria of the prospective transaction with the monitoredpublic market participant involving the asset available, where themessage data packet is configured for communication via the interfacesystem.

In embodiments, the asset is available in a hot wallet of the digitalwallet system. In embodiments, the asset is available in a cold walletof the digital wallet system. In embodiments, the asset is available ina custodial wallet of the digital wallet system. In embodiments, theoperations further comprise: receiving a response message from themonitored public market participant; determining that the responsemessage indicates an acknowledgement to fulfill the request for theactual transaction; and facilitating fulfillment of the actualtransaction according to the set of asset control criteria of theprospective transaction configured into the smart contract.

In embodiments, facilitating fulfillment of the actual transactionincludes storing a digital form of the asset in a public append-onlydata structure to represent execution of the actual transaction, wherethe public append-only data structure is the distributed ledger, andstoring a digital form of the asset includes being stored via one ormore blocks of the distributed ledger. In embodiments, facilitatingfulfillment of the asset request includes: signing the actualtransaction involving the asset on a cold wallet; and relaying thesigned transaction using a hot wallet of the digital wallet system thatis associated with the cold wallet. In embodiments, storing the digitalform of the asset to a public append-only data structure facilitatesusing at least one key from a hot wallet of the digital wallet system.In embodiments, storing the digital form of the asset to a publicappend-only data structure facilitating using at least one key from acold wallet of the digital wallet system. In embodiments, the set ofasset controls includes an asset control that matches an access controlfor an enterprise entity that submitted the asset to the digital walletsystem. In embodiments, the set of asset controls includes an assetcontrol that indicates a security clearance level for the asset. Inembodiments, the set of asset controls defines transactional detailrequirements for the asset that are configured into the smart contract.

Trust and Transactions Summary

In embodiments, a method includes identifying, by a device, anopportunity to engage in a transaction associated with a blockchainaddress of a blockchain ledger; receiving, by the device and fromanother device that is a member of a consensus trust network, aconsensus trust score that is associated with the blockchain address;determining, by the device, whether to engage in the transaction withthe blockchain address, wherein the determining is based on theconsensus trust score received from the consensus trust network; andperforming, by the device, an action to initiate an engagement in thetransaction in response to determining to engage in the transaction withthe blockchain address based on the consensus trust score.

In embodiments, a method includes receiving, by a device, informationthat associates a blockchain address with fraudulent activity, whereinthe blockchain address is associated with a blockchain ledger;generating, by the device, a first trust score for the blockchainaddress, wherein the local node trust score is based on the information;receiving, by the device and from at least two other devices, at leasttwo additional trust scores for the blockchain address; determining, bythe device, a consensus trust score based on the first trust score andthe at least two additional trust scores; and generating, by the device,a blockchain entry on the blockchain ledger, a blockchain entry thatassociates the consensus trust score with the blockchain address. Insome embodiments, the device and the at least two other devices aremembers of a trust network of devices that determine consensus trustscores for blockchain addresses of the blockchain ledger. In someembodiments, the device is configured to generate the blockchain entryon the blockchain ledger in response to a request on the blockchainledger for a trust evaluation of the blockchain address. In someembodiments, the device is configured to generate the blockchain entryon the blockchain ledger in response to an activity of the blockchainaddress on the blockchain ledger. In some embodiments, the device isconfigured to generate the blockchain entry on the blockchain ledger inresponse to a transaction on the blockchain ledger, wherein thetransaction is associated with the blockchain address. In someembodiments, the device is further configured to monitor the blockchainledger to detect transaction with the blockchain address. In someembodiments, the blockchain entry includes a blockchain report thatprovides a basis for the consensus trust rating. Some embodimentsfurther include generating, on the blockchain ledger, a fraud entryassociated with the blockchain address, wherein the fraud entry is basedon a change of a consensus trust rating associated with the blockchainaddress. In some embodiments, the consensus trust score is based on afirst reputation score associated with the device and additionalreputation scores associated with each of the at least two otherdevices. In some embodiments, determining the consensus trust scorefurther includes weighting the trust score generated by and/or receivedfrom a respective device, wherein the weighting is based on thereputation score associated with the respective device. In someembodiments, the reputation score associated with a respective device isbased on an amount of work performed by the respective device ingenerating trust scores for blockchain addresses of the blockchainledger. In some embodiments, determining the consensus trust scorefurther includes excluding, from the determining of the consensus trustscore, an outlier trust score that is statistically inconsistent withother trust scores included in the determining of the consensus trustscore. In some embodiments, determining the consensus trust scoreincludes determining that an average variance of the trust scoresincluded in the determining of the consensus trust score is within athreshold variance.

In embodiments, a method includes receiving, by a device, a request togenerate a trust score for a blockchain address associated with ablockchain ledger; determining, by the device, information thatassociates the blockchain address with fraudulent activity; generating,by the device, a first trust score for the blockchain address, whereinthe local node trust score is based on the information; transmitting, bythe device, the first trust score associated with the blockchain addressto another device; receiving, by the device and from another device, aconsensus trust score for the blockchain address, wherein the consensustrust score is based on the first trust score and at least twoadditional trust scores associated with the blockchain address; andgenerating, by the device, a blockchain entry on the blockchain ledger,a blockchain entry that associates the consensus trust score with theblockchain address. In some embodiments, the device and the other deviceare members of a trust network of devices that determine consensus trustscores for blockchain addresses of the blockchain ledger. In someembodiments, the device is configured to generate the blockchain entryon the blockchain ledger in response to a request on the blockchainledger for a trust evaluation of the blockchain address. In someembodiments, the device is configured to generate the blockchain entryon the blockchain ledger in response to an activity of the blockchainaddress on the blockchain ledger. In some embodiments, the device isconfigured to generate the blockchain entry on the blockchain ledger inresponse to a transaction on the blockchain ledger, wherein thetransaction is associated with the blockchain address. In someembodiments, the device is further configured to monitor the blockchainledger to detect transaction with the blockchain address. In someembodiments, the blockchain entry includes a blockchain report thatprovides a basis for the consensus trust rating. Some embodimentsfurther include generating, on the blockchain ledger, a fraud entryassociated with the blockchain address, wherein the fraud entry is basedon a change of a consensus trust rating associated with the blockchainaddress. In some embodiments, the consensus trust score is based on afirst reputation score associated with the device and an additionalreputation score associated with the other device. In some embodiments,determining the consensus trust score includes weighting the trust scoregenerated by and/or received from a respective device, wherein theweighting is based on the reputation score associated with therespective device. In some embodiments, the reputation score associatedwith a respective device is based on an amount of work performed by therespective device in generating trust scores for blockchain addresses ofthe blockchain ledger. In some embodiments, determining the consensustrust score includes excluding, from the determining of the consensustrust score, an outlier trust score that is statistically inconsistentwith other trust scores included in the determining of the consensustrust score. In some embodiments, determining the consensus trust scoreincludes determining that an average variance of the trust scoresincluded in the determining of the consensus trust score is within athreshold variance.

In embodiments, a method includes receiving, by a device, informationthat associates a blockchain address with fraudulent activity, whereinthe blockchain address is associated with a blockchain ledger;processing, by the device, the information with a machine learningmodel, wherein the machine learning model is configured to generatetrust scores based on information that associates blockchain addresseswith fraudulent activity; receiving, by the device, an output of themachine learning model, wherein the output includes a first trust scorefor the blockchain address; receiving, by the device and from at leasttwo other devices, at least two additional trust scores for theblockchain address; determining, by the device, a consensus trust scorebased on the first trust score and the at least two additional trustscores; and generating, by the device, a blockchain entry on theblockchain ledger, a blockchain entry that associates the consensustrust score with the blockchain address. In some embodiments, the deviceand the at least two other devices are members of a trust network ofdevices that determine consensus trust scores for blockchain addressesof the blockchain ledger. In some embodiments, the device is configuredto generate the blockchain entry on the blockchain ledger in response toa request on the blockchain ledger for a trust evaluation of theblockchain address. In some embodiments, the device is configured togenerate the blockchain entry on the blockchain ledger in response to anactivity of the blockchain address on the blockchain ledger. In someembodiments, the device is configured to generate the blockchain entryon the blockchain ledger in response to a transaction on the blockchainledger, wherein the transaction is associated with the blockchainaddress. In some embodiments, the device is further configured tomonitor the blockchain ledger to detect transaction with the blockchainaddress. In some embodiments, the blockchain entry includes a blockchainreport that provides a basis for the consensus trust rating. Someembodiments further include generating, on the blockchain ledger, afraud entry associated with the blockchain address, wherein the fraudentry is based on a change of a consensus trust rating associated withthe blockchain address. In some embodiments, the consensus trust scoreis based on a first reputation score associated with the device andadditional reputation scores associated with each of the at least twoother devices. In some embodiments, determining the consensus trustscore includes weighting the trust score generated by and/or receivedfrom a respective device, wherein the weighting is based on thereputation score associated with the respective device. In someembodiments, the reputation score associated with a respective device isbased on an amount of work performed by the respective device ingenerating trust scores for blockchain addresses of the blockchainledger. In some embodiments, determining the consensus trust scoreincludes excluding, from the determining of the consensus trust score,an outlier trust score that is statistically inconsistent with othertrust scores included in the determining of the consensus trust score.In some embodiments, determining the consensus trust score includesdetermining that an average variance of the trust scores included in thedetermining of the consensus trust score is within a threshold variance.

In embodiments, a method includes receiving, by a device, informationthat associates a blockchain address with fraudulent activity, whereinthe blockchain address is associated with a blockchain ledger;processing, by the device, the information with a quantum computingsystem, wherein the quantum computing system is configured to generatetrust scores based on information that associates blockchain addresseswith fraudulent activity; receiving, by the device, an output of thequantum computing system, wherein the output includes a first trustscore for the blockchain address; receiving, by the device and from atleast two other devices, at least two additional trust scores for theblockchain address; determining, by the device, a consensus trust scorebased on the first trust score and the at least two additional trustscores; and generating, by the device, a blockchain entry on theblockchain ledger, a blockchain entry that associates the consensustrust score with the blockchain address. In some embodiments, the deviceand the at least two other devices are members of a trust network ofdevices that determine consensus trust scores for blockchain addressesof the blockchain ledger. In some embodiments, the device is configuredto generate the blockchain entry on the blockchain ledger in response toa request on the blockchain ledger for a trust evaluation of theblockchain address. In some embodiments, the device is configured togenerate the blockchain entry on the blockchain ledger in response to anactivity of the blockchain address on the blockchain ledger. In someembodiments, the device is configured to generate the blockchain entryon the blockchain ledger in response to a transaction on the blockchainledger, wherein the transaction is associated with the blockchainaddress. In some embodiments, the device is further configured tomonitor the blockchain ledger to detect transaction with the blockchainaddress. In some embodiments, the blockchain entry includes a blockchainreport that provides a basis for the consensus trust rating. Someembodiments further include generating, on the blockchain ledger, afraud entry associated with the blockchain address, wherein the fraudentry is based on a change of a consensus trust rating associated withthe blockchain address. In some embodiments, the consensus trust scoreis based on a first reputation score associated with the device andadditional reputation scores associated with each of the at least twoother devices. In some embodiments, determining the consensus trustscore includes weighting the trust score generated by and/or receivedfrom a respective device, wherein the weighting is based on thereputation score associated with the respective device. In someembodiments, the reputation score associated with a respective device isbased on an amount of work performed by the respective device ingenerating trust scores for blockchain addresses of the blockchainledger. In some embodiments, determining the consensus trust scoreincludes excluding, from the determining of the consensus trust score,an outlier trust score that is statistically inconsistent with othertrust scores included in the determining of the consensus trust score.In some embodiments, determining the consensus trust score includesdetermining that an average variance of the trust scores included in thedetermining of the consensus trust score is within a threshold variance.In some embodiments, the quantum computing system is configured togenerate trust scores based on a graph clustering analysis of activitiesassociated with the blockchain ledger, wherein the graph clusteringanalysis includes the blockchain address. In some embodiments, thequantum computing system is further configured to detect a market trendassociated with an asset, and the quantum prediction algorithm isconfigured to generate trust scores for respective blockchain addressesbased on an activity of the respective blockchain address that isassociated with the asset. In some embodiments, the quantum computingsystem is further configured to generate trust scores based on a quantumprincipal component analysis of the information associated with theblockchain addresses.

In embodiments, a method includes receiving, by a device, informationthat associates a blockchain address with fraudulent activity, whereinthe blockchain address is associated with a blockchain ledger;generating, by the device, a digital twin of an entity associated withthe blockchain address, wherein a response of the digital twin to astimulus corresponds to a predicted response of the entity to thestimulus; generating, by the device, a first trust score for theblockchain address, wherein the local node trust score is based on ananalysis of the digital twin; receiving, by the device and from at leasttwo other devices, at least two additional trust scores for theblockchain address; determining, by the device, a consensus trust scorebased on the first trust score and the at least two additional trustscores; and generating, by the device, a blockchain entry on theblockchain ledger, a blockchain entry that associates the consensustrust score with the blockchain address. In some embodiments, the deviceand the at least two other devices are members of a trust network ofdevices that determine consensus trust scores for blockchain addressesof the blockchain ledger. In some embodiments, the device is configuredto generate the blockchain entry on the blockchain ledger in response toa request on the blockchain ledger for a trust evaluation of theblockchain address. In some embodiments, the request is associated witha transaction that includes the blockchain address, and the device isconfigured to determine the first trust score based on a simulation ofthe transaction including the digital twin and a behavior of the digitaltwin during the simulation. In some embodiments, the device isconfigured to generate the blockchain entry on the blockchain ledger inresponse to an activity of the blockchain address on the blockchainledger. In some embodiments, the device is configured to generate theblockchain entry on the blockchain ledger in response to a transactionon the blockchain ledger, wherein the transaction is associated with theblockchain address. In some embodiments, the device is furtherconfigured to monitor the blockchain ledger to detect transaction withthe blockchain address. In some embodiments, the blockchain entryincludes a blockchain report that provides a basis for the consensustrust rating. Some embodiments further include generating, on theblockchain ledger, a fraud entry associated with the blockchain address,wherein the fraud entry is based on a change of a consensus trust ratingassociated with the blockchain address. In some embodiments, theconsensus trust score is based on a first reputation score associatedwith the device and additional reputation scores associated with each ofthe at least two other devices. In some embodiments, determining theconsensus trust score includes weighting the trust score generated byand/or received from a respective device, wherein the weighting is basedon the reputation score associated with the respective device. In someembodiments, the reputation score associated with a respective device isbased on an amount of work performed by the respective device ingenerating trust scores for blockchain addresses of the blockchainledger. In some embodiments, determining the consensus trust scoreincludes excluding, from the determining of the consensus trust score,an outlier trust score that is statistically inconsistent with othertrust scores included in the determining of the consensus trust score.In some embodiments, determining the consensus trust score includesdetermining that an average variance of the trust scores included in thedetermining of the consensus trust score is within a threshold variance.

In embodiments, a method includes receiving, by a device, informationthat associates a blockchain address with fraudulent activity, whereinthe blockchain address is associated with a blockchain ledger;processing, by the device, the information with a dual purposeartificial neural network, wherein the dual purpose artificial neuralnetwork is configured to generate trust scores based on information thatassociates blockchain addresses with fraudulent activity; receiving, bythe device, an output of the dual purpose artificial neural network,wherein the output includes a first trust score for the blockchainaddress; receiving, by the device and from at least two other devices,at least two additional trust scores for the blockchain address;determining, by the device, a consensus trust score based on the firsttrust score and the at least two additional trust scores; andgenerating, by the device, a blockchain entry on the blockchain ledger,a blockchain entry that associates the consensus trust score with theblockchain address. In some embodiments, the device and the at least twoother devices are members of a trust network of devices that determineconsensus trust scores for blockchain addresses of the blockchainledger. In some embodiments, the device is configured to generate theblockchain entry on the blockchain ledger in response to a request onthe blockchain ledger for a trust evaluation of the blockchain address.In some embodiments, the device is configured to generate the blockchainentry on the blockchain ledger in response to an activity of theblockchain address on the blockchain ledger. In some embodiments, thedevice is configured to generate the blockchain entry on the blockchainledger in response to a transaction on the blockchain ledger, whereinthe transaction is associated with the blockchain address. In someembodiments, the device is further configured to monitor the blockchainledger to detect transaction with the blockchain address. In someembodiments, the blockchain entry includes a blockchain report thatprovides a basis for the consensus trust rating. Some embodimentsfurther include generating, on the blockchain ledger, a fraud entryassociated with the blockchain address, wherein the fraud entry is basedon a change of a consensus trust rating associated with the blockchainaddress. In some embodiments, the consensus trust score is based on afirst reputation score associated with the device and additionalreputation scores associated with each of the at least two otherdevices. In some embodiments, determining the consensus trust scoreincludes weighting the trust score generated by and/or received from arespective device, wherein the weighting is based on the reputationscore associated with the respective device. In some embodiments, thereputation score associated with a respective device is based on anamount of work performed by the respective device in generating trustscores for blockchain addresses of the blockchain ledger. In someembodiments, determining the consensus trust score includes excluding,from the determining of the consensus trust score, an outlier trustscore that is statistically inconsistent with other trust scoresincluded in the determining of the consensus trust score. In someembodiments, determining the consensus trust score includes determiningthat an average variance of the trust scores included in the determiningof the consensus trust score is within a threshold variance. Someembodiments further include updating, by the device, a data set that isassociated with a training of the dual purpose artificial neuralnetwork; and retraining, by the device, the dual purpose artificialneural network based on the updated data set. In some embodiments,updating the data set includes adding, to the data set, one or more datasamples based on a subsequent activity associated with the blockchainaddress.

In embodiments, a method includes receiving, by a device, informationthat associates a blockchain address with fraudulent activity, whereinthe blockchain address is associated with a blockchain ledger;generating, by the device, a first trust score for the blockchainaddress, wherein the local node trust score is based on the information;receiving, by the device and from at least two other devices, at leasttwo additional trust scores for the blockchain address; determining, bythe device, a consensus trust score based on the first trust score andthe at least two additional trust scores; and processing, by the device,one or more transactions associated with the blockchain address by arobotic process automation module, wherein the robotic processautomation module is configured to engage in transactions withrespective blockchain addresses associated with the blockchain ledgerbased on respective consensus trust scores associated with therespective blockchain addresses. In some embodiments, the device and theat least two other devices are members of a trust network of devicesthat determine consensus trust scores for blockchain addresses of theblockchain ledger. In some embodiments, the device is further configuredto monitor the blockchain ledger to detect transaction with theblockchain address. In some embodiments, the blockchain entry includes ablockchain report that provides a basis for the consensus trust rating.Some embodiments further include generating, on the blockchain ledger, afraud entry associated with the blockchain address, wherein the fraudentry is based on a change of a consensus trust rating associated withthe blockchain address. In some embodiments, the consensus trust scoreis based on a first reputation score associated with the device andadditional reputation scores associated with each of the at least twoother devices. In some embodiments, determining the consensus trustscore includes weighting the trust score generated by and/or receivedfrom a respective device, wherein the weighting is based on thereputation score associated with the respective device. In someembodiments, the reputation score associated with a respective device isbased on an amount of work performed by the respective device ingenerating trust scores for blockchain addresses of the blockchainledger. In some embodiments, determining the consensus trust scoreincludes excluding, from the determining of the consensus trust score,an outlier trust score that is statistically inconsistent with othertrust scores included in the determining of the consensus trust score.In some embodiments, determining the consensus trust score includesdetermining that an average variance of the trust scores included in thedetermining of the consensus trust score is within a threshold variance.In some embodiments, the robotic process automation module is furtherconfigured to determine whether or not to engage in transactions withrespective blockchain addresses, and the determining is based on therespective consensus trust scores associated with the respectiveblockchain addresses. In some embodiments, the robotic processautomation module is configured to engage in transactions withrespective blockchain addresses associated with the blockchain ledgerbased on a training of the robotic process automation module, and thetraining is based on a training data set including data samples thatcorrespond to actions taken by one or more users while engaging intransactions with blockchain addresses associated with the blockchainledger. In some embodiments, the robotic process automation module isconfigured to determine whether or not to engage in transactions withrespective blockchain addresses associated with the blockchain ledgerbased on a training of the robotic process automation module, and thetraining is based on a training data set including data samples thatcorrespond to actions taken by one or more users while determiningwhether or not to engage in transactions with blockchain addressesassociated with the blockchain ledger.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 is a schematic diagram of components of a platform for enablingintelligent transactions in accordance with embodiments of the presentdisclosure.

FIGS. 2A and 2B are schematic diagrams of additional components of aplatform for enabling intelligent transactions in accordance withembodiments of the present disclosure.

FIG. 3 is a schematic diagram of additional components of a platform forenabling intelligent transactions in accordance with embodiments of thepresent disclosure.

FIG. 4 to FIG. 31 are schematic diagrams of embodiments of neural netsystems that may connect to, be integrated in, and be accessible by theplatform for enabling intelligent transactions including ones involvingexpert systems, self-organization, machine learning, artificialintelligence and including neural net systems trained for patternrecognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes in accordance with embodiments of the present disclosure.

FIG. 32 is a schematic diagram of components of an environment includingan intelligent energy and compute facility, a host intelligent energyand compute facility resource management platform, a set of datasources, a set of expert systems, interfaces to a set of marketplatforms and external resources, and a set of user or client systemsand devices in accordance with embodiments of the present disclosure.

FIG. 33 depicts components and interactions of a transactional,financial and marketplace enablement system.

FIG. 34 depicts components and interactions of a set of data handlinglayers of a transactional, financial and marketplace enablement system.

FIG. 35 depicts adaptive intelligence and robotic process automationcapabilities of a transactional, financial and marketplace enablementsystem.

FIG. 36 depicts opportunity mining capabilities of a transactional,financial and marketplace enablement system.

FIG. 37 depicts adaptive edge computation management and edgeintelligence capabilities of a transactional, financial and marketplaceenablement system.

FIG. 38 depicts protocol adaptation and adaptive data storagecapabilities of a transactional, financial and marketplace enablementsystem.

FIG. 39 depicts robotic operational analytic capabilities of atransactional, financial and marketplace enablement system.

FIG. 40 depicts a blockchain and smart contract platform for a forwardmarket for access rights to events.

FIG. 41 depicts an algorithm and a dashboard of a blockchain and smartcontract platform for a forward market for access rights to events.

FIG. 42 depicts a blockchain and smart contract platform for forwardmarket demand aggregation.

FIG. 43 depicts an algorithm and a dashboard of a blockchain and smartcontract platform for forward market demand aggregation.

FIG. 44 depicts a blockchain and smart contract platform forcrowdsourcing for innovation.

FIG. 45 depicts an algorithm and a dashboard of a blockchain and smartcontract platform for crowdsourcing for innovation.

FIG. 46 depicts a blockchain and smart contract platform forcrowdsourcing for evidence.

FIG. 47 depicts an algorithm and a dashboard of a blockchain and smartcontract platform for crowdsourcing for evidence.

FIG. 48 depicts components and interactions of an embodiment of alending platform having a set of data-integrated microservices includingdata collection and monitoring services for handling lending entitiesand transactions.

FIG. 49 depicts components and interactions of an embodiment of alending platform in which a set of lending solutions are supported by adata-integrated set of data collection and monitoring services, adaptiveintelligent systems, and data storage systems.

FIG. 50 depicts components and interactions of an embodiment of alending platform having a set of data integrated blockchain services,smart contract services, social network analytic services, crowdsourcingservices and Internet of Things data collection and monitoring servicesfor collecting, monitoring, and processing information about entitiesinvolved in or related to a lending transaction.

FIG. 51 depicts components and interactions of a lending platform havingan Internet of Things and sensor platform for monitoring at least one ofa set of assets, a set of collateral, and a guarantee for a loan, abond, or a debt transaction.

FIG. 52 depicts components and interactions of a lending platform havinga crowdsourcing system for collecting information related to entitiesinvolved in a lending transaction.

FIG. 53 depicts an embodiment of a crowdsourcing workflow enabled by alending platform.

FIG. 54 depicts components and interactions of an embodiment of alending platform having a smart contract system that automaticallyadjusts an interest rate for a loan based on information collected viaat least one of an Internet of Things system, a crowdsourcing system, aset of social network analytic services and a set of data collection andmonitoring services.

FIG. 55 depicts components and interactions of an embodiment of alending platform having a smart contract that automatically restructuresdebt based on a monitored condition.

FIG. 56 depicts components and interactions of a lending platform havinga set of data collection and monitoring systems for validating thereliability of a guarantee for a loan, including an Internet of Thingssystem and a social network analytics system.

FIG. 57 depicts components and interactions of a lending platform havinga robotic process automation system for negotiation of a set of termsand conditions for a loan.

FIG. 58 depicts components and interactions of a lending platform havinga robotic process automation system for loan collection.

FIG. 59 depicts components and interactions of a lending platform havinga robotic process automation system for consolidating a set of loans.

FIG. 60 depicts components and interactions of a lending platform havinga robotic process automation system for managing a factoring loan.

FIG. 61 depicts components and interactions of a lending platform havinga robotic process automation system for brokering a mortgage loan.

FIG. 62 depicts components and interactions of a lending platform havinga crowdsourcing and automated classification system for validatingcondition of an issuer for a bond, a social network monitoring systemwith artificial intelligence for classifying a condition about a bond,and an Internet of Things data collection and monitoring system withartificial intelligence for classifying a condition about a bond.

FIG. 63 depicts components and interactions of a lending platform havinga system that manages the terms and conditions of a loan based on aparameter monitored by the IoT, by a parameter determined by a socialnetwork analytic system, or a parameter determined by a crowdsourcingsystem.

FIG. 64 depicts components and interactions of a lending platform havingan automated blockchain custody service for managing a set of custodialassets.

FIG. 65 depicts components and interactions of a lending platform havingan underwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.

FIG. 66 depicts components and interactions of a lending platform havinga loan marketing system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services and smart contract services formarketing a loan to a set of prospective parties.

FIG. 67 depicts components and interactions of a lending platform havinga rating system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities.

FIG. 68 depicts components and interactions of a lending platform havinga regulatory and/or compliance system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for automatically facilitating compliance with atleast one of a law, a regulation and a policy that applies to a lendingtransaction.

FIG. 69 , depicts a system for automated loan management.

FIG. 70 depicts a system with a blockchain service circuit.

FIG. 71 depicts a method for handling a loan.

FIG. 72 depicts a system for adaptive intelligence and robotic processautomation capabilities of a transactional, financial and marketplaceenablement.

FIG. 73 depicts a method for automated smart contract creation andcollateral assignment.

FIG. 74 depicts a system for handling a loan.

FIG. 75 depicts a method for handling a loan.

FIG. 76 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 77 depicts a method for loan creation and management.

FIG. 78 depicts a system for adaptive intelligence and robotic processautomation capabilities of a transactional, financial and marketplaceenablement.

FIG. 79 depicts a method for robotic process automation oftransactional, financial and marketplace activities.

FIG. 80 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 81 depicts a method for automated transactional, financial andmarketplace activities.

FIG. 82 depicts a system for adaptive intelligence and robotic process.

FIG. 83 depicts a method for performing loan related actions.

FIG. 84 depicts a system for adaptive intelligence and robotic process.

FIG. 85 depicts a method for performing loan related actions.

FIG. 86 depicts a system for adaptive intelligence and robotic process.

FIG. 87 depicts a method for performing loan related actions.

FIG. 88 depicts a smart contract system for managing collateral for aloan.

FIG. 89 depicts a smart contract method for managing collateral for aloan.

FIG. 90 depicts a system for validating conditions of collateral or aguarantor for a loan.

FIG. 91 depicts a crowdsourcing method for validating conditions ofcollateral or a guarantor for a loan.

FIG. 92 depicts a smart contract system for modifying a loan.

FIG. 93 depicts a smart contract method for modifying a loan.

FIG. 94 depicts a smart contract system for modifying a loan.

FIG. 95 depicts a smart contract method for modifying a loan.

FIG. 96 depicts a smart contract system for modifying a loan.

FIG. 97 depicts a smart contract method for modifying a loan.

FIG. 98 depicts a monitoring system for validating conditions of aguarantee for a loan.

FIG. 99 depicts a monitoring method for validating conditions of aguarantee for a loan.

FIG. 100 depicts a robotic process automation system for negotiating aloan.

FIG. 101 depicts a robotic process automation method for negotiating aloan.

FIG. 102 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 103 depicts a loan collection method.

FIG. 104 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 105 depicts a loan refinancing method.

FIG. 106 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 107 depicts a for loan consolidation method.

FIG. 108 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 109 depicts a loan factoring method.

FIG. 110 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 111 depicts a mortgage brokering method.

FIG. 112 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 113 depicts a method for debt management.

FIG. 114 depicts a system for adaptive intelligence and robotic processautomation.

FIG. 115 depicts a method for bond management.

FIG. 116 depicts a system for monitoring a condition of an issuer for abond.

FIG. 117 depicts a method for monitoring a condition of an issuer for abond

FIG. 118 depicts a system for monitoring a condition of an issuer for abond.

FIG. 119 depicts a method for monitoring a condition of an issuer for abond.

FIG. 120 depicts a system for automatic subsidized loan management.

FIG. 121 depicts a method for automatically modifying subsidized loanterms and conditions.

FIG. 122 depicts a system to automatically modify terms and conditionsof a loan.

FIG. 123 depicts a method for collecting social network informationabout an entity involved in a subsidized loan transaction.

FIG. 124 depicts a system for automating handling of a subsidized loanusing crowdsourcing.

FIG. 125 depicts a method for automating handling of a subsidized loan.

FIG. 126 depicts a system for asset access control.

FIG. 127 depicts a method for asset access control.

FIG. 128 depicts a system automated handling of loan foreclosure.

FIG. 129 depicts a method for facilitating foreclosure on collateral.

FIG. 130 depicts an example energy and computing resource platform.

FIG. 131 depicts an example facility data record.

FIG. 132 depicts an example schema of a person data record.

FIG. 133 depicts a cognitive processing system.

FIG. 134 depicts a process for a lead generation system to generate alead list.

FIG. 135 depicts a process for a lead generation system to determinefacility outputs for identified leads.

FIG. 136 depicts a process to generate and output personalized content.

FIG. 137 depicts a schematic illustrating an example of a portion of aninformation technology system for transaction artificial intelligenceleveraging digital twins according to some embodiments of the presentdisclosure.

FIG. 138 depicts a schematic illustrating a compliance system thatfacilitates the licensing of personality rights according to someembodiments of the present disclosure.

FIG. 139 depicts a schematic illustrating an example set of componentsof a compliance system according to some embodiments of the presentdisclosure.

FIG. 140 depicts a set of operations of a method for vetting a potentiallicensee for purposes of licensing personality rights of a licensoraccording to some embodiments of the present disclosure.

FIG. 141 depicts a set of operations of a method for facilitating thelicensing of personality rights of a licensor by a licensee according tosome embodiments of the present disclosure.

FIG. 142 depicts a set of operations of a method for detecting potentialcircumvention of rules or regulations by a licensor and/or licenseeaccording to some embodiments of the present disclosure.

FIG. 143 depicts a method for selecting an AI solution.

FIG. 144 depicts a method for selecting an AI solution.

FIG. 145 depicts an example of an assembled AI solution.

FIG. 146 depicts a method for selecting an AI solution.

FIG. 147 depicts a method for selecting an AI solution.

FIG. 148 depicts an AI solution selection and configuration system.

FIG. 149 depicts an AI solution selection and configuration system.

FIG. 150 depicts an AI solution selection and configuration system.

FIG. 151 depicts a component configuration circuit.

FIG. 152 depicts an AI solution selection and configuration system.

FIG. 153 depicts a system for selecting and configuring an artificialintelligence model.

FIG. 154 depicts a method of selecting and configuring an artificialintelligence model.

FIG. 155 is a schematic illustrating examples of architecture of adigital twin system according to embodiments of the present disclosure.

FIG. 156 is a schematic illustrating exemplary components of a digitaltwin management system according to embodiments of the presentdisclosure.

FIG. 157 is a schematic illustrating examples of a digital twin I/Osystem that interfaces with an environment, the digital twin system,and/or components thereof to provide bi-directional transfer of databetween coupled components according to embodiments of the presentdisclosure.

FIG. 158 is a schematic illustrating an example set of identified statesrelated to industrial environments that the digital twin system mayidentify and/or store for access by intelligent systems (e.g., acognitive intelligence system) or users of the digital twin systemaccording to embodiments of the present disclosure.

FIG. 159 is a schematic illustrating example embodiments of methods forupdating a set of properties of a digital twin of the present disclosureon behalf of a client application and/or one or more embedded digitaltwins.

FIG. 160 illustrates example embodiments of a display interface of thepresent disclosure that renders a digital twin of a dryer centrifugewith information relating to the dryer centrifuge.

FIG. 161 is a schematic illustrating an example embodiment of a methodfor updating a set of vibration fault level states of machine componentssuch as bearings in the digital twin of an industrial machine, on behalfof a client application.

FIG. 162 is a schematic illustrating an example embodiment of a methodfor updating a set of vibration severity unit values of machinecomponents such as bearings in the digital twin of a machine on behalfof a client application.

FIG. 163 is a schematic illustrating an example embodiment of a methodfor updating a set of probability of failure values in the digital twinsof machine components on behalf of a client application.

FIG. 164 is a schematic illustrating an example embodiment of a methodfor updating a set of probability of downtime values of machines in thedigital twin of a manufacturing facility on behalf of a clientapplication.

FIG. 165 is a schematic illustrating an example embodiment of a methodfor updating a set of probability of shutdown values of manufacturingfacilities in the digital twin of an enterprise on behalf of a clientapplication.

FIG. 166 is a schematic illustrating an example embodiment of a methodfor updating a set of cost of downtime values of machines in the digitaltwin of a manufacturing facility.

FIG. 167 is a schematic illustrating an example embodiment of a methodfor updating one or more manufacturing KPI values in a digital twin of amanufacturing facility, on behalf of a client application.

FIG. 168 is a schematic diagram of components of a knowledgedistribution system and a communication network for facilitatingmanagement of digital knowledge in accordance with embodiments of thepresent disclosure.

FIG. 169 is a schematic diagram of a ledger network of the knowledgedistribution system in accordance with embodiments of the presentdisclosure.

FIG. 170 is a schematic diagram of the knowledge distribution system ofFIG. 168 including details of a smart contract and a smart contractsystem of the knowledge distribution system in accordance withembodiments of the present disclosure.

FIG. 171 is a schematic diagram of a plurality of datastores of theknowledge distribution system in accordance with embodiments of thepresent disclosure.

FIG. 172 illustrates a method of deploying a knowledge token and relatedsmart contract via the knowledge distribution system in accordance withembodiments of the present disclosure.

FIG. 173 illustrates a method of performing high level process flow of asmart contract that distributes digital knowledge via the knowledgedistribution system in accordance with embodiments of the presentdisclosure.

FIG. 174 is a schematic diagram of another embodiment of components ofthe knowledge distribution system and a communication network forfacilitating management of digital knowledge in accordance withembodiments of the present disclosure.

FIG. 175 depicts a knowledge distribution system for controlling rightsrelated to digital knowledge.

FIG. 176 depicts a computer-implemented method for controlling rightsrelated to digital knowledge.

FIG. 177 depicts a computer-implemented method for controlling rightsrelated to digital knowledge.

FIG. 178 depicts a knowledge distribution system for controlling rightsrelated to digital knowledge.

FIG. 179 depicts possible components of a 3D printer instruction set.

FIG. 180 depicts possible content of tokenized digital knowledge.

FIG. 181 depicts possible smart contract actions.

FIG. 182 depicts possible conditions relating to triggering events.

FIG. 183 depicts possible control and access rights.

FIG. 184 depicts possible triggering events.

FIG. 185 depicts a computer-implemented method for controlling rightsrelated to digital knowledge.

FIG. 186 depicts a computer-implemented method for controlling rightsrelated to digital knowledge.

FIG. 187 depicts possible crowdsourced information.

FIG. 188 depicts possible contents of a distributed ledger.

FIG. 189 depicts possible parameters.

FIG. 190 depicts an embodiment of a knowledge distribution system forcontrolling rights related to digital knowledge.

FIGS. 191-196 depict embodiments of operations for controlling rightsrelated to digital knowledge.

FIG. 197 is a diagrammatic view illustrating an example implementationof the knowledge distribution system including a trust network foridentifying the likelihood of fraudulent transactions using a consensustrust score and preventing such fraudulent transactions according tosome embodiments of the present disclosure.

FIG. 198 illustrates an example method that describes operation of anexample trust network illustrated in FIG. 197 according to someembodiments of the present disclosure.

FIG. 199 is a diagrammatic view illustrating a transaction beingprocessed by the ledger network including a plurality of node computingdevices according to some embodiments of the present disclosure.

FIG. 200 is a diagrammatic view illustrating an example implementationof the knowledge distribution system including a digital marketplaceconfigured to provide an environment allowing knowledge providers andknowledge recipients to engage in commerce relating to the transfer ofdigital knowledge according to some embodiments of the presentdisclosure.

FIG. 201 is a diagrammatic view illustrating an example user interfaceof a digital marketplace configured to enable transactions and commercebetween various users of the knowledge distribution system according tosome embodiments of the present disclosure.

FIG. 202 is a schematic view of an exemplary embodiment of the marketorchestration system according to some embodiments of the presentdisclosure.

FIG. 203 is a schematic view of an exemplary embodiment of the marketorchestration system including a marketplace configuration system forconfiguring and launching a marketplace.

FIG. 204 is a schematic illustrating an example embodiment of a methodof configuring and launching a marketplace according to some embodimentsof the present disclosure.

FIG. 205 is a schematic view of an exemplary embodiment of the marketorchestration system including a robotic process automation systemconfigured to automate internal marketplace workflows based on roboticprocess automation.

FIG. 206 is a schematic view of an exemplary embodiment of the marketorchestration system including an edge device configured to perform edgecomputation and intelligence.

FIG. 207 is a schematic view of an exemplary embodiment of the marketorchestration system including a digital twin system configured tointegrate a set of adaptive edge computing systems with a marketorchestration digital twin.

FIG. 208 is a schematic view of a digital twin system according to someembodiments.

Gaming Engine and Smart Contract Platform FIGS.

FIG. 209A, FIG. 209B, and FIG. 209C are a block diagrams depictingsystems of a gaming engine smart contract executing platform in anexemplary deployment environment.

FIG. 210 is a block diagram depicting a gaming engine system of a gamingengine smart contract executing platform in an exemplary deploymentenvironment.

FIG. 211 is a block diagram depicting an intelligence layer of a gamingengine smart contract executing platform in an exemplary deploymentenvironment.

FIG. 212 is a block diagram depicting a cloud-based deployment of thegaming engine smart contract platform of FIGS. 209A-C.

FIG. 213 is a block diagram depicting an exemplary embodiment of agaming engine system of the gaming engine smart contract platform.

FIG. 214 is a flowchart depicting an exemplary execution flow of thegaming engine smart contract platform.

FIG. 215 is a flowchart depicting another exemplary execution flow ofthe gaming engine smart contract platform.

FIG. 216A and FIG. 216B are flowcharts depicting yet another exemplaryexecution flow of the gaming engine smart contract platform.

FIG. 217 is a block diagram depicting an exemplary embodiment of anintelligence layer of the gaming engine smart contract platform.

FIG. 218 is a block diagram depicting an exemplary embodiment of adistributed ledger system of the gaming engine smart contract platform.

FIG. 219 is a block diagram depicting an exemplary embodiment of adistributed ledger network of the gaming engine smart contract platform.

FIG. 220 is a block diagram depicting another exemplary embodiment of adistributed ledger network of the gaming engine smart contract platform.

FIG. 221 is a flowchart depicting an exemplary method of executing asmart contract via the gaming engine smart contract platform.

Additive Manufacturing FIGS.

FIG. 222 is a diagrammatic view illustrating an example environment ofan autonomous additive manufacturing platform according to someembodiments of the present disclosure.

FIG. 223 is a schematic illustrating an example implementation of anautonomous additive manufacturing platform for automating and optimizingthe digital production workflow for metal additive manufacturingaccording to some embodiments of the present disclosure.

FIG. 224 is a flow diagram illustrating the optimization of differentparameters of an additive manufacture process according to someembodiments of the present disclosure.

FIG. 225A is a schematic illustrating an example artificial neuralnetwork used to provide real-time, adaptive control of an additivemanufacturing process according to some embodiments of the presentdisclosure.

FIG. 225B is a diagrammatic view illustrating an example implementationof a data processing system using a convolutional neural network (CNN)to provide automatic classification and clustering of parts and defectsin an additive manufacturing process according to some embodiments ofthe present disclosure.

FIG. 226 is a schematic view illustrating a system for learning on datafrom an autonomous additive manufacturing platform to train anartificial learning system to use digital twins for classification,predictions and decision making according to some embodiments of thepresent disclosure.

FIG. 227A, FIG. 227B, and FIG. 227C are schematics illustrating anexample implementation of an autonomous additive manufacturing platformincluding various components along with other entities of a distributedmanufacturing network according to some embodiments of the presentdisclosure.

FIG. 228 is a schematic illustrating an example implementation of anautonomous additive manufacturing platform for automating and managingmanufacturing functions and sub-processes including process and materialselection, hybrid part workflows, feedstock formulation, part designoptimization, risk prediction and management, marketing and customerservice according to some embodiments of the present disclosure.

FIG. 229 is a diagrammatic view of a distributed manufacturing networkenabled by an autonomous additive manufacturing platform and built on adistributed ledger system according to some embodiments of the presentdisclosure.

FIG. 230 is a schematic illustrating an example implementation of adistributed manufacturing network where the digital thread data istokenized and stored in a distributed ledger so as to ensuretraceability of parts printed at one or more manufacturing nodes in thedistributed manufacturing network according to some embodiments of thepresent disclosure.

Enterprise Access Layer FIGS.

FIG. 231 is a schematic view of an example of an enterprise ecosystemhaving an enterprise access layer.

FIG. 232 is a schematic view of another example of an enterpriseecosystem having an enterprise access layer.

FIG. 233 is a schematic view of examples as to how the enterprise accesslayer of FIG. 232 may be integrated with portions of an enterpriseecosystem.

FIG. 234 is a schematic view of an example market orchestration systemthat includes an enterprise access layer.

Intelligence Services System FIGS.

FIG. 235 is a schematic view of an example of an intelligence servicessystem according to some embodiments.

FIG. 236 is a schematic view of an example of a neural network accordingto some embodiments.

FIG. 237 is a schematic view of an example of a convolutional neuralnetwork according to some embodiments.

FIG. 238 is a schematic view of an example of a neural network accordingto some embodiments.

FIG. 239 is a diagram of an approach based on reinforcement learningaccording to some embodiments.

Market Orchestration Architecture FIGS.

FIG. 240 depicts a block diagram of a market orchestration architecturethat integrates cross market exchange methods and systems describedherein.

FIG. 241 depicts an example of normalizing item values within a set ofitems for exchange-specific currencies.

FIG. 242 depicts an example of normalizing item values across sets ofitems for exchange-specific currencies.

FIG. 243 depicts an example of normalizing a value of an item across aplurality of exchange-specific currencies.

FIG. 244 depicts an example of item value translation among exchanges.

FIG. 245 depicts an example of conditional item value translation amongexchanges.

FIG. 246 depicts an example of item-representative token generation foruse in a target exchange based on characteristics of the item from asource exchange.

FIG. 247 depicts an example of the item-representative token generationof FIG. 246 through application of item characteristics harvestingalgorithms.

FIG. 248 depicts an example of the item-representative token generationof FIG. 246 through processing of smart contracts associated with theitem in a source exchange.

FIG. 249 depicts an example of generating a rights token for an itembased on at least one of a smart contract and terms and conditions forthe item.

FIG. 250 depicts an example of generating a rights token for an itembased on at least one of a smart contract and terms and conditions forthe item for a range of exchange governing rules.

FIG. 251 depicts an example of generating a rights token for an itembased on at least one of a smart contract and terms and conditions forthe item and further based on conformance of detected rights withexchange governing rules.

FIG. 252 depicts an example of generating an adaptable rights token foran item based on at least one of a smart contract and terms andconditions for the item and target exchange adaptation rules.

FIG. 253 depicts an example of automatically cascading actions acrossexchanges in which workflows are automated through robotic processautomation.

FIG. 254 depicts an example of automatically cascading workflowinitiation actions across exchanges in which the workflows are automatedthrough robotic process automation.

FIG. 255 depicts an example of automatically cascading actions ofworkflows across exchanges in which the workflows are automated throughrobotic process automation.

FIG. 256 depicts an example of applying robotic process automation togenerate a cross-exchange smart contract from discrete exchange-specificsmart contracts.

FIG. 257 depicts an example of a self-adapting asset data deliverynetwork infrastructure pipeline that includes one or more of thenormalization, value translation, item tokenization, or rightstokenization methods or systems described herein.

Intelligent Data Layer FIGS.

FIG. 258 depicts a block diagram of exemplary features, capabilities,and interfaces of an intelligent data layer platform.

FIG. 259 depicts a block diagram of an exemplary intelligent data layerarchitecture.

FIG. 260 depicts a block diagram of an independently operatedintelligent data layer for producing data for a plurality of dataconsumers.

FIG. 261 depicts a block diagram of an intelligent data layer platformdeployment for data-strategic approach of an enterprise.

FIG. 262 depicts a block diagram of a remote intelligent data layer withactively deployed elements for dynamic on-demand IDL operation.

FIG. 263 depicts a diagram of mapping parameters of a data producer(e.g., source) with a data consumer.

FIG. 264 depicts a block diagram of an enterprise deployment ofintelligent data layers.

FIG. 265 depicts a block diagram of a network constructed of intelligentdata layers.

FIG. 266 depicts a block diagram of an exemplary cloud-based deploymentfor an intelligent data layer architecture.

FIG. 267 depicts a block diagram of a multi-use (configurable)intelligent data layer architecture to produce different layer contentand intelligence for different purposes/uses/consumers.

FIG. 268 depicts a block diagram of a marketplace/transactionenvironment deployment of intelligent data layers.

FIG. 269 depicts a block diagram of use of intelligent data layers forsource discovery.

Data and Networking Pipeline for Market Orchestration FIGS.

FIGS. 270-287 illustrate various features associated with data networkand infrastructure pipelines.

Cross-Market Transaction Engine FIGS.

FIG. 288 illustrates an exemplary environment of a cross-markettransaction engine according to some embodiments of the presentdisclosure.

FIG. 289 illustrates another exemplary environment of a cross-markettransaction engine according to some embodiments of the presentdisclosure.

Marketplace Prediction System FIG.

FIG. 290 is a diagrammatic view that illustrates embodiments of themarket prediction system platform in accordance with the presentdisclosure.

Quantum FIGS.

FIG. 291 is a schematic view of an exemplary embodiment of the quantumcomputing service according to some embodiments of the presentdisclosure.

FIG. 292 illustrates quantum computing service request handlingaccording to some embodiments of the present disclosure.

Trust Network FIGS.

FIGS. 293-297 illustrate an example trust network in communication withcryptocurrency transactor computing devices, intermediate transactionsystems, and automated transaction systems.

FIG. 298 is a method that describes operation of an example trustnetwork.

FIG. 299 is a functional block diagram of an example node thatcalculates local trust scores and consensus trust scores.

FIG. 300 is a functional block diagram of an example node thatcalculates consensus trust scores.

FIG. 301 is a flow diagram that illustrates an example method forcalculating a consensus trust score.

FIG. 302 is a functional block diagram of an example node thatcalculates reputation values.

FIG. 303 is a functional block diagram of an example node thatimplements a token economy for a trust network.

FIG. 304 illustrates an example method that describes operation of areward protocol.

FIGS. 305-306 illustrate graphical user interfaces (GUIs) for requestingand reviewing trust reports.

FIG. 307 is a functional block diagram of a trust network being used ina payment insurance implementation.

FIG. 308 illustrates an example relationship of staked token andconsensus trust score cost.

FIG. 309 illustrates example services associated with different levelsof nodes.

FIG. 310 illustrates an example relationship between the number ofnodes, the number of cliques, the address overlap, and the probabilitythat a node will get a single address in their control.

FIG. 311 illustrates sample token staking amounts and number of nodes.

FIG. 312 is a functional block diagram of an example trust scoredetermination module and local trust data store.

FIG. 313 is a method that describes operation of an example trust scoredetermination module.

FIG. 314 is a functional block diagram of a data acquisition andprocessing module.

FIG. 315 is a functional block diagram of a blockchain data acquisitionand processing module.

FIGS. 316-317 illustrate generation and processing of a blockchain graphdata structure.

FIG. 318 is a functional block diagram of a scoring feature generationmodule and a scoring model generation module.

FIG. 319 is a functional block diagram that illustrates operation of ascore generation module.

FIG. 320 illustrates an environment that includes a cryptocurrencyblockchain network that executes smart contracts.

FIG. 321 illustrates a method that describes operation of theenvironment of FIG. 320 .

FIG. 322 is a functional block diagram that illustrates interactionsbetween a sender user device, an intermediate transaction system, ablockchain network, and a trust network/system.

FIGS. 323-324 illustrate an example trust system and an example trustnode that can determine trust scores for blockchain addresses.

FIGS. 325-326 illustrate an example sender interface on a user device.

FIG. 327 illustrates an example method describing operation of anintermediate transaction system.

FIG. 328 illustrates an example method describing operation of a trustnetwork/system.

Dual Process Artificial Neural Network Figures

FIG. 329 is a diagrammatic view of a dual process artificial neuralnetwork system in accordance with some embodiments.

FIG. 330 is a diagrammatic view that illustrates embodiments of thebiology-based system in accordance with the present disclosure.

FIG. 331 is a diagrammatic view thalamus service in accordance with thepresent disclosure.

DETAILED DESCRIPTION

The term services/microservices (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a service/microservice includesany system (or platform) configured to functionally perform theoperations of the service, where the system may be data-integrated,including data collection circuits, blockchain circuits, artificialintelligence circuits, and/or smart contract circuits for handlinglending entities and transactions. Services/microservices may facilitatedata handling and may include facilities for data extraction,transformation and loading; data cleansing and deduplication facilities;data normalization facilities; data synchronization facilities; datasecurity facilities; computational facilities (e.g., for performingpre-defined calculation operations on data streams and providing anoutput stream); compression and de-compression facilities; analyticfacilities (such as providing automated production of datavisualizations), data processing facilities, and/or data storagefacilities (including storage retention, formatting, compression,migration, etc.), and others.

Services/microservices may include controllers, processors, networkinfrastructure, input/output devices, servers, client devices (e.g.,laptops, desktops, terminals, mobile devices, and/or dedicated devices),sensors (e.g., IoT sensors associated with one or more entities,equipment, and/or collateral), actuators (e.g., automated locks,notification devices, lights, camera controls, etc.), virtualizedversions of any one or more of the foregoing (e.g., outsourced computingresources such as a cloud storage, computing operations; virtualsensors; subscribed data to be gathered such as stock or commodityprices, recordal logs, etc.), and/or include components configured ascomputer readable instructions that, when performed by a processor,cause the processor to perform one or more functions of the service,etc. Services may be distributed across a number of devices, and/orfunctions of a service may be performed by one or more devicescooperating to perform the given function of the service.

Services/microservices may include application programming interfacesthat facilitate connection among the components of the system performingthe service (e.g., microservices) and between the system to entities(e.g., programs, websites, user devices, etc.) that are external to thesystem. Without limitation to any other aspect of the presentdisclosure, example microservices that may be present in certainembodiments include (a) a multi-modal set of data collection circuitsthat collect information about and monitor entities related to a lendingtransaction; (b) blockchain circuits for maintaining a secure historicalledger of events related to a loan, the blockchain circuits havingaccess control features that govern access by a set of parties involvedin a loan; (c) a set of application programming interfaces, dataintegration services, data processing workflows and user interfaces forhandling loan-related events and loan-related activities; and (d) smartcontract circuits for specifying terms and conditions of smart contractsthat govern at least one of loan terms and conditions, loan-relatedevents, and loan-related activities. Any of the services/microservicesmay be controlled by or have control over a controller. Certain systemsmay not be considered to be a service/microservice. For example, a pointof sale device that simply charges a set cost for a good or service maynot be a service. In another example, a service that tracks the cost ofa good or service and triggers notifications when the value changes maynot be a valuation service itself, but may rely on valuation services,and/or may form a portion of a valuation service in certain embodiments.It can be seen that a given circuit, controller, or device may be aservice or a part of a service in certain embodiments, such as when thefunctions or capabilities of the circuit, controller, or device areconfigured to support a service or microservice as described herein, butmay not be a service or part of a service for other embodiments (e.g.,where the functions or capabilities of the circuit, controller, ordevice are not relevant to a service or microservice as describedherein). In another example, a mobile device being operated by a usermay form a portion of a service as described herein at a first point intime (e.g., when the user accesses a feature of the service through anapplication or other communication from the mobile device, and/or when amonitoring function is being performed via the mobile device), but maynot form a portion of the service at a second point in time (e.g., aftera transaction is completed, after the user un-installs an application,and/or when a monitoring function is stopped and/or passed to anotherdevice). Accordingly, the benefits of the present disclosure may beapplied in a wide variety of processes or systems, and any suchprocesses or systems may be considered a service (or a part of aservice) herein.

One of skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, how to combine processes and systemsfrom the present disclosure to construct, provide performancecharacteristics (e.g., bandwidth, computing power, time response, etc.),and/or provide operational capabilities (e.g., time between checks,up-time requirements including longitudinal (e.g., continuous operatingtime) and/or sequential (e.g., time-of-day, calendar time, etc.),resolution and/or accuracy of sensing, data determinations (e.g.,accuracy, timing, amount of data), and/or actuator confirmationcapability) of components of the service that are sufficient to providea given embodiment of a service, platform, and/or microservice asdescribed herein. Certain considerations for the person of skill in theart, in determining the configuration of components, circuits,controllers, and/or devices to implement a service, platform, and/ormicroservice (“service” in the listing following) as described hereininclude, without limitation: the balance of capital costs versusoperating costs in implementing and operating the service; theavailability, speed, and/or bandwidth of network services available forsystem components, service users, and/or other entities that interactwith the service; the response time of considerations for the service(e.g., how quickly decisions within the service must be implemented tosupport the commercial function of the service, the operating time forvarious artificial intelligence or other high computation operations)and/or the capital or operating cost to support a given response time;the location of interacting components of the service, and the effectsof such locations on operations of the service (e.g., data storagelocations and relevant regulatory schemes, network communicationlimitations and/or costs, power costs as a function of the location,support availability for time zones relevant to the service, etc.); theavailability of certain sensor types, the related support for thosesensors, and the availability of sufficient substitutes (e.g., a cameramay require supportive lighting, and/or high network bandwidth or localstorage) for the sensing purpose; an aspect of the underlying value ofan aspect of the service (e.g., a principal amount of a loan, a value ofcollateral, a volatility of the collateral value, a net worth orrelative net worth of a lender, guarantor, and/or borrower, etc.)including the time sensitivity of the underlying value (e.g., if itchanges quickly or slowly relative to the operations of the service orthe term of the loan); a trust indicator between parties of atransaction (e.g., history of performance between the parties, a creditrating, social rating, or other external indicator, conformance ofactivity related to the transaction to an industry standard or othernormalized transaction type, etc.); and/or the availability of costrecovery options (e.g., subscriptions, fees, payment for services, etc.)for given configurations and/or capabilities of the service, platform,and/or microservice. Without limitation to any other aspect of thepresent disclosure, certain operations performed by services hereininclude: performing real-time alterations to a loan based on trackeddata; utilizing data to execute a collateral-backed smart contract;re-evaluating debt transactions in response to a tracked condition ordata, and the like. While specific examples of services/microservicesand considerations are described herein for purposes of illustration,any system benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein, are specifically contemplated within thescope of the present disclosure.

Without limitation, services include a financial service (e.g., a loantransaction service), a data collection service (e.g., a data collectionservice for collecting and monitoring data), a blockchain service (e.g.,a blockchain service to maintain secure data), data integration services(e.g., a data integration service to aggregate data), smart contractservices (e.g., a smart contract service to determine aspects of smartcontracts), software services (e.g., a software service to extract datarelated to the entities from publicly available information sites),crowdsourcing services (e.g., a crowdsourcing service to solicit andreport information), Internet of Things services (e.g., an Internet ofThings service to monitor an environment), publishing services (e.g., apublishing services to publish data), microservices (e.g., having a setof application programming interfaces that facilitate connection amongthe microservices), valuation services (e.g., that use a valuation modelto set a value for collateral based on information), artificialintelligence services, market value data collection services (e.g., thatmonitor and report on marketplace information), clustering services(e.g., for grouping the collateral items based on similarity ofattributes), social networking services (e.g., that enablesconfiguration with respect to parameters of a social network), assetidentification services (e.g., for identifying a set of assets for whicha financial institution is responsible for taking custody), identitymanagement services (e.g., by which a financial institution verifiesidentities and credentials), and the like, and/or similar functionalterminology. Example services to perform one or more functions hereininclude computing devices; servers; networked devices; user interfaces;inter-device interfaces such as communication protocols, sharedinformation and/or information storage, and/or application programminginterfaces (APIs); sensors (e.g., IoT sensors operationally coupled tomonitored components, equipment, locations, or the like); distributedledgers; circuits; and/or computer readable code configured to cause aprocessor to execute one or more functions of the service. One or moreaspects or components of services herein may be distributed across anumber of devices, and/or may consolidated, in whole or part, on a givendevice. In embodiments, aspects or components of services herein may beimplemented at least in part through circuits, such as, in non-limitingexamples, a data collection service implemented at least in part as adata collection circuit structured to collect and monitor data, ablockchain service implemented at least in part as a blockchain circuitstructured to maintain secure data, data integration servicesimplemented at least in part as a data integration circuit structured toaggregate data, smart contract services implemented at least in part asa smart contract circuit structured to determine aspects of smartcontracts, software services implemented at least in part as a softwareservice circuit structured to extract data related to the entities frompublicly available information sites, crowdsourcing services implementedat least in part as a crowdsourcing circuit structured to solicit andreport information, Internet of Things services implemented at least inpart as an Internet of Things circuit structured to monitor anenvironment, publishing services implemented at least in part as apublishing services circuit structured to publish data, microserviceservice implemented at least in part as a microservice circuitstructured to interconnect a plurality of service circuits, valuationservice implemented at least in part as valuation services circuitstructured to access a valuation model to set a value for collateralbased on data, artificial intelligence service implemented at least inpart as an artificial intelligence services circuit, market value datacollection service implemented at least in part as market value datacollection service circuit structured to monitor and report onmarketplace information, clustering service implemented at least in partas a clustering services circuit structured to group collateral itemsbased on similarity of attributes, a social networking serviceimplemented at least in part as a social networking analytic servicescircuit structured to configure parameters with respect to a socialnetwork, asset identification services implemented at least in part asan asset identification service circuit for identifying a set of assetsfor which a financial institution is responsible for taking custody,identity management services implemented at least in part as an identitymanagement service circuit enabling a financial institution to verifyidentities and credentials, and the like. Accordingly, the benefits ofthe present disclosure may be applied in a wide variety of systems, andany such systems may be considered with respect to items and servicesherein, while in certain embodiments a given system may not beconsidered with respect to items and services herein. One of skill inthe art, having the benefit of the disclosure herein and knowledge abouta contemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.Among the considerations that one of skill in the art may contemplate todetermine a configuration for a particular service include: thedistribution and access devices available to one or more parties to aparticular transaction; jurisdictional limitations on the storage, type,and communication of certain types of information; requirements ordesired aspects of security and verification of informationcommunication for the service; the response time of informationgathering, inter-party communications, and determinations to be made byalgorithms, machine learning components, and/or artificial intelligencecomponents of the service; cost considerations of the service, includingcapital expenses and operating costs, as well as which party or entitywill bear the costs and availability to recover costs such as throughsubscriptions, service fees, or the like; the amount of information tobe stored and/or communicated to support the service; and/or theprocessing or computing power to be utilized to support the service.

The terms items and services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, items and service include anyitems and service, including, without limitation, items and servicesused as a reward, used as collateral, become the subject of anegotiation, and the like, such as, without limitation, an applicationfor a warranty or guarantee with respect to an item that is the subjectof a loan, collateral for a loan, or the like, such as a product, aservice, an offering, a solution, a physical product, software, a levelof service, quality of service, a financial instrument, a debt, an itemof collateral, performance of a service, or other items. Withoutlimitation to any other aspect or description of the present disclosure,items and service include any items and service, including, withoutlimitation, items and services as applied to physical items (e.g., avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty), a financial item (e.g., a commodity, a security, a currency,a token of value, a ticket, a cryptocurrency), a consumable item (e.g.,an edible item, a beverage), a highly valued item (e.g., a preciousmetal, an item of jewelry, a gemstone), an intellectual item (e.g., anitem of intellectual property, an intellectual property right, acontractual right), and the like. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered with respect to items and servicesherein, while in certain embodiments a given system may not beconsidered with respect to items and services herein. One of skill inthe art, having the benefit of the disclosure herein and knowledge abouta contemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.

The terms agent, automated agent, and similar terms as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an agent or automated agent mayprocess events relevant to at least one of the value, the condition, andthe ownership of items of collateral or assets. The agent or automatedagent may also undertake an action related to a loan, debt transaction,bond transaction, subsidized loan, or the like to which the collateralor asset is subject, such as in response to the processed events. Theagent or automated agent may interact with a marketplace for purposes ofcollecting data, testing spot market transactions, executingtransactions, and the like, where dynamic system behavior involvescomplex interactions that a user may desire to understand, predict,control, and/or optimize. Certain systems may not be considered an agentor an automated agent. For example, if events are merely collected butnot processed, the system may not be an agent or automated agent. Insome embodiments, if a loan-related action is undertaken not in responseto a processed event, it may not have been undertaken by an agent orautomated agent. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure include and/or benefit from agents or automatedagent. Certain considerations for the person of skill in the art, orembodiments of the present disclosure with respect to an agent orautomated agent include, without limitation: rules that determine whenthere is a change in a value, condition or ownership of an asset orcollateral, and/or rules to determine if a change warrants a furtheraction on a loan or other transaction, and other considerations. Whilespecific examples of market values and marketplace information aredescribed herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein are specifically contemplated within the scope of thepresent disclosure.

The term marketplace information, market value and similar terms asutilized herein should be understood broadly. Without limitation to anyother aspect or description of the present disclosure, marketplaceinformation and market value describe a status or value of an asset,collateral, food, or service at a defined point or period in time.Market value may refer to the expected value placed on an item in amarketplace or auction setting, or pricing or financial data for itemsthat are similar to the item, asset, or collateral in at least onepublic marketplace. For a company, market value may be the number of itsoutstanding shares multiplied by the current share price. Valuationservices may include market value data collection services that monitorand report on marketplace information relevant to the value (e.g.,market value) of collateral, the issuer, a set of bonds, and a set ofassets. a set of subsidized loans, a party, and the like. Market valuesmay be dynamic in nature because they depend on an assortment offactors, from physical operating conditions to economic climate to thedynamics of demand and supply. Market value may be affected by, andmarketplace information may include, proximity to other assets,inventory or supply of assets, demand for assets, origin of items,history of items, underlying current value of item components, abankruptcy condition of an entity, a foreclosure status of an entity, acontractual default status of an entity, a regulatory violation statusof an entity, a criminal status of an entity, an export controls statusof an entity, an embargo status of an entity, a tariff status of anentity, a tax status of an entity, a credit report of an entity, acredit rating of an entity, a web site rating of an entity, a set ofcustomer reviews for a product of an entity, a social network rating ofan entity, a set of credentials of an entity, a set of referrals of anentity, a set of testimonials for an entity, a set of behavior of anentity, a location of an entity, and a geolocation of an entity. Incertain embodiments, a market value may include information such as avolatility of a value, a sensitivity of a value (e.g., relative to otherparameters having an uncertainty associated therewith), and/or aspecific value of the valuated object to a particular party (e.g., anobject may have more value as possessed by a first party than aspossessed by a second party).

Certain information may not be marketplace information or a marketvalue. For example, where variables related to a value are notmarket-derived, they may be a value-in-use or an investment value. Incertain embodiments, an investment value may be considered a marketvalue (e.g., when the valuating party intends to utilize the asset as aninvestment if acquired), and not a market value in other embodiments(e.g., when the valuating party intends to immediately liquidate theinvestment if acquired). One of skill in the art, having the benefit ofthe disclosure herein and knowledge about a contemplated systemordinarily available to that person, can readily determine which aspectsof the present disclosure will benefit from marketplace information or amarket value. Certain considerations for the person of skill in the art,in determining whether the term market value is referring to an asset,item, collateral, good, or service include: the presence of othersimilar assets in a marketplace, the change in value depending onlocation, an opening bid of an item exceeding a list price, and otherconsiderations. While specific examples of market values and marketplaceinformation are described herein for purposes of illustration, anyembodiment benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein are specifically contemplated within the scopeof the present disclosure.

The term apportion value or apportioned value and similar terms asutilized herein should be understood broadly. Without limitation to anyother aspect or description of the present disclosure, apportion valuedescribes a proportional distribution or allocation of valueproportionally, or a process to divide and assign value according to arule of proportional distribution. Apportionment of the value may be toseveral parties (e.g., each of the several parties is a beneficiary of aportion of the value), to several transactions (e.g., each of thetransactions utilizes a portion of the value), and/or in a many-to-manyrelationship (e.g., a group of objects has an aggregate value that isapportioned between a number of parties and/or transactions). In someembodiments, the value may be a net loss and the apportioned value isthe allocation of a liability to each entity. In other embodiments,apportioned value may refer to the distribution or allocation of aneconomic benefit, real estate, collateral, or the like. In certainembodiments, apportionment may include a consideration of the valuerelative to the parties, for example, a $10 million asset apportioned50/50 between two parties, where the parties have distinct valueconsiderations for the asset, may result in one party crediting theapportionment differing resulting values from the apportionment. Incertain embodiments, apportionment may include a consideration of thevalue relative to given transactions, for example, a first type oftransaction (e.g., a long-term loan) may have a different valuation of agiven asset than a second type of transaction (e.g., a short-term lineof credit).

Certain conditions or processes may not relate to apportioned value. Forexample, the total value of an item may provide its inherent worth, butnot how much of the value is held by each identified entity. One ofskill in the art, having the benefit of the disclosure herein andknowledge about apportioned value, can readily determine which aspectsof the present disclosure will benefit a particular application forapportioned value. Certain considerations for the person of skill in theart, or embodiments of the present disclosure with respect to anapportioned value include, without limitation: the currency of theprincipal sum, the anticipated transaction type (loan, bond or debt),the specific type of collateral, the ratio of the loan to value, theratio of the collateral to the loan, the gross transaction/loan amount,the amount of the principal sum, the number of entities owed, the valueof the collateral, and the like. While specific examples of apportionedvalues are described herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein are specifically contemplated within the scope of thepresent disclosure.

The term financial condition and similar terms as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, financial condition describes acurrent status of an entity's assets, liabilities, and equity positionsat a defined point or period in time. The financial condition may bememorialized in financial statement. The financial condition may furtherinclude an assessment of the ability of the entity to survive futurerisk scenarios or meet future or maturing obligations. Financialcondition may be based on a set of attributes of the entity selectedfrom among a publicly stated valuation of the entity, a set of propertyowned by the entity as indicated by public records, a valuation of a setof property owned by the entity, a bankruptcy condition of an entity, aforeclosure status of an entity, a contractual default status of anentity, a regulatory violation status of an entity, a criminal status ofan entity, an export controls status of an entity, an embargo status ofan entity, a tariff status of an entity, a tax status of an entity, acredit report of an entity, a credit rating of an entity, a websiterating of an entity, a set of customer reviews for a product of anentity, a social network rating of an entity, a set of credentials of anentity, a set of referrals of an entity, a set of testimonials for anentity, a set of behavior of an entity, a location of an entity, and ageolocation of an entity. A financial condition may also describe arequirement or threshold for an agreement or loan. For example,conditions for allowing a developer to proceed may be variouscertifications and their agreement to a financial payout. That is, thedeveloper's ability to proceed is conditioned upon a financial element,among others. Certain conditions may not be a financial condition. Forexample, a credit card balance alone may be a clue as to the financialcondition, but may not be the financial condition on its own. In anotherexample, a payment schedule may determine how long a debt may be on anentity's balance sheet, but in a silo may not accurately provide afinancial condition. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure include and/or will benefit from a financialcondition. Certain considerations for the person of skill in the art, indetermining whether the term financial condition is referring to acurrent status of an entity's assets, liabilities, and equity positionsat a defined point or period in time and/or for a given purpose include:the reporting of more than one financial data point, the ratio of a loanto value of collateral, the ratio of the collateral to the loan, thegross transaction/loan amount, the credit scores of the borrower and thelender, and other considerations. While specific examples of financialconditions are described herein for purposes of illustration, anyembodiment benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein are specifically contemplated within the scopeof the present disclosure.

The term interest rate and similar terms, as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, interest rate includes an amountof interest due per period, as a proportion of an amount lent,deposited, or borrowed. The total interest on an amount lent or borrowedmay depend on the principal sum, the interest rate, the compoundingfrequency, and the length of time over which it is lent, deposited, orborrowed. Typically, interest rate is expressed as an annual percentagebut can be defined for any time period. The interest rate relates to theamount a bank or other lender charges to borrow its money, or the rate abank or other entity pays its savers for keeping money in an account.Interest rate may be variable or fixed. For example, an interest ratemay vary in accordance with a government or other stakeholder directive,the currency of the principal sum lent or borrowed, the term to maturityof the investment, the perceived default probability of the borrower,supply and demand in the market, the amount of collateral, the status ofan economy, or special features like call provisions. In certainembodiments, an interest rate may be a relative rate (e.g., relative toa prime rate, an inflation index, etc.). In certain embodiments, aninterest rate may further consider costs or fees applied (e.g.,“points”) to adjust the interest rate. A nominal interest rate may notbe adjusted for inflation while a real interest rate takes inflationinto account. Certain examples may not be an interest rate for purposesof particular embodiments. For example, a bank account growing by afixed dollar amount each year, and/or a fixed fee amount, may not be anexample of an interest rate for certain embodiments. One of skill in theart, having the benefit of the disclosure herein and knowledge aboutinterest rates, can readily determine the characteristics of an interestrate for a particular embodiment. Certain considerations for the personof skill in the art, or embodiments of the present disclosure withrespect to an interest rate include, without limitation: the currency ofthe principal sum, variables for setting an interest rate, criteria formodifying an interest rate, the anticipated transaction type (loan, bondor debt), the specific type of collateral, the ratio of the loan tovalue, the ratio of the collateral to the loan, the grosstransaction/loan amount, the amount of the principal sum, theappropriate lifespans of transactions and/or collateral for a particularindustry, the likelihood that a lender will sell and/or consolidate aloan before the term, and the like. While specific examples of interestrates are described herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein are specifically contemplated within the scope of thepresent disclosure.

The term valuation services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a valuation service includes anyservice that sets a value for a good or service. Valuation services mayuse a valuation model to set a value for collateral based on informationfrom data collection and monitoring services. Smart contract servicesmay process output from the set of valuation services and assign itemsof collateral sufficient to provide security for a loan and/or apportionvalue for an item of collateral among a set of lenders and/ortransactions. Valuation services may include artificial intelligenceservices that may iteratively improve the valuation model based onoutcome data relating to transactions in collateral. Valuation servicesmay include market value data collection services that may monitor andreport on marketplace information relevant to the value of collateral.Certain processes may not be considered to be a valuation service. Forexample, a point of sale device that simply charges a set cost for agood or service may not be a valuation service. In another example, aservice that tracks the cost of a good or service and triggersnotifications when the value changes may not be a valuation serviceitself, but may rely on valuation services and/or form a part of avaluation service. Accordingly, the benefits of the present disclosuremay be applied in a wide variety of processes systems, and any suchprocesses or systems may be considered a valuation service herein, whilein certain embodiments a given service may not be considered a valuationservice herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system and how to combineprocesses and systems from the present disclosure to enhance operationsof the contemplated system and/or to provide a valuation service.Certain considerations for the person of skill in the art, indetermining whether a contemplated system is a valuation service and/orwhether aspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation: perform real-timealterations to a loan based on a value of a collateral; utilizemarketplace data to execute a collateral-backed smart contract;re-evaluate collateral based on a storage condition or geolocation; thetendency of the collateral to have a volatile value, be utilized, and/orbe moved; and the like. While specific examples of valuation servicesand considerations are described herein for purposes of illustration,any system benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein, are specifically contemplated within thescope of the present disclosure.

The term collateral attributes (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, collateral attributes include anyidentification of the durability (ability of the collateral to withstandwear or the useful life of the collateral), value, identification (doesthe collateral have definite characteristics that make it easy toidentify or market), stability of value (does the collateral maintainvalue over time), standardization, grade, quality, marketability,liquidity, transferability, desirability, trackability, deliverability(ability of the collateral be delivered or transfer without adeterioration in value), market transparency (is the collateral valueeasily verifiable or widely agreed upon), physical or virtual.Collateral attributes may be measured in absolute or relative terms,and/or may include qualitative (e.g., categorical descriptions) orquantitative descriptions. Collateral attributes may be different fordifferent industries, products, elements, uses, and the like. Collateralattributes may be assigned quantitative or qualitative values. Valuesassociated with collateral attributes may be based on a scale (such as1-10) or a relative designation (high, low, better, etc.). Collateralmay include various components; each component may have collateralattributes. Collateral may, therefore, have multiple values for the samecollateral attribute. In some embodiments, multiple values of collateralattributes may be combined to generate one value for each attribute.Some collateral attributes may apply only to specific portions ofcollateral. Some collateral attributes, even for a given component ofthe collateral, may have distinct values depending upon the party ofinterest (e.g., a party that values an aspect of the collateral morehighly than another party) and/or depending upon the type of transaction(e.g., the collateral may be more valuable or appropriate for a firsttype of loan than for a second type of loan). Certain attributesassociated with collateral may not be collateral attributes as describedherein depending upon the purpose of the collateral attributes herein.For example, a product may be rated as durable relative to similarproducts; however, if the life of the product is much lower than theterm of a particular loan in consideration, the durability of theproduct may be rated differently (e.g., not durable) or irrelevant(e.g., where the current inventory of the product is attached as thecollateral, and is expected to change out during the term of the loan).Accordingly, the benefits of the present disclosure may be applied to avariety of attributes, and any such attributes may be consideredcollateral attributes herein, while in certain embodiments a givenattribute may not be considered a collateral attribute herein. One ofskill in the art, having the benefit of the disclosure herein andknowledge about contemplated collateral attributes ordinarily availableto that person, can readily determine which aspects of the presentdisclosure will benefit a particular collateral attribute. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated attribute is a collateral attribute and/orwhether aspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation: the source of theattribute and the source of the value of the attribute (e.g. does theattribute and attribute value comes from a reputable source), thevolatility of the attribute (e.g. does the attribute values for thecollateral fluctuate, is the attribute a new attribute for thecollateral), relative differences in attribute values for similarcollateral, exceptional values for attributes (e.g., some attributevalues may be high, such as, in the 98th percentile or very low, such asin the 2nd percentile, compared to similar class of collateral), thefungibility of the collateral, the type of transaction related to thecollateral, and/or the purpose of the utilization of collateral for aparticular party or transaction. While specific examples of collateralattributes and considerations are described herein for purposes ofillustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term blockchain services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, blockchain services include anyservice related to the processing, recordation, and/or updating of ablockchain, and may include services for processing blocks, computinghash values, generating new blocks in a blockchain, appending a block tothe blockchain, creating a fork in the blockchain, merging of forks inthe blockchain, verifying previous computations, updating a sharedledger, updating a distributed ledger, generating cryptographic keys,verifying transactions, maintaining a blockchain, updating a blockchain,verifying a blockchain, generating random numbers. The services may beperformed by execution of computer readable instructions on localcomputers and/or by remote servers and computers. Certain services maynot be considered blockchain services individually but may be consideredblockchain services based on the final use of the service and/or in aparticular embodiment, for example, a computing a hash value may beperformed in a context outside of a blockchain such in the context ofsecure communication. Some initial services may be invoked without firstbeing applied to blockchains, but further actions or services inconjunction with the initial services may associate the initial servicewith aspects of blockchains. For example, a random number may beperiodically generated and stored in memory; the random numbers mayinitially not be generated for blockchain purposes but may be utilizedfor blockchains. Accordingly, the benefits of the present disclosure maybe applied in a wide variety of services, and any such services may beconsidered blockchain services herein, while in certain embodiments agiven service may not be considered a blockchain service herein. One ofskill in the art, having the benefit of the disclosure herein andknowledge about a contemplated blockchain service ordinarily availableto that person, can readily determine which aspects of the presentdisclosure can be configured to implement, and/or will benefit, aparticular blockchain service. Certain considerations for the person ofskill in the art, in determining whether a contemplated service is ablockchain service and/or whether aspects of the present disclosure canbenefit or enhance the contemplated system include, without limitation:the application of the service, the source of the service (e.g., if theservice is associated with a known or verifiable blockchain serviceprovider), responsiveness of the service (e.g., some blockchain servicesmay have an expected completion time, and/or may be determined throughutilization), cost of the service, the amount of data requested for theservice, and/or the amount of data generated by the service (blocks ofblockchain or keys associated with blockchains may be a specific size ora specific range of sizes). While specific examples of blockchainservices and considerations are described herein for purposes ofillustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term blockchain (and variations such as cryptocurrency ledger, andthe like) as utilized herein may be understood broadly to describe acryptocurrency ledger that records, administrates, or otherwiseprocesses online transactions. A blockchain may be public, private, or acombination thereof, without limitation. A blockchain may also be usedto represent a set of digital transactions, agreement, terms, or otherdigital value. Without limitation to any other aspect or description ofthe present disclosure, in the former case, a blockchain may also beused in conjunction with investment applications, token-tradingapplications, and/or digital/cryptocurrency based marketplaces. Ablockchain can also be associated with rendering consideration, such asproviding goods, services, items, fees, access to a restricted area orevent, data, or other valuable benefit. Blockchains in various forms maybe included where discussing a unit of consideration, collateral,currency, cryptocurrency, or any other form of value. One of skill inthe art, having the benefit of the disclosure herein and knowledgeordinarily available about a contemplated system, can readily determinethe value symbolized or represented by a blockchain. While specificexamples of blockchains are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms ledger and distributed ledger (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, a ledger may be adocument, file, computer file, database, book, and the like whichmaintains a record of transactions. Ledgers may be physical or digital.Ledgers may include records related to sales, accounts, purchases,transactions, assets, liabilities, incomes, expenses, capital, and thelike. Ledgers may provide a history of transactions that may beassociated with time. Ledgers may be centralized ordecentralized/distributed. A centralized ledger may be a document thatis controlled, updated, or viewable by one or more selected entities ora clearinghouse and wherein changes or updates to the ledger aregoverned or controlled by the entity or clearinghouse. A distributedledger may be a ledger that is distributed across a plurality ofentities, participants or regions which may independently, concurrently,or consensually, update, or modify their copies of the ledger. Ledgersand distributed ledgers may include security measures and cryptographicfunctions for signing, concealing, or verifying content. In the case ofdistributed ledgers, blockchain technology may be used. In the case ofdistributed ledgers implemented using blockchain, the ledger may beMerkle trees comprising a linked list of nodes in which each nodecontains hashed or encrypted transactional data of the previous nodes.Certain records of transactions may not be considered ledgers. A file,computer file, database, or book may or may not be a ledger depending onwhat data it stores, how the data is organized, maintained, or secured.For example, a list of transactions may not be considered a ledger if itcannot be trusted or verified, and/or if it is based on inconsistent,fraudulent, or incomplete data. Data in ledgers may be organized in anyformat such as tables, lists, binary streams of data, or the like whichmay depend on convenience, source of data, type of data, environment,applications, and the like. A ledger that is shared among variousentities may not be a distributed ledger, but the distinction ofdistributed may be based on which entities are authorized to makechanges to the ledger and/or how the changes are shared and processedamong the different entities. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of data, and any such datamay be considered ledgers herein, while in certain embodiments a givendata may not be considered a ledger herein. One of skill in the art,having the benefit of the disclosure herein and knowledge aboutcontemplated ledgers and distributed ledger ordinarily available to thatperson, can readily determine which aspects of the present disclosurecan be utilized to implement, and/or will benefit a particular ledger.Certain considerations for the person of skill in the art, indetermining whether a contemplated data is a ledger and/or whetheraspects of the present disclosure can benefit or enhance thecontemplated ledger include, without limitation: the security of thedata in the ledger (can the data be tampered or modified), the timeassociated with making changes to the data in the ledger, cost of makingchanges (computationally and monetarily), detail of data, organizationof data (does the data need to be processed for use in an application),who controls the ledger (can the party be trusted or relied to managethe ledger), confidentiality of the data (who can see or track the datain the ledger), size of the infrastructure, communication requirements(distributed ledgers may require a communication interface or specificinfrastructure), resiliency. While specific examples of blockchainservices and considerations are described herein for purposes ofillustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term loan (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a loan may be an agreementrelated to an asset that is borrowed, and that is expected to bereturned in kind (e.g., money borrowed, and money returned) or as anagreed transaction (e.g., a first good or service is borrowed, andmoney, a second good or service, or a combination, is returned). Assetsmay be money, property, time, physical objects, virtual objects,services, a right (e.g., a ticket, a license, or other rights), adepreciation amount, a credit (e.g., a tax credit, an emissions credit,etc.), an agreed assumption of a risk or liability, and/or anycombination thereof. A loan may be based on a formal or informalagreement between a borrower and a lender wherein a lender may providean asset to the borrower for a predefined amount of time, a variableperiod of time, or indefinitely. Lenders and borrowers may beindividuals, entities, corporations, governments, groups of people,organizations, and the like. Loan types may include mortgage loans,personal loans, secured loans, unsecured loans, concessional loans,commercial loans, microloans, and the like. The agreement between theborrower and the lender may specify terms of the loan. The borrower maybe required to return an asset or repay with a different asset than wasborrowed. In some cases, a loan may require interest to be repaid on theborrowed asset. Borrowers and lenders may be intermediaries betweenother entities and may never possess or use the asset. In someembodiments, a loan may not be associated with direct transfer of goodsbut may be associated with usage rights or shared usage rights. Incertain embodiments, the agreement between the borrower and the lendermay be executed between the borrower and the lender, and/or executedbetween an intermediary (e.g., a beneficiary of a loan right such asthrough a sale of the loan). In certain embodiment, the agreementbetween the borrower and the lender may be executed through servicesherein, such as through a smart contract service that determines atleast a portion of the terms and conditions of the loans, and in certainembodiments may commit the borrower and/or the lender to the terms ofthe agreement, which may be a smart contract. In certain embodiments,the smart contract service may populate the terms of the agreement, andpresent them to the borrower and/or lender for execution. In certainembodiments, the smart contract service may automatically commit one ofthe borrower or the lender to the terms (at least as an offer) and maypresent the offer to the other one of the borrower or the lender forexecution. In certain embodiments, a loan agreement may include multipleborrowers and/or multiple lenders, for example where a set of loansincludes a number of beneficiaries of payment on the set of loans,and/or a number of borrowers on the set of loans. In certainembodiments, the risks and/or obligations of the set of loans may beindividualized (e.g., each borrower and/or lender is related to specificloans of the set of loans), apportioned (e.g., a default on a particularloan has an associated loss apportioned between the lenders), and/orcombinations of these (e.g., one or more subsets of the set of loans istreated individually and/or apportioned).

Certain agreements may not be considered a loan. An agreement totransfer or borrow assets may not be a loan depending on what assets aretransferred, how the assets were transferred, or the parties involved.For example, in some cases, the transfer of assets may be for anindefinite time and may be considered a sale of the asset or a permanenttransfer. Likewise, if an asset is borrowed or transferred without clearor definite terms or lack of consensus between the lender and theborrower it may, in some cases, not be considered a loan. An agreementmay be considered a loan even if a formal agreement is not directlycodified in a written agreement as long as the parties willingly andknowingly agreed to the arrangement, and/or ordinary practices (e.g., ina particular industry) may treat the transaction as a loan. Accordingly,the benefits of the present disclosure may be applied in a wide varietyof agreements, and any such agreement may be considered a loan herein,while in certain embodiments a given agreement may not be considered aloan herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about contemplated loans ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure implement a loan, utilize a loan, or benefit aparticular loan transaction. Certain considerations for the person ofskill in the art, in determining whether a contemplated data is a loanand/or whether aspects of the present disclosure can benefit or enhancethe contemplated loan include, without limitation: the value of theassets involved, the ability of the borrower to return or repay theloan, the types of assets involved (e.g., whether the asset is consumedthrough utilization), the repayment time frame associated with the loan,the interest on the loan, how the agreement of the loan was arranged,formality of the agreement, detail of the agreement, the detail of theagreements of the loan, the collateral attributes associated with theloan, and/or the ordinary business expectations of any of the foregoingin a particular context. While specific examples of loans andconsiderations are described herein for purposes of illustration, anysystem benefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term loan related event(s) (and similar terms, includingloan-related events) as utilized herein should be understood broadly.Without limitation to any other aspect or description of the presentdisclosure, a loan related events may include any event related to termsof the loan or events triggered by the agreement associated with theloan. Loan-related events may include default on loan, breach ofcontract, fulfillment, repayment, payment, change in interest, late feeassessment, refund assessment, distribution, and the like. Loan-relatedevents may be triggered by explicit agreement terms; for example, anagreement may specify a rise in interest rate after a time period haselapsed from the beginning of the loan; the rise in interest ratetriggered by the agreement may be a loan related event. Loan-relatedevents may be triggered implicitly by related loan agreement terms. Incertain embodiments, any occurrence that may be considered relevant toassumptions of the loan agreement, and/or expectations of the parties tothe loan agreement, may be considered an occurrence of an event. Forexample, if collateral for a loan is expected to be replaceable (e.g.,an inventory as collateral), then a change in inventory levels may beconsidered an occurrence of a loan related event. In another example, ifreview and/or confirmation of the collateral is expected, then a lack ofaccess to the collateral, the disablement or failure of a monitoringsensor, etc. may be considered an occurrence of a loan related event. Incertain embodiments, circuits, controllers, or other devices describedherein may automatically trigger the determination of a loan-relatedevents. In some embodiments, loan-related events may be triggered byentities that manage loans or loan-related contracts. Loan-relatedevents may be conditionally triggered based on one or more conditions inthe loan agreement. Loan related events may be related to tasks orrequirements that need to be completed by the lender, borrower, or athird party. Certain events may be considered loan-related events incertain embodiments and/or in certain contexts, but may not beconsidered a loan-related event in another embodiment or context. Manyevents may be associated with loans but may be caused by externaltriggers not associated with a loan. However, in certain embodiments, anexternally triggered event (e.g., a commodity price change related to acollateral item) may be loan-related events. For example, renegotiationof loan terms initiated by a lender may not be considered a loan relatedevent if the terms and/or performance of the existing loan agreement didnot trigger the renegotiation. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of events, and any suchevent may be considered a loan related event herein, while in certainembodiments given events may not be considered a loan related eventherein. One of skill in the art, having the benefit of the disclosureherein and knowledge about a contemplated system ordinarily available tothat person, can readily determine which aspects of the presentdisclosure may be considered a loan-related event for the contemplatedsystem and/or for particular transactions supported by the system.Certain considerations for the person of skill in the art, indetermining whether a contemplated data is a loan related event and/orwhether aspects of the present disclosure can benefit or enhance thecontemplated transaction system include, without limitation: the impactof the related event on the loan (events that cause default ortermination of the loan may have higher impact), the cost (capitaland/or operating) associated with the event, the cost (capital and/oroperating) associated with monitoring for an occurrence of the event,the entities responsible for responding to the event, a time periodand/or response time associated with the event (e.g., time required tocomplete the event and time that is allotted from the time the event istriggered to when processing or detection of the event is desired tooccur), the entity responsible for the event, the data required forprocessing the event (e.g., confidential information may have differentsafeguards or restrictions), the availability of mitigating actions ifan undetected event occurs, and/or the remedies available to an at-riskparty if the event occurs without detection. While specific examples ofloan-related events and considerations are described herein for purposesof illustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term loan-related activities (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a loan related activity mayinclude activities related to the generation, maintenance, termination,collection, enforcement, servicing, billing, marketing, ability toperform, or negotiation of a loan. Loan-related activity may includeactivities related to the signing of a loan agreement or a promissorynote, review of loan documents, processing of payments, evaluation ofcollateral, evaluation of compliance of the borrower or lender to theloan terms, renegotiation of terms, perfection of security or collateralfor the loan, and/or a negation of terms. Loan-related activities mayrelate to events associated with a loan before formal agreement on theterms, such as activities associated with initial negotiations.Loan-related activities may relate to events during the life of the loanand after the termination of a loan. Loan-related activities may beperformed by a lender, borrower, or a third party. Certain activitiesmay not be considered loan related activities services individually butmay be considered loan related activities based on the specificity ofthe activity to the loan lifecycle—for example, billing or invoicingrelated to outstanding loans may be considered a loan related activity,however when the invoicing or billing of loans is combined with billingor invoicing for non loan-related elements the invoicing may not beconsidered a loan related activity. Some activities may be performed inrelation to an asset regardless of whether a loan is associated with theasset; in these cases, the activity may not be considered a loan relatedactivity. For example, regular audits related to an asset may occurregardless of whether the asset is associated with a loan and may not beconsidered a loan related activity. In another example, a regular auditrelated to an asset may be required by a loan agreement and would nottypically occur but for the association with a loan, in this case, theactivity may be considered a loan related activity. In some embodiments,activities may be considered loan-related activities if the activitywould otherwise not occur if the loan is not active or present, but maystill be considered a loan-related activity in some instances (e.g., ifauditing occurs normally, but the lender does not have the ability toenforce or review the audit, then the audit may be considered aloan-related activity even though it already occurs otherwise).Accordingly, the benefits of the present disclosure may be applied in awide variety of events, and any such event may be considered a loanrelated event herein, while in certain embodiments given events may notbe considered a loan related events herein. One of skill in the art,having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine a loan related activity for the purposes of the contemplatedsystem. Certain considerations for the person of skill in the art, indetermining whether a contemplated data is a loan related activityand/or whether aspects of the present disclosure can benefit or enhancethe contemplated loan include, without limitation: the necessity of theactivity for the loan (can the loan agreement or terms be satisfiedwithout the activity), the cost of the activity, the specificity of theactivity to the loan (is the activity similar or identical to otherindustries), time involved in the activity, the impact of the activityon a loan life cycle, entity performing the activity, amount of datarequired for the activity (does the activity require confidentialinformation related to the loan, or personal information related to theentities), and/or the ability of parties to enforce and/or review theactivity. While specific examples of loan-related events andconsiderations are described herein for purposes of illustration, anysystem benefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The terms loan-terms, loan terms, terms for a loan, terms andconditions, and the like as utilized herein should be understood broadly(“loan terms”). Without limitation to any other aspect or description ofthe present disclosure, loan terms may relate to conditions, rules,limitations, contract obligations, and the like related to the timing,repayment, origination, and other enforceable conditions agreed to bythe borrower and the lender of the loan. Loan terms may be specified ina formal contract between a borrower and the lender. Loan terms mayspecify aspects of an interest rate, collateral, foreclose conditions,consequence of debt, payment options, payment schedule, a covenant, andthe like. Loan terms may be negotiable or may change during the life ofa loan. Loan terms may be change or be affected by outside parameterssuch as market prices, bond prices, conditions associated with a lenderor borrower, and the like. Certain aspects of a loan may not beconsidered loan terms. In certain embodiments, aspects of loan that havenot been formally agreed upon between a lender and a borrower, and/orthat are not ordinarily understood in the course of business (and/or theparticular industry) may not be considered loan terms. Certain aspectsof a loan may be preliminary or informal until they have been formallyagreed or confirmed in a contract or a formal agreement. Certain aspectsof a loan may not be considered loan terms individually but may not beconsidered loan terms based on the specificity of the aspect to aspecific loan. Certain aspects of a loan may not be considered loanterms at a particular time during the loan, but may be considered loanterms at another time during the loan (e.g., obligations and/or waiversthat may occur through the performance of the parties, and/or expirationof a loan term). For example, an interest rate may generally not beconsidered a loan term until it is defined in relation of a loan anddefined as to how the interest compounded (annual, monthly), calculated,and the like. An aspect of a loan may not be considered a term if it isindefinite or unenforceable. Some aspects may be manifestations orrelated to terms of a loan but may themselves not be the terms. Forexample, a loan term may be the repayment period of a loan, such as oneyear. The term may not specify how the loan is to be repaid in the year.The loan may be repaid with 12 monthly payments or one annual payment. Amonthly payment plan in this case may not be considered a loan term asit can be just one or many options for repayment not directly specifiedby a loan. Accordingly, the benefits of the present disclosure may beapplied in a wide variety of loan aspects, and any such aspect may beconsidered a loan term herein, while in certain embodiments givenaspects may not be considered loan terms herein. One of skill in theart, having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure are loan terms for thecontemplated system.

Certain considerations for the person of skill in the art, indetermining whether a contemplated data is a loan term and/or whetheraspects of the present disclosure can benefit or enhance thecontemplated loan include, without limitation: the enforceability of theterms (can the conditions be enforced by the lender or the lender or theborrower), the cost of enforcing the terms (amount of time, or effortrequired ensure the conditions are being followed), the complexity ofthe terms (how easily can they be followed or understood by the partiesinvolved, are the terms error prone or easily misunderstood), entitiesresponsible for the terms, fairness of the terms, stability of the terms(how often do they change), observability of the terms (can the terms beverified by a another party), favorability of the terms to one party (dothe terms favor the borrower or the lender), risk associated with theloan (terms may depend on the probability that the loan may not berepaid), characteristics of the borrower or lender (their ability tomeet the terms), and/or ordinary expectations for the loan and/orrelated industry.

While specific examples of loan terms are described herein for purposesof illustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term loan conditions, loan-conditions, conditions for a loan, termsand conditions, and the like as utilized herein should be understoodbroadly (“loan conditions”). Without limitation to any other aspect ordescription of the present disclosure, loan conditions may relate torules, limits, and/or obligations related to a loan. Loan conditions mayrelate to rules or necessary obligations for obtaining a loan, formaintaining a loan, for applying for a loan, for transferring a loan,and the like. Loan conditions may include principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, treatment of collateral, access to collateral, a party, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition,conditions related to other debts of the borrower, and a consequence ofdefault.

Certain aspects of a loan may not be considered loan conditions. Aspectsof loan that have not been formally agreed upon between a lender and aborrower, and/or that are not ordinarily understood in the course ofbusiness (and/or the particular industry), may not be considered loanconditions. Certain aspects of a loan may be preliminary or informaluntil they have been formally agreed or confirmed in a contract or aformal agreement. Certain aspects of a loan may not be considered loanconditions individually but may be considered loan conditions based onthe specificity of the aspect to a specific loan. Certain aspects of aloan may not be considered loan conditions at a particular time duringthe loan, but may be considered loan conditions at another time duringthe loan (e.g., obligations and/or waivers that may occur through theperformance of the parties, and/or expiration of a loan condition).Accordingly, the benefits of the present disclosure may be applied in awide variety of loan aspects, and any such aspect may be considered loanconditions herein, while in certain embodiments given aspects may not beconsidered loan conditions herein. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure are loan conditions for thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated data is a loan conditionand/or whether aspects of the present disclosure can benefit or enhancethe contemplated loan include, without limitation: the enforceability ofthe condition (can the conditions be enforced by the lender or thelender or the borrower), the cost of enforcing the condition (amount oftime, or effort required ensure the conditions are being followed), thecomplexity of the condition (how easily can they be followed orunderstood by the parties involved, are the conditions error prone oreasily misunderstood), entities responsible for the conditions, fairnessof the conditions, observability of the conditions (can the conditionsbe verified by a another party), favorability of the conditions to oneparty (do the conditions favor the borrower or the lender), riskassociated with the loan (conditions may depend on the probability thatthe loan may not be repaid), and/or ordinary expectations for the loanand/or related industry.

While specific examples of loan conditions are described herein forpurposes of illustration, any system benefitting from the disclosuresherein, and any considerations understood to one of skill in the arthaving the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term loan collateral, collateral, item of collateral, collateralitem, and the like as utilized herein should be understood broadly.Without limitation to any other aspect or description of the presentdisclosure, a loan collateral may relate to any asset or property that aborrower promises to a lender as backup in exchange for a loan, and/oras security for the loan. Collateral may be any item of value that isaccepted as an alternate form of repayment in case of default on a loan.Collateral may include any number of physical or virtual items such as avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty. Collateral may include more than one item or types of items.

A collateral item may describe an asset, a property, a value, or otheritem defined as a security for a loan or a transaction. A set ofcollateral items may be defined, and within that set substitution,removal or addition of collateral items may be affected. For example, acollateral item may be, without limitation: a vehicle, a ship, a plane,a building, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, or an item of personal property, or the like. If aset or plurality of collateral items is defined, substitution, removalor addition of collateral items may be affected, such as substituting,removing, or adding a collateral item to or from a set of collateralitems. Without limitation to any other aspect or description of thepresent disclosure, a collateral item or set of collateral items mayalso be used in conjunction with other terms to an agreement or loan,such as a representation, a warranty, an indemnity, a covenant, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a security, a personal guarantee, a lien, a duration, aforeclose condition, a default condition, and a consequence of default.In certain embodiments, a smart contract may calculate whether aborrower has satisfied conditions or covenants and in cases where theborrower has not satisfied such conditions or covenants, may enableautomated action, or trigger another conditions or terms that may affectthe status, ownership, or transfer of a collateral item, or initiate thesubstitution, removal, or addition of collateral items to a set ofcollateral for a loan. One of skill in the art, having the benefit ofthe disclosure herein and knowledge about collateral items, can readilydetermine the purposes and use of collateral items in variousembodiments and contexts disclosed herein, including the substitution,removal, and addition thereof.

While specific examples of loan collateral are described herein forpurposes of illustration, any system benefitting from the disclosuresherein, and any considerations understood to one of skill in the arthaving the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term smart contract services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a smart contract service includesany service or application that manages a smart contract or a smartlending contract. For example, the smart contract service may specifyterms and conditions of a smart contract, such as in a rules database,or process output from a set of valuation services and assign items ofcollateral sufficient to provide security for a loan. Smart contractservices may automatically execute a set of rules or conditions thatembody the smart contract, wherein the execution may be based on or takeadvantage of collected data. Smart contract services may automaticallyinitiate a demand for payment of a loan, automatically initiate aforeclosure process, automatically initiate an action to claimsubstitute or backup collateral or transfer ownership of collateral,automatically initiate an inspection process, automatically change apayment, or interest rate term that is based on the collateral, and mayalso configure smart contracts to automatically undertake a loan-relatedaction. Smart contracts may govern at least one of loan terms andconditions, loan-related events, and loan-related activities. Smartcontracts may be agreements that are encoded as computer protocols andmay facilitate, verify, or enforce the negotiation or performance of asmart contract. Smart contracts may or may not be one or more ofpartially or fully self-executing, or partially or fully self-enforcing.

Certain processes may not be considered to be smart-contract relatedindividually, but may be considered smart-contract related in anaggregated system—for example automatically undertaking a loan-relatedaction may not be smart contract-related in one instance, but in anotherinstance, may be governed by terms of a smart contract. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofprocesses systems, and any such processes or systems may be considered asmart contract or smart contract service herein, while in certainembodiments a given service may not be considered a smart contractservice herein.

One of skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system and how to combine processes andsystems from the present disclosure to implement a smart contractservice and/or enhance operations of the contemplated system. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated system includes a smart contract service or smartcontract and/or whether aspects of the present disclosure can benefit orenhance the contemplated system include, without limitation: ability totransfer ownership of collateral automatically in response to an event;automated actions available upon a finding of covenant compliance (orlack of compliance); the amenity of the collateral to clustering,re-balancing, distribution, addition, substitution, and removal of itemsfrom collateral; the modification parameters of an aspect of a loan inresponse to an event (e.g., timing, complexity, suitability for the loantype, etc.); the complexity of terms and conditions of loans for thesystem, including benefits from rapid determination and/or predictionsof changes to entities (e.g., in the collateral, a financial conditionof a party, offset collateral, and/or in an industry related to a party)related to the loan; the suitability of automated generation of termsand conditions and/or execution of terms and conditions for the types ofloans, parties, and/or industries contemplated for the system; and thelike. While specific examples of smart contract services andconsiderations are described herein for purposes of illustration, anysystem benefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term IoT system (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, an IoT system includes any systemof uniquely identified and interrelated computing devices, mechanicaland digital machines, sensors, and objects that are able to transferdata over a network without intervention. Certain components may not beconsidered an IoT system individually, but may be considered an IoTsystem in an aggregated system, for example, a single networked.

The sensor, smart speaker, and/or medical device may be not an IoTsystem, but may be a part of a larger system and/or be accumulated witha number of other similar components to be considered an IoT systemand/or a part of an IoT system. In certain embodiments, a system may beconsidered an IoT system for some purposes but not for otherpurposes—for example, a smart speaker may be considered part of an IoTsystem for certain operations, such as for providing surround sound, orthe like, but not part of an IoT system for other operations such asdirectly streaming content from a single, locally networked source.Additionally, in certain embodiments, otherwise similar looking systemsmay be differentiated in determining whether such systems are IoTsystems, and/or which type of IoT system. For example, one group ofmedical devices may not, at a given time, be sharing to an aggregatedHER database, while another group of medical devices may be sharing datato an aggregate HER for the purposes of a clinical study, andaccordingly one group of medical devices may be an IoT system, while theother is not. Accordingly, the benefits of the present disclosure may beapplied in a wide variety of systems, and any such systems may beconsidered an IoT system herein, while in certain embodiments a givensystem may not be considered an IoT system herein. One of skill in theart, having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, how to combine processes and systems from the presentdisclosure to enhance operations of the contemplated system, and whichcircuits, controllers, and/or devices include an IoT system for thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is an IoT systemand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation: the transmissionenvironment of the system (e.g., availability of low power, inter-devicenetworking); the shared data storage of a group of devices;establishment of a geofence by a group of devices; service as blockchainnodes; the performance of asset, collateral, or entity monitoring; therelay of data between devices; ability to aggregate data from aplurality of sensors or monitoring devices, and the like. While specificexamples of IoT systems and considerations are described herein forpurposes of illustration, any system benefitting from the disclosuresherein, and any considerations understood to one of skill in the arthaving the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term data collection services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a data collection serviceincludes any service that collects data or information, including anycircuit, controller, device, or application that may store, transmit,transfer, share, process, organize, compare, report on and/or aggregatedata. The data collection service may include data collection devices(e.g., sensors) and/or may be in communication with data collectiondevices. The data collection service may monitor entities, such as toidentify data or information for collection. The data collection servicemay be event-driven, run on a periodic basis, or retrieve data from anapplication at particular points in the application's execution. Certainprocesses may not be considered to be a data collection serviceindividually, but may be considered a data collection service in anaggregated system—for example, a networked storage device may be acomponent of a data collection service in one instance, but in anotherinstance, may have stand-alone functionality. Accordingly, the benefitsof the present disclosure may be applied in a wide variety of processessystems, and any such processes or systems may be considered a datacollection service herein, while in certain embodiments a given servicemay not be considered a data collection service herein. One of skill inthe art, having the benefit of the disclosure herein and knowledge abouta contemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system and how to combine processes and systems from thepresent disclosure implement a data collection service and/or to enhanceoperations of the contemplated system. Certain considerations for theperson of skill in the art, in determining whether a contemplated systemis a data collection service and/or whether aspects of the presentdisclosure can benefit or enhance the contemplated system include,without limitation: ability to modify a business rule on the fly andalter a data collection protocol; perform real-time monitoring ofevents; connection of a device for data collection to a monitoringinfrastructure, execution of computer readable instructions that cause aprocessor to log or track events; use of an automated inspection system;occurrence of sales at a networked point-of-sale; need for data from oneor more distributed sensors or cameras; and the like. While specificexamples of data collection services and considerations are describedherein for purposes of illustration, any system benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term data integration services (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, a data integrationservice includes any service that integrates data or information,including any device or application that may extract, transform, load,normalize, compress, decompress, encode, decode, and otherwise processdata packets, signals, and other information. The data integrationservice may monitor entities, such as to identify data or informationfor integration. The data integration service may integrate dataregardless of required frequency, communication protocol, or businessrules needed for intricate integration patterns. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofprocesses systems, and any such processes or systems may be considered adata integration service herein, while in certain embodiments a givenservice may not be considered a data integration service herein. One ofskill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system and how to combine processes andsystems from the present disclosure to implement a data integrationservice and/or enhance operations of the contemplated system. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated system is a data integration service and/orwhether aspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation: ability to modify abusiness rule on the fly and alter a data integration protocol;communication with third party databases to pull in data to integratewith; synchronization of data across disparate platforms; connection toa central data warehouse; data storage capacity, processing capacity,and/or communication capacity distributed throughout the system; theconnection of separate, automated workflows; and the like. Whilespecific examples of data integration services and considerations aredescribed herein for purposes of illustration, any system benefittingfrom the disclosures herein, and any considerations understood to one ofskill in the art having the benefit of the disclosures herein, arespecifically contemplated within the scope of the present disclosure.

The term computational services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, computational services may beincluded as a part of one or more services, platforms, or microservices,such as blockchain services, data collection services, data integrationservices, valuation services, smart contract services, data monitoringservices, data mining, and/or any service that facilitates collection,access, processing, transformation, analysis, storage, visualization, orsharing of data. Certain processes may not be considered to be acomputational service. For example, a process may not be considered acomputational service depending on the sorts of rules governing theservice, an end product of the service, or the intent of the service.Accordingly, the benefits of the present disclosure may be applied in awide variety of processes systems, and any such processes or systems maybe considered a computational service herein, while in certainembodiments a given service may not be considered a computationalservice herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system and how to combineprocesses and systems from the present disclosure to implement one ormore computational service, and/or to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is a computationalservice and/or whether aspects of the present disclosure can benefit orenhance the contemplated system include, without limitation:agreement-based access to the service; mediate an exchange betweendifferent services; provides on demand computational power to a webservice; accomplishes one or more of monitoring, collection, access,processing, transformation, analysis, storage, integration,visualization, mining, or sharing of data. While specific examples ofcomputational services and considerations are described herein forpurposes of illustration, any system benefitting from the disclosuresherein, and any considerations understood to one of skill in the arthaving the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term sensor as utilized herein should be understood broadly. Withoutlimitation to any other aspect or description of the present disclosure,a sensor may be a device, module, machine, or subsystem that detects ormeasures a physical quality, event, or change. In embodiments, mayrecord, indicate, transmit, or otherwise respond to the detection ormeasurement. Examples of sensors may be sensors for sensing movement ofentities, for sensing temperatures, pressures or other attributes aboutentities or their environments, cameras that capture still or videoimages of entities, sensors that collect data about collateral orassets, such as, for example, regarding the location, condition (health,physical, or otherwise), quality, security, possession, or the like. Inembodiments, sensors may be sensitive to, but not influential on, theproperty to be measured but insensitive to other properties. Sensors maybe analog or digital. Sensors may include processors, transmitters,transceivers, memory, power, sensing circuit, electrochemical fluidreservoirs, light sources, and the like. Further examples of sensorscontemplated for use in the system include biosensors, chemical sensors,black silicon sensor, IR sensor, acoustic sensor, induction sensor,motion sensor, optical sensor, opacity sensor, proximity sensor,inductive sensor, Eddy-current sensor, passive infrared proximitysensor, radar, capacitance sensor, capacitive displacement sensor,hall-effect sensor, magnetic sensor, GPS sensor, thermal imaging sensor,thermocouple, thermistor, photoelectric sensor, ultrasonic sensor,infrared laser sensor, inertial motion sensor, MEMS internal motionsensor, ultrasonic 3D motion sensor, accelerometer, inclinometer, forcesensor, piezoelectric sensor, rotary encoders, linear encoders, ozonesensor, smoke sensor, heat sensor, magnetometer, carbon dioxidedetector, carbon monoxide detector, oxygen sensor, glucose sensor, smokedetector, metal detector, rain sensor, altimeter, GPS, detection ofbeing outside, detection of context, detection of activity, objectdetector (e.g. collateral), marker detector (e.g. geo-location marker),laser rangefinder, sonar, capacitance, optical response, heart ratesensor, or an RF/micropower impulse radio (MIR) sensor. In certainembodiments, a sensor may be a virtual sensor—for example determining aparameter of interest as a calculation based on other sensed parametersin the system. In certain embodiments, a sensor may be a smartsensor—for example reporting a sensed value as an abstractedcommunication (e.g., as a network communication) of the sensed value. Incertain embodiments, a sensor may provide a sensed value directly (e.g.,as a voltage level, frequency parameter, etc.) to a circuit, controller,or other device in the system. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit from a sensor. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated device is a sensor and/or whether aspects of thepresent disclosure can benefit from or be enhanced by the contemplatedsensor include, without limitation: the conditioning of anactivation/deactivation of a system to an environmental quality; theconversion of electrical output into measured quantities; the ability toenforce a geofence; the automatic modification of a loan in response tochange in collateral; and the like. While specific examples of sensorsand considerations are described herein for purposes of illustration,any system benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein, are specifically contemplated within thescope of the present disclosure.

The term storage condition and similar terms, as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, storage condition includes anenvironment, physical location, environmental quality, level ofexposure, security measures, maintenance description, accessibilitydescription, and the like related to the storage of an asset,collateral, or an entity specified and monitored in a contract, loan, oragreement or backing the contract, loan or other agreement, and thelike. Based on a storage condition of a collateral, an asset, or entity,actions may be taken to, maintain, improve, and/or confirm a conditionof the asset or the use of that asset as collateral. Based on a storagecondition, actions may be taken to alter the terms or conditions of aloan or bond. Storage condition may be classified in accordance withvarious rules, thresholds, conditional procedures, workflows, modelparameters, and the like and may be based on self-reporting or on datafrom Internet of Things devices, data from a set of environmentalcondition sensors, data from a set of social network analytic servicesand a set of algorithms for querying network domains, social media data,crowdsourced data, and the like. The storage condition may be tied to ageographic location relating to the collateral, the issuer, theborrower, the distribution of the funds or other geographic locations.Examples of IoT data may include images, sensor data, location data, andthe like. Examples of social media data or crowdsourced data may includebehavior of parties to the loan, financial condition of parties,adherence to a party's a term or condition of the loan, or bond, or thelike. Parties to the loan may include issuers of a bond, relatedentities, lender, borrower, 3rd parties with an interest in the debt.Storage condition may relate to an asset or type of collateral such as amunicipal asset, a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty. The storage condition may include an environment whereenvironment may include an environment selected from among a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle. Actions based on the storagecondition of a collateral, an asset or an entity may include managing,reporting on, altering, syndicating, consolidating, terminating,maintaining, modifying terms and/or conditions, foreclosing an asset, orotherwise handling a loan, contract, or agreement. One of skill in theart, having the benefit of the disclosure herein and knowledge about acontemplated storage condition, can readily determine which aspects ofthe present disclosure will benefit a particular application for astorage condition. Certain considerations for the person of skill in theart, or embodiments of the present disclosure in choosing an appropriatestorage condition to manage and/or monitor, include, without limitation:the legality of the condition given the jurisdiction of the transaction,the data available for a given collateral, the anticipated transactiontype (loan, bond or debt), the specific type of collateral, the ratio ofthe loan to value, the ratio of the collateral to the loan, the grosstransaction/loan amount, the credit scores of the borrower and thelender, ordinary practices in the industry, and other considerations.While specific examples of storage conditions are described herein forpurposes of illustration, any embodiment benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein are specificallycontemplated within the scope of the present disclosure.

The term geolocation and similar terms, as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, geolocation includes theidentification or estimation of the real-world geographic location of anobject, including the generation of a set of geographic coordinates(e.g. latitude and longitude) and/or street address. Based on ageolocation of a collateral, an asset, or entity, actions may be takento maintain or improve a condition of the asset or the use of that assetas collateral. Based on a geolocation, actions may be taken to alter theterms or conditions of a loan or bond. Based on a geolocation,determinations or predictions related to a transaction may beperformed—for example based upon the weather, civil unrest in aparticular area, and/or local disasters (e.g., an earthquake, flood,tornado, hurricane, industrial accident, etc.). Geolocations may bedetermined in accordance with various rules, thresholds, conditionalprocedures, workflows, model parameters, and the like and may be basedon self-reporting or on data from Internet of Things devices, data froma set of environmental condition sensors, data from a set of socialnetwork analytic services and a set of algorithms for querying networkdomains, social media data, crowdsourced data, and the like. Examples ofgeolocation data may include GPS coordinates, images, sensor data,street address, and the like. Geolocation data may be quantitative(e.g., longitude/latitude, relative to a plat map, etc.) and/orqualitative (e.g., categorical such as “coastal”, “rural”, etc.; “withinNew York City”, etc.). Geolocation data may be absolute (e.g., GPSlocation) or relative (e.g., within 100 yards of an expected location).Examples of social media data or crowdsourced data may include behaviorof parties to the loan as inferred by their geolocation, financialcondition of parties inferred by geolocation, adherence of parties to aterm or condition of the loan, or bond, or the like. Geolocation may bedetermined for an asset or type of collateral such as a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a consumable item, an edible item, a beverage, a preciousmetal, an item of jewelry, a gemstone, an antique, a fixture, an item offurniture, an item of equipment, a tool, an item of machinery, and anitem of personal property. Geolocation may be determined for an entitysuch as one of the parties, a third-party (e.g., an inspection service,maintenance service, cleaning service, etc. relevant to a transaction),or any other entity related to a transaction. The geolocation mayinclude an environment selected from among a municipal environment, acorporate environment, a securities trading environment, a real propertyenvironment, a commercial facility, a warehousing facility, atransportation environment, a manufacturing environment, a storageenvironment, a home, and a vehicle. Actions based on the geolocation ofa collateral, an asset or an entity may include managing, reporting on,altering, syndicating, consolidating, terminating, maintaining,modifying terms and/or conditions, foreclosing an asset, or otherwisehandling a loan, contract, or agreement. One of skill in the art, havingthe benefit of the disclosure herein and knowledge about a contemplatedsystem, can readily determine which aspects of the present disclosurewill benefit a particular application for a geolocation, and whichlocation aspect of an item is a geolocation for the contemplated system.Certain considerations for the person of skill in the art, orembodiments of the present disclosure in choosing an appropriategeolocation to manage, include, without limitation: the legality of thegeolocation given the jurisdiction of the transaction, the dataavailable for a given collateral, the anticipated transaction type(loan, bond or debt), the specific type of collateral, the ratio of theloan to value, the ratio of the collateral to the loan, the grosstransaction/loan amount, the frequency of travel of the borrower tocertain jurisdictions and other considerations, the mobility of thecollateral, and/or a likelihood of location-specific event occurrencerelevant to the transaction (e.g., weather, location of a relevantindustrial facility, availability of relevant services, etc.). Whilespecific examples of geolocation are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein are specifically contemplated withinthe scope of the present disclosure.

The term jurisdictional location and similar terms, as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, jurisdictional location refers tothe laws and legal authority governing a loan entity. The jurisdictionallocation may be based on a geolocation of an entity, a registrationlocation of an entity (e.g. a ship's flag state, a state ofincorporation for a business, and the like), a granting state forcertain rights such as intellectual priority, and the like. In certainembodiments, a jurisdictional location may be one or more of thegeolocations for an entity in the system. In certain embodiments, ajurisdictional location may not be the same as the geolocation of anyentity in the system (e.g., where an agreement specifies some otherjurisdiction). In certain embodiments, a jurisdictional location mayvary for entities in the system (e.g., borrower at A, lender at B,collateral positioned at C, agreement enforced at D, etc.). In certainembodiments, a jurisdictional location for a given entity may varyduring the operations of the system (e.g., due to movement ofcollateral, related data, changes in terms and conditions, etc.). Incertain embodiments, a given entity of the system may have more than onejurisdictional location (e.g., due to operations of the relevant law,and/or options available to one or more parties), and/or may havedistinct jurisdictional locations for different purposes. Ajurisdictional location of an item of collateral, an asset, or entity,actions may dictate certain terms or conditions of a loan or bond,and/or may indicate different obligations for notices to parties,foreclosure and/or default execution, treatment of collateral and/ordebt security, and/or treatment of various data within the system. Whilespecific examples of jurisdictional location are described herein forpurposes of illustration, any embodiment benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein are specificallycontemplated within the scope of the present disclosure.

The terms token of value, token, and variations such as cryptocurrencytoken, and the like, as utilized herein, in the context of increments ofvalue, may be understood broadly to describe either: (a) a unit ofcurrency or cryptocurrency (e.g. a cryptocurrency token), and (b) mayalso be used to represent a credential that can be exchanged for a good,service, data or other valuable consideration (e.g. a token of value).Without limitation to any other aspect or description of the presentdisclosure, in the former case, a token may also be used in conjunctionwith investment applications, token-trading applications, andtoken-based marketplaces. In the latter case, a token can also beassociated with rendering consideration, such as providing goods,services, fees, access to a restricted area or event, data, or othervaluable benefit. Tokens can be contingent (e.g. contingent accesstoken) or not contingent. For example, a token of value may be exchangedfor accommodations, (e.g. hotel rooms), dining/food goods and services,space (e.g. shared space, workspace, convention space, etc.),fitness/wellness goods or services, event tickets or event admissions,travel, flights or other transportation, digital content, virtual goods,license keys, or other valuable goods, services, data, or consideration.Tokens in various forms may be included where discussing a unit ofconsideration, collateral, or value, whether currency, cryptocurrency,or any other form of value such as goods, services, data, or otherbenefits. One of skill in the art, having the benefit of the disclosureherein and knowledge about a token, can readily determine the valuesymbolized or represented by a token, whether currency, cryptocurrency,good, service, data, or other value. While specific examples of tokensare described herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term pricing data as utilized herein may be understood broadly todescribe a quantity of information such as a price or cost, of one ormore items in a marketplace. Without limitation to any other aspect ordescription of the present disclosure, pricing data may also be used inconjunction with spot market pricing, forward market pricing, pricingdiscount information, promotional pricing, and other informationrelating to the cost or price of items. Pricing data may satisfy one ormore conditions, or may trigger application of one or more rules of asmart contract. Pricing data may be used in conjunction with other formsof data such as market value data, accounting data, access data, assetand facility data, worker data, event data, underwriting data, claimsdata or other forms of data. Pricing data may be adjusted for thecontext of the valued item (e.g., condition, liquidity, location, etc.)and/or for the context of a particular party. One of skill in the art,having the benefit of the disclosure herein and knowledge about pricingdata, can readily determine the purposes and use of pricing data invarious embodiments and contexts disclosed herein.

Without limitation to any other aspect or description of the presentdisclosure, a token includes any token including, without limitation, atoken of value, such as collateral, an asset, a reward, such as in atoken serving as representation of value, such as a value holdingvoucher that can be exchanged for goods or services. Certain componentsmay not be considered tokens individually, but may be considered tokensin an aggregated system, for example, a value placed on an asset may notbe in itself be a token, but the value of an asset may be placed in atoken of value, such as to be stored, exchanged, traded, and the like.For instance, in a non-limiting example, a blockchain circuit may bestructured to provide lenders a mechanism to store the value of assets,where the value attributed to the token is stored in a distributedledger of the blockchain circuit, but the token itself, assigned thevalue, may be exchanged, or traded such as through a token marketplace.In certain embodiments, a token may be considered a token for somepurposes but not for other purposes—for example, a token may be used asan indication of ownership of an asset, but this use of a token wouldnot be traded as a value where a token including the value of the assetmight. Accordingly, the benefits of the present disclosure may beapplied in a wide variety of systems, and any such systems may beconsidered a token herein, while in certain embodiments a given systemmay not be considered a token herein. One of skill in the art, havingthe benefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit a particular system,and/or how to combine processes and systems from the present disclosureto enhance operations of the contemplated system. Certain considerationsfor the person of skill in the art, in determining whether acontemplated system is a token and/or whether aspects of the presentdisclosure can benefit or enhance the contemplated system include,without limitation, access data such as relating to rights of access,tickets, and tokens; use in an investment application such as forinvestment in shares, interests, and tokens; a token-tradingapplication; a token-based marketplace; forms of consideration such asmonetary rewards and tokens; translating the value of a resources intokens; a cryptocurrency token; indications of ownership such asidentity information, event information, and token information; ablockchain-based access token traded in a marketplace application;pricing application such as for setting and monitoring pricing forcontingent access rights, underlying access rights, tokens, and fees;trading applications such as for trading or exchanging contingent accessrights or underlying access rights or tokens; tokens created and storedon a blockchain for contingent access rights resulting in an ownership(e.g., a ticket); and the like.

The term financial data as utilized herein may be understood broadly todescribe a collection of financial information about an asset,collateral or other item or items. Financial data may include revenues,expenses, assets, liabilities, equity, bond ratings, default, return onassets (ROA), return on investment (ROI), past performance, expectedfuture performance, earnings per share (EPS), internal rate of return(IRR), earnings announcements, ratios, statistical analysis of any ofthe foregoing (e.g. moving averages), and the like. Without limitationto any other aspect or description of the present disclosure, financialdata may also be used in conjunction with pricing data and market valuedata. Financial data may satisfy one or more conditions, or may triggerapplication of one or more rules of a smart contract. Financial data maybe used in conjunction with other forms of data such as market valuedata, pricing data, accounting data, access data, asset and facilitydata, worker data, event data, underwriting data, claims data or otherforms of data. One of skill in the art, having the benefit of thedisclosure herein and knowledge about financial data, can readilydetermine the purposes and use of pricing data in various embodimentsand contexts disclosed herein.

The term covenant as utilized herein may be understood broadly todescribe a term, agreement, or promise, such as performance of someaction or inaction. For example, a covenant may relate to behavior of aparty or legal status of a party. Without limitation to any other aspector description of the present disclosure, a covenant may also be used inconjunction with other related terms to an agreement or loan, such as arepresentation, a warranty, an indemnity, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, aforeclose condition, a default condition, and a consequence of default.A covenant or lack of performance of a covenant may satisfy one or moreconditions, or may trigger collection, breach or other terms andconditions. In certain embodiments, a smart contract may calculatewhether a covenant is satisfied and in cases where the covenant is notsatisfied, may enable automated action, or trigger other conditions orterms. One of skill in the art, having the benefit of the disclosureherein and knowledge about covenants, can readily determine the purposesand use of covenants in various embodiments and contexts disclosedherein.

The term entity as utilized herein may be understood broadly to describea party, a third-party (e.g., an auditor, regulator, service provider,etc.), and/or an identifiable related object such as an item ofcollateral related to a transaction. Example entities include anindividual, partnership, corporation, limited liability company or otherlegal organization. Other example entities include an identifiable itemof collateral, offset collateral, potential collateral, or the like. Forexample, an entity may be a given party, such as an individual, to anagreement or loan. Data or other terms herein may be characterized ashaving a context relating to an entity, such as entity-oriented data. Anentity may be characterized with a specific context or application, suchas a human entity, physical entity, transactional entity, or a financialentity, without limitation. An entity may have representatives thatrepresent or act on its behalf. Without limitation to any other aspector description of the present disclosure, an entity may also be used inconjunction with other related entities or terms to an agreement orloan, such as a representation, a warranty, an indemnity, a covenant, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a foreclose condition, a defaultcondition, and a consequence of default. An entity may have a set ofattributes such as: a publicly stated valuation, a set of property ownedby the entity as indicated by public records, a valuation of a set ofproperty owned by the entity, a bankruptcy condition, a foreclosurestatus, a contractual default status, a regulatory violation status, acriminal status, an export controls status, an embargo status, a tariffstatus, a tax status, a credit report, a credit rating, a web siterating, a set of customer reviews for a product of an entity, a socialnetwork rating, a set of credentials, a set of referrals, a set oftestimonials, a set of behavior, a location, and a geolocation, withoutlimitation. In certain embodiments, a smart contract may calculatewhether an entity has satisfied conditions or covenants and in caseswhere the entity has not satisfied such conditions or covenants, mayenable automated action, or trigger other conditions or terms. One ofskill in the art, having the benefit of the disclosure herein andknowledge about entities, can readily determine the purposes and use ofentities in various embodiments and contexts disclosed herein.

The term party as utilized herein may be understood broadly to describea member of an agreement, such as an individual, partnership,corporation, limited liability company or other legal organization. Forexample, a party may be a primary lender, a secondary lender, a lendingsyndicate, a corporate lender, a government lender, a bank lender, asecured lender, a bond issuer, a bond purchaser, an unsecured lender, aguarantor, a provider of security, a borrower, a debtor, an underwriter,an inspector, an assessor, an auditor, a valuation professional, agovernment official, an accountant or other entities having rights orobligations to an agreement, transaction or loan. A party maycharacterize a different term, such as transaction as in the termmulti-party transaction, where multiple parties are involved in atransaction, or the like, without limitation. A party may haverepresentatives that represent or act on its behalf. In certainembodiments, the term party may reference a potential party or aprospective party—for example, an intended lender or borrowerinteracting with a system, that may not yet be committed to an actualagreement during the interactions with the system. Without limitation toany other aspect or description of the present disclosure, an party mayalso be used in conjunction with other related parties or terms to anagreement or loan, such as a representation, a warranty, an indemnity, acovenant, a balance of debt, a fixed interest rate, a variable interestrate, a payment amount, a payment schedule, a balloon payment schedule,a specification of collateral, a specification of substitutability ofcollateral, an entity, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a foreclose condition, a defaultcondition, and a consequence of default. A party may have a set ofattributes such as: an identity, a creditworthiness, an activity, abehavior, a business practice, a status of performance of a contract,information about accounts receivable, information about accountspayable, information about the value of collateral, and other types ofinformation, without limitation. In certain embodiments, a smartcontract may calculate whether a party has satisfied conditions orcovenants and in cases where the party has not satisfied such conditionsor covenants, may enable automated action, or trigger other conditionsor terms. One of skill in the art, having the benefit of the disclosureherein and knowledge about parties, can readily determine the purposesand use of parties in various embodiments and contexts disclosed herein.

The term party attribute, entity attribute, or party/entity attribute asutilized herein may be understood broadly to describe a value,characteristic, or status of a party or entity. For example, attributesof a party or entity may be, without limitation: value, quality,location, net worth, price, physical condition, health condition,security, safety, ownership, identity, creditworthiness, activity,behavior, business practice, status of performance of a contract,information about accounts receivable, information about accountspayable, information about the value of collateral, and other types ofinformation, and the like. In certain embodiments, a smart contract maycalculate values, status or conditions associated with attributes of aparty or entity, and in cases where the party or entity has notsatisfied such conditions or covenants, may enable automated action, ortrigger other conditions or terms. One of skill in the art, having thebenefit of the disclosure herein and knowledge about attributes of aparty or entity, can readily determine the purposes and use of theseattributes in various embodiments and contexts disclosed herein.

The term lender as utilized herein may be understood broadly to describea party to an agreement offering an asset for lending, proceeds of aloan, and may include an individual, partnership, corporation, limitedliability company, or other legal organization. For example, a lendermay be a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,an unsecured lender, or other party having rights or obligations to anagreement, transaction or loan offering a loan to a borrower, withoutlimitation. A lender may have representatives that represent or act onits behalf. Without limitation to any other aspect or description of thepresent disclosure, an party may also be used in conjunction with otherrelated parties or terms to an agreement or loan, such as a borrower, aguarantor, a representation, a warranty, an indemnity, a covenant, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a security, a personal guarantee, a lien, a duration, aforeclose condition, a default condition, and a consequence of default.In certain embodiments, a smart contract may calculate whether a lenderhas satisfied conditions or covenants and in cases where the lender hasnot satisfied such conditions or covenants, may enable automated action,a notification or alert, or trigger other conditions or terms. One ofskill in the art, having the benefit of the disclosure herein andknowledge about a lender, can readily determine the purposes and use ofa lender in various embodiments and contexts disclosed herein.

The term crowdsourcing services as utilized herein may be understoodbroadly to describe services offered or rendered in conjunction with acrowdsourcing model or transaction, wherein a large group of people orentities supply contributions to fulfill a need, such as a loan, for thetransaction. Crowdsourcing services may be provided by a platform orsystem, without limitation. A crowdsourcing request may be communicatedto a group of information suppliers and by which responses to therequest may be collected and processed to provide a reward to at leastone successful information supplier. The request and parameters may beconfigured to obtain information related to the condition of a set ofcollateral for a loan. The crowdsourcing request may be published. Incertain embodiments, without limitation, crowdsourcing services may beperformed by a smart contract, wherein the reward is managed by a smartcontract that processes responses to the crowdsourcing request andautomatically allocates a reward to information that satisfies a set ofparameter configured for the crowdsourcing request. One of skill in theart, having the benefit of the disclosure herein and knowledge aboutcrowdsourcing services, can readily determine the purposes and use ofcrowdsourcing services in various embodiments and contexts disclosedherein.

The term publishing services as utilized herein may be understood todescribe a set of services to publish a crowdsourcing request.Publishing services may be provided by a platform or system, withoutlimitation. In certain embodiments, without limitation, publishingservices may be performed by a smart contract, wherein the crowdsourcingrequest is published, or publication is initiated by the smart contract.One of skill in the art, having the benefit of the disclosure herein andknowledge about publishing services, can readily determine the purposesand use of publishing services in various embodiments and contextsdisclosed herein.

The term interface as utilized herein may be understood broadly todescribe a component by which interaction or communication is achieved,such as a component of a computer, which may be embodied in software,hardware, or a combination thereof. For example, an interface may servea number of different purposes or be configured for differentapplications or contexts, such as, without limitation: an applicationprogramming interface, a graphic user interface, user interface,software interface, marketplace interface, demand aggregation interface,crowdsourcing interface, secure access control interface, networkinterface, data integration interface or a cloud computing interface, orcombinations thereof. An interface may serve to act as a way to enter,receive or display data, within the scope of lending, refinancing,collection, consolidation, factoring, brokering or foreclosure, withoutlimitation. An interface may serve as an interface for anotherinterface. Without limitation to any other aspect or description of thepresent disclosure, an interface may be used in conjunction withapplications, processes, modules, services, layers, devices, components,machines, products, sub-systems, interfaces, connections, or as part ofa system. In certain embodiments, an interface may be embodied insoftware, hardware, or a combination thereof, as well as stored on amedium or in memory. One of skill in the art, having the benefit of thedisclosure herein and knowledge about an interface, can readilydetermine the purposes and use of an interface in various embodimentsand contexts disclosed herein.

The term graphical user interface as utilized herein may be understoodas a type of interface to allow a user to interact with a system,computer, or other interfaces, in which interaction or communication isachieved through graphical devices or representations. A graphical userinterface may be a component of a computer, which may be embodied incomputer readable instructions, hardware, or a combination thereof. Agraphical user interface may serve a number of different purposes or beconfigured for different applications or contexts. Such an interface mayserve to act as a way to receive or display data using visualrepresentation, stimulus or interactive data, without limitation. Agraphical user interface may serve as an interface for another graphicaluser interface or other interfaces. Without limitation to any otheraspect or description of the present disclosure, a graphical userinterface may be used in conjunction with applications, processes,modules, services, layers, devices, components, machines, products,sub-systems, interfaces, connections, or as part of a system. In certainembodiments, a graphical user interface may be embodied in computerreadable instructions, hardware, or a combination thereof, as well asstored on a medium or in memory. Graphical user interfaces may beconfigured for any input types, including keyboards, a mouse, a touchscreen, and the like. Graphical user interfaces may be configured forany desired user interaction environments, including for example adedicated application, a web page interface, or combinations of these.One of skill in the art, having the benefit of the disclosure herein andknowledge about a graphical user interface, can readily determine thepurposes and use of a graphical user interface in various embodimentsand contexts disclosed herein.

The term user interface as utilized herein may be understood as a typeof interface to allow a user to interact with a system, computer, orother apparatus, in which interaction or communication is achievedthrough graphical devices or representations. A user interface may be acomponent of a computer, which may be embodied in software, hardware, ora combination thereof. The user interface may be stored on a medium orin memory. User interfaces may include drop-down menus, tables, forms,or the like with default, templated, recommended, or pre-configuredconditions. In certain embodiments, a user interface may include voiceinteraction. Without limitation to any other aspect or description ofthe present disclosure, a user interface may be used in conjunction withapplications, circuits, controllers, processes, modules, services,layers, devices, components, machines, products, sub-systems,interfaces, connections, or as part of a system. User interfaces mayserve a number of different purposes or be configured for differentapplications or contexts. For example, a lender-side user interface mayinclude features to view a plurality of customer profiles, but may berestricted from making certain changes. A debtor-side user interface mayinclude features to view details and make changes to a user account. A3rd party neutral-side interface (e.g. a 3rd party not having aninterest in an underlying transaction, such as a regulator, auditor,etc.) may have features that enable a view of company oversight andanonymized user data without the ability to manipulate any data, and mayhave scheduled access depending upon the 3rd party and the purpose forthe access. A 3rd party interested-side interface (e.g. a 3rd party thatmay have an interest in an underlying transaction, such as a collector,debtor advocate, investigator, partial owner, etc.) may include featuresenabling a view of particular user data with restrictions on makingchanges. Many more features of these user interfaces may be available toimplements embodiments of the systems and/or procedures describedthroughout the present disclosure. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of processes andsystems, and any such processes or systems may be considered a serviceherein. One of skill in the art, having the benefit of the disclosureherein and knowledge about a user interface, can readily determine thepurposes and use of a user interface in various embodiments and contextsdisclosed herein. Certain considerations for the person of skill in theart, in determining whether a contemplated interface is a user interfaceand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation: configurable views,ability to restrict manipulation or views, report functions, ability tomanipulate user profile and data, implement regulatory requirements,provide the desired user features for borrowers, lenders, and 3rdparties, and the like.

Interfaces and dashboards as utilized herein may further be understoodbroadly to describe a component by which interaction or communication isachieved, such as a component of a computer, which may be embodied insoftware, hardware, or a combination thereof. Interfaces and dashboardsmay acquire, receive, present, or otherwise administrate an item,service, offering or other aspects of a transaction or loan. Forexample, interfaces and dashboards may serve a number of differentpurposes or be configured for different applications or contexts, suchas, without limitation: an application programming interface, a graphicuser interface, user interface, software interface, marketplaceinterface, demand aggregation interface, crowdsourcing interface, secureaccess control interface, network interface, data integration interfaceor a cloud computing interface, or combinations thereof. An interface ordashboard may serve to act as a way to receive or display data, withinthe context of lending, refinancing, collection, consolidation,factoring, brokering or foreclosure, without limitation. An interface ordashboard may serve as an interface or dashboard for another interfaceor dashboard. Without limitation to any other aspect or description ofthe present disclosure, an interface may be used in conjunction withapplications, circuits, controllers, processes, modules, services,layers, devices, components, machines, products, sub-systems,interfaces, connections, or as part of a system. In certain embodiments,an interface or dashboard may be embodied in computer readableinstructions, hardware, or a combination thereof, as well as stored on amedium or in memory. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the purposes and use ofinterfaces and/or dashboards in various embodiments and contextsdisclosed herein.

The term domain as utilized herein may be understood broadly to describea scope or context of a transaction and/or communications related to atransaction. For example, a domain may serve a number of differentpurposes or be configured for different applications or contexts, suchas, without limitation: a domain for execution, a domain for a digitalasset, domains to which a request will be published, domains to whichsocial network data collection and monitoring services will be applied,domains to which Internet of Things data collection and monitoringservices will be applied, network domains, geolocation domains,jurisdictional location domains, and time domains. Without limitation toany other aspect or description of the present disclosure, one or moredomains may be utilized relative to any applications, circuits,controllers, processes, modules, services, layers, devices, components,machines, products, sub-systems, interfaces, connections, or as part ofa system. In certain embodiments, a domain may be embodied in computerreadable instructions, hardware, or a combination thereof, as well asstored on a medium or in memory. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a domain, canreadily determine the purposes and use of a domain in variousembodiments and contexts disclosed herein.

The term request (and variations) as utilized herein may be understoodbroadly to describe the action or instance of initiating or asking for athing (e.g. information, a response, an object, and the like) to beprovided. A specific type of request may also serve a number ofdifferent purposes or be configured for different applications orcontexts, such as, without limitation: a formal legal request (e.g. asubpoena), a request to refinance (e.g. a loan), or a crowdsourcingrequest. Systems may be utilized to perform requests as well as fulfillrequests. Requests in various forms may be included where discussing alegal action, a refinancing of a loan, or a crowdsourcing service,without limitation. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system, can readilydetermine the value of a request implemented in an embodiment. Whilespecific examples of requests are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term reward (and variations) as utilized herein may be understoodbroadly to describe a thing or consideration received or provided inresponse to an action or stimulus. Rewards can be of a financial type,or non-financial type, without limitation. A specific type of reward mayalso serve a number of different purposes or be configured for differentapplications or contexts, such as, without limitation: a reward event,claims for rewards, monetary rewards, rewards captured as a data set,rewards points, and other forms of rewards. Rewards may be triggered,allocated, generated for innovation, provided for the submission ofevidence, requested, offered, selected, administrated, managed,configured, allocated, conveyed, identified, without limitation, as wellas other actions. Systems may be utilized to perform the aforementionedactions. Rewards in various forms may be included where discussing aparticular behavior, or encouragement of a particular behavior, withoutlimitation. In certain embodiments herein, a reward may be utilized as aspecific incentive (e.g., rewarding a particular person that responds toa crowdsourcing request) or as a general incentive (e.g., providing areward responsive to a successful crowdsourcing request, in addition toor alternatively to a reward to the particular person that responded).One of skill in the art, having the benefit of the disclosure herein andknowledge about a reward, can readily determine the value of a rewardimplemented in an embodiment. While specific examples of rewards aredescribed herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term robotic process automation system as utilized herein may beunderstood broadly to describe a system capable of performing tasks orproviding needs for a system of the present disclosure. For example, arobotic process automation system, without limitation, can be configuredfor: negotiation of a set of terms and conditions for a loan,negotiation of refinancing of a loan, loan collection, consolidating aset of loans, managing a factoring loan, brokering a mortgage loan,training for foreclosure negotiations, configuring a crowdsourcingrequest based on a set of attributes for a loan, setting a reward,determining a set of domains to which a request will be published,configuring the content of a request, configuring a data collection andmonitoring action based on a set of attributes of a loan, determining aset of domains to which the Internet of Things data collection andmonitoring services will be applied, and iteratively training andimproving based on a set of outcomes. A robotic process automationsystem may include: a set of data collection and monitoring services, anartificial intelligence system, and another robotic process automationsystem which is a component of the higher level robotic processautomation system. The robotic process automation system may include: atleast one of the set of mortgage loan activities and the set of mortgageloan interactions includes activities among marketing activity,identification of a set of prospective borrowers, identification ofproperty, identification of collateral, qualification of borrower, titlesearch, title verification, property assessment, property inspection,property valuation, income verification, borrower demographic analysis,identification of capital providers, determination of available interestrates, determination of available payment terms and conditions, analysisof existing mortgage, comparative analysis of existing and new mortgageterms, completion of application workflow, population of fields ofapplication, preparation of mortgage agreement, completion of scheduleto mortgage agreement, negotiation of mortgage terms and conditions withcapital provider, negotiation of mortgage terms and conditions withborrower, transfer of title, placement of lien and closing of mortgageagreement. Example and non-limiting robotic process automation systemsmay include one or more user interfaces, interfaces with circuits and/orcontrollers throughout the system to provide, request, and/or sharedata, and/or one or more artificial intelligence circuits configured toiteratively improve one or more operations of the robotic processautomation system. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated robotic process automation system, can readily determinethe circuits, controllers, and/or devices to include to implement arobotic process automation system performing the selected functions forthe contemplated system. While specific examples of robotic processautomation systems are described herein for purposes of illustration,any embodiment benefitting from the disclosures herein, and anyconsiderations understood.

The term loan-related action (and other related terms such asloan-related event and loan-related activity) are utilized herein andmay be understood broadly to describe one or multiple actions, events oractivities relating to a transaction that includes a loan within thetransaction. The action, event or activity may occur in many differentcontexts of loans, such as lending, refinancing, consolidation,factoring, brokering, foreclosure, administration, negotiating,collecting, procuring, enforcing and data processing (e.g. datacollection), or combinations thereof, without limitation. A loan-relatedaction may be used in the form of a noun (e.g. a notice of default hasbeen communicated to the borrower with formal notice, which could beconsidered a loan-related action). A loan-related action, event, oractivity may refer to a single instance, or may characterize a group ofactions, events, or activities. For example, a single action such asproviding a specific notice to a borrower of an overdue payment may beconsidered a loan-related action. Similarly, a group of actions fromstart to finish relating to a default may also be considered a singleloan-related action. Appraisal, inspection, funding, and recording,without limitation, may all also be considered loan-related actions thathave occurred, as well as events relating to the loan, and may also beloan-related events. Similarly, these activities of completing theseactions may also be considered loan-related activities (e.g. appraising,inspecting, funding, recording, etc.), without limitation. In certainembodiments, a smart contract or robotic process automation system mayperform loan-related actions, loan-related events, or loan-relatedactivities for one or more of the parties, and process appropriate tasksfor completion of the same. In some cases the smart contract or roboticprocess automation system may not complete a loan-related action, anddepending upon such outcome this may enable an automated action or maytrigger other conditions or terms. One of skill in the art, having thebenefit of the disclosure herein and knowledge about loan-relatedactions, events, and activities can readily determine the purposes anduse of this term in various forms and embodiments as describedthroughout the present disclosure.

The term loan-related action, events, and activities, as noted herein,may also more specifically be utilized to describe a context for callingof a loan. A calling of a loan is an action wherein the lender candemand the loan be repaid, usually triggered by some other condition orterm, such as delinquent payment(s). For example, a loan-related actionfor calling of the loan may occur when a borrower misses three paymentsin a row, such that there is a severe delinquency in the loan paymentschedule, and the loan goes into default. In such a scenario, a lendermay be initiating loan-related actions for calling of the loan toprotect its rights. In such a scenario, perhaps the borrower pays a sumto cure the delinquency and penalties, which may also be considered as aloan-related action for calling of the loan. In some circumstances, asmart contract or robotic process automation system may initiate,administrate, or process loan-related actions for calling of the loan,which without limitation, may including providing notice, researching,and collecting payment history, or other tasks performed as a part ofthe calling of the loan. One of skill in the art, having the benefit ofthe disclosure herein and knowledge about loan-related actions forcalling of the loan, or other forms of the term and its various forms,can readily determine the purposes and use of this term in the contextof an event or other various embodiments and contexts disclosed herein.

The term loan-related action, events, and activities, as noted herein,may also more specifically be utilized to describe a context for paymentof a loan. Typically in transactions involving loans, withoutlimitation, a loan is repaid on a payment schedule. Various actions maybe taken to provide a borrower with information to pay back the loan, aswell as actions for a lender to receive payment for the loan. Forexample, if a borrower makes a payment on the loan, a loan-relatedaction for payment of the loan may occur. Without limitation, such apayment may comprise several actions that may occur with respect to thepayment on the loan, such as: the payment being tendered to the lender,the loan ledger or accounting reflecting that a payment has been made, areceipt provided to the borrower of the payment made, and the nextpayment being requested of the borrower. In some circumstances, a smartcontract or robotic process automation system may initiate,administrate, or process such loan-related actions for payment of theloan, which without limitation, may including providing notice to thelender, researching and collecting payment history, providing a receiptto the borrower, providing notice of the next payment due to theborrower, or other actions associated with payment of the loan. One ofskill in the art, having the benefit of the disclosure herein andknowledge about loan-related actions for payment of a loan, or otherforms of the term and its various forms, can readily determine thepurposes and use of this term in the context of an event or othervarious embodiments and contexts disclosed herein.

The term loan-related action, events, and activities, as noted herein,may also more specifically be utilized to describe a context for apayment schedule or alternative payment schedule. Typically intransactions involving loans, without limitation, a loan is repaid on apayment schedule, which may be modified over time. Or, such a paymentschedule may be developed and agreed in the alternative, with analternative payment schedule. Various actions may be taken in thecontext of a payment schedule or alternate payment schedule for thelender or the borrower, such as: the amount of such payments, when suchpayments are due, what penalties or fees may attach to late payments, orother terms. For example, if a borrower makes an early payment on theloan, a loan-related action for payment schedule and alternative paymentschedule of the loan may occur; in such case, perhaps the payment isapplied as principal, with the regular payment still being due. Withoutlimitation, loan-related actions for a payment schedule and alternativepayment schedule may comprise several actions that may occur withrespect to the payment on the loan, such as: the payment being tenderedto the lender, the loan ledger or accounting reflecting that a paymenthas been made, a receipt provided to the borrower of the payment made, acalculation if any fees are attached or due, and the next payment beingrequested of the borrower. In certain embodiments, an activity todetermine a payment schedule or alternative payment schedule may be aloan-related action, event, or activity. In certain embodiments, anactivity to communicate the payment schedule or alternative paymentschedule (e.g., to the borrower, the lender, or a 3rd party) may be aloan-related action, event, or activity. In some circumstances, a smartcontract circuit or robotic process automation system may initiate,administrate, or process such loan-related actions for payment scheduleand alternative payment schedule, which without limitation, may includeproviding notice to the lender, researching and collecting paymenthistory, providing a receipt to the borrower, calculating the next duedate, calculating the final payment amount and date, providing notice ofthe next payment due to the borrower, determining the payment scheduleor an alternate payment schedule, communicating the payment scheduler oran alternate payment schedule, or other actions associated with paymentof the loan. One of skill in the art, having the benefit of thedisclosure herein and knowledge about loan-related actions for paymentschedule and alternative payment schedule, or other forms of the termand its various forms, can readily determine the purposes and use ofthis term in the context of an event or other various embodiments andcontexts disclosed herein.

The term regulatory notice requirement (and any derivatives) as utilizedherein may be understood broadly to describe an obligation or conditionto communicate a notification or message to another party or entity. Theregulatory notice requirement may be required under one or moreconditions that are triggered, or generally required. For example, alender may have a regulatory notice requirement to provide notice to aborrower of a default of a loan, or change of an interest rate of aloan, or other notifications relating to a transaction or loan. Theregulatory aspect of the term may be attributed to jurisdiction-specificlaws, rules, or codes that require certain obligations of communication.In certain embodiments, a policy directive may be treated as aregulatory notice requirement, for example where a lender has aninternal notice policy that may exceed the regulatory requirements ofone or more of the jurisdictional locations related to a transaction.The notice aspect generally relates to formal communications, which maytake many different forms, but may specifically be specified as aparticular form of notice, such as a certified mail, facsimile, emailtransmission, or other physical or electronic form, a content for thenotice, and/or a timing requirement related to the notice. Therequirement aspect relates to the necessity of a party to complete itsobligation to be in compliance with laws, rules, codes, policies,standard practices, or terms of an agreement or loan. In certainembodiments, a smart contract may process or trigger regulatory noticerequirements and provide appropriate notice to a borrower. This may bebased on location of at least one of: the lender, the borrower, thefunds provided via the loan, the repayment of the loan, and thecollateral of the loan, or other locations as designated by the terms ofthe loan, transaction, or agreement. In cases where a party or entityhas not satisfied such regulatory notice requirements, certain changesin the rights or obligations between the parties may be triggered—forexample where a lender provides a non-compliant notice to the borrower,an automated action or trigger based on the terms and conditions of theloan, and/or based on external information (e.g., a regulatoryprescription, internal policy of the lender, etc.) may be affected by asmart contract circuit and/or robotic process automation system may beimplemented. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the purposes and use ofregulatory notice requirements in various embodiments and contextsdisclosed herein.

The term regulatory notice requirement may also be utilized herein todescribe an obligation or condition to communicate a notification ormessage to another party or entity based upon a general or specificpolicy, rather than based on a particular jurisdiction, or laws, rules,or codes of a particular location (as in regulatory notice requirementthat may be jurisdiction-specific). The regulatory notice requirementmay be prudent or suggested, rather than obligatory or required, underone or more conditions that are triggered, or generally required. Forexample, a lender may have a regulatory notice requirement that ispolicy based to provide notice to a borrower of a new informationalwebsite, or will experience a change of an interest rate of a loan inthe future, or other notifications relating to a transaction or loanthat are advisory or helpful, rather than mandatory (although mandatorynotices may also fall under a policy basis). Thus, in policy based usesof the regulatory notice requirement term, a smart contract circuit mayprocess or trigger regulatory notice requirements and provideappropriate notice to a borrower which may or may not necessarily berequired by a law, rule, or code. The basis of the notice orcommunication may be out of prudence, courtesy, custom, or obligation.

The term regulatory notice may also be utilized herein to describe anobligation or condition to communicate a notification or message toanother party or entity specifically, such as a lender or borrower. Theregulatory notice may be specifically directed toward any party orentity, or a group of parties or entities. For example, a particularnotice or communication may be advisable or required to be provided to aborrower, such as on circumstances of a borrower's failure to providescheduled payments on a loan resulting in a default. As such, such aregulatory notice directed to a particular user, such as a lender orborrower, may be as a result of a regulatory notice requirement that isjurisdiction-specific or policy-based, or otherwise. Thus, in somecircumstances a smart contract may process or trigger a regulatorynotice and provide appropriate notice to a specific party such as aborrower, which may or may not necessarily be required by a law, rule,or code, but may otherwise be provided out of prudence, courtesy orcustom. In cases where a party or entity has not satisfied suchregulatory notice requirements to a specific party or parties, it maycreate circumstances where certain rights may be forgiven by one or moreparties or entities, or may enable automated action or trigger otherconditions or terms. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the purposes and use ofregulatory notice requirements based in various embodiments and contextsdisclosed herein.

The term regulatory foreclosure requirement (and any derivatives) asutilized herein may be understood broadly to describe an obligation orcondition in order to trigger, process or complete default of a loan,foreclosure, or recapture of collateral, or other related foreclosureactions. The regulatory foreclosure requirement may be required underone or more conditions that are triggered, or generally required. Forexample, a lender may have a regulatory foreclosure requirement toprovide notice to a borrower of a default of a loan, or othernotifications relating to the default of a loan prior to foreclosure.The regulatory aspect of the term may be attributed tojurisdiction-specific laws, rules, or codes that require certainobligations of communication. The foreclosure aspect generally relatesto the specific remedy of foreclosure, or a recapture of collateralproperty and default of a loan, which may take many different forms, butmay be specified in the terms of the loan. The requirement aspectrelates to the necessity of a party to complete its obligation in orderto be in compliance or performance of laws, rules, codes or terms of anagreement or loan. In certain embodiments, a smart contract circuit mayprocess or trigger regulatory foreclosure requirements and processappropriate tasks relating to such a foreclosure action. This may bebased on a jurisdictional location of at least one of the lender, theborrower, the fund provided via the loan, the repayment of the loan, andthe collateral of the loan, or other locations as designated by theterms of the loan, transaction, or agreement. In cases where a party orentity has not satisfied such regulatory foreclosure requirements,certain rights may be forgiven by the party or entity (e.g. a lender),or such a failure to comply with the regulatory notice requirement mayenable automated action or trigger other conditions or terms. One ofskill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system, can readilydetermine the purposes and use of regulatory foreclosure requirements invarious embodiments and contexts disclosed herein.

The term regulatory foreclosure requirement may also be utilized hereinto describe an obligation or in order to trigger, process or completedefault of a loan, foreclosure, or recapture of collateral, or otherrelated foreclosure actions. based upon a general or specific policyrather than based on a particular jurisdiction, or laws, rules, or codesof a particular location (as in regulatory foreclosure requirement thatmay be jurisdiction-specific). The regulatory foreclosure requirementmay be prudent or suggested, rather than obligatory or required, underone or more conditions that are triggered, or generally required. Forexample, a lender may have a regulatory foreclosure requirement that ispolicy based to provide notice to a borrower of a default of a loan, orother notifications relating to a transaction or loan that are advisoryor helpful, rather than mandatory (although mandatory notices may alsofall under a policy basis). Thus, in policy based uses of the regulatoryforeclosure requirement term, a smart contract may process or triggerregulatory foreclosure requirements and provide appropriate notice to aborrower which may or may not necessarily be required by a law, rule, orcode. The basis of the notice or communication may be out of prudence,courtesy, custom, industry practice, or obligation.

The term regulatory foreclosure requirements may also be utilized hereinto describe an obligation or condition that is to be performed withregard to a specific user, such as a lender or a borrower. Theregulatory notice may be specifically directed toward any party orentity, or a group of parties or entities. For example, a particularnotice or communication may be advisable or required to be provided to aborrower, such as on circumstances of a borrower's failure to providescheduled payments on a loan resulting in a default. As such, such aregulatory foreclosure requirement is directed to a particular user,such as a lender or borrower, and may be a result of a regulatoryforeclosure requirement that is jurisdiction-specific or policy-based,or otherwise. For example, the foreclosure requirement may be related toa specific entity involved with a transaction (e.g., the currentborrower has been a customer for 30 years, so s/he receives uniquetreatment), or to a class of entities (e.g., “preferred” borrowers, or“first time default” borrowers). Thus, in some circumstances a smartcontract circuit may process or trigger an obligation or action thatmust be taken pursuant to a foreclosure, where the action is directed orfrom a specific party such as a lender or a borrower, which may or maynot necessarily be required by a law, rule, or code, but may otherwisebe provided out of prudence, courtesy, or custom. In certainembodiments, the obligation or condition that is to be performed withregard to the specific user may form a part of the terms and conditionsor otherwise be known to the specific user to which it applies (e.g., aninsurance company or bank that advertises a specific practice withregard to a specific class of customers, such as first-time defaultcustomers, first-time accident customers, etc.), and in certainembodiments the obligation or condition that is to be performed withregard to the specific user may be unknown to the specific user to whichit applies (e.g., a bank has a policy relating to a class of users towhich the specific user belongs, but the specific user is not aware ofthe classification).

The terms value, valuation, valuation model (and similar terms) asutilized herein should be understood broadly to describe an approach toevaluate and determine the estimated value for collateral. Withoutlimitation to any other aspect or description of the present disclosure,a valuation model may be used in conjunction with: collateral (e.g. asecured property), artificial intelligence services (e.g. to improve avaluation model), data collection and monitoring services (e.g. to set avaluation amount), valuation services (e.g. the process of informing,using, and/or improving a valuation model), and/or outcomes relating totransactions in collateral (e.g. as a basis of improving the valuationmodel). “Jurisdiction-specific valuation model” is also used as avaluation model used in a specific geographic/jurisdictional area orregion; wherein, the jurisdiction can be specific to jurisdiction of thelender, the borrower, the delivery of funds, the payment of the loan orthe collateral of the loan, or combinations thereof. In certainembodiments, a jurisdiction-specific valuation model considersjurisdictional effects on a valuation of collateral, including at least:rights and obligations for borrowers and lenders in the relevantjurisdiction(s); jurisdictional effects on the ability to move, import,export, substitute, and/or liquidate the collateral; jurisdictionaleffects on the timing between default and foreclosure or collection ofcollateral; and/or jurisdictional effects on the volatility and/orsensitivity of collateral value determinations. In certain embodiments,a geolocation-specific valuation model considers geolocation effects ona valuation of the collateral, which may include a similar list ofconsiderations relative jurisdictional effects (although thejurisdictional location(s) may be distinct from the geolocation(s)), butmay also include additional effects, such as: weather-related effects;distance of the collateral from monitoring, maintenance, or seizureservices; and/or proximity of risk phenomenon (e.g., fault lines,industrial locations, a nuclear plant, etc.). A valuation model mayutilize a valuation of offset collateral (e.g., a similar item ofcollateral, a generic value such as a market value of similar orfungible collateral, and/or a value of an item that correlates with avalue of the collateral) as a part of the valuation of the collateral.In certain embodiments, an artificial intelligence circuit includes oneor more machine learning and/or artificial intelligence algorithms, toimprove a valuation model, including, for example, utilizing informationover time between multiple transactions involving similar or offsetcollateral, and/or utilizing outcome information (e.g., where loantransactions are completed successfully or unsuccessfully, and/or inresponse to collateral seizure or liquidation events that demonstratereal-world collateral valuation determinations) from the same or othertransactions to iteratively improve the valuation model. In certainembodiments, an artificial intelligence circuit is trained on acollateral valuation data set, for example previously determinedvaluations and/or through interactions with a trainer (e.g., a human,accounting valuations, and/or other valuation data). In certainembodiments, the valuation model and/or parameters of the valuationmodel (e.g., assumptions, calibration values, etc.) may be determinedand/or negotiated as a part of the terms and conditions of thetransaction (e.g., a loan, a set of loans, and/or a subset of the set ofloans). One of skill in the art, having the benefit of the disclosureherein and knowledge ordinarily available about a contemplated system,can readily determine which aspects of the present disclosure willbenefit a particular application for a valuation model, and how tochoose or combine valuation models to implement an embodiment of avaluation model. Certain considerations for the person of skill in theart, or embodiments of the present disclosure in choosing an appropriatevaluation model, include, without limitation: the legal considerationsof a valuation model given the jurisdiction of the collateral; the dataavailable for a given collateral; the anticipated transaction/loantype(s); the specific type of collateral; the ratio of the loan tovalue; the ratio of the collateral to the loan; the grosstransaction/loan amount; the credit scores of the borrower; accountingpractices for the loan type and/or related industry; uncertaintiesrelated to any of the foregoing; and/or sensitivities related to any ofthe foregoing. While specific examples of valuation models andconsiderations are described herein for purposes of illustration, anyembodiment benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein, are specifically contemplated within thescope of the present disclosure

The term market value data, or marketplace information, (and other formsor variations) as utilized herein may be understood broadly to describedata or information relating to the valuation of a property, asset,collateral, or other valuable items which may be used as the subject ofa loan, collateral, or transaction. Market value data or marketplaceinformation may change from time to time, and may be estimated,calculated, or objectively or subjectively determined from varioussources of information. Market value data or marketplace information maybe related directly to an item of collateral or to an off-set item ofcollateral. Market value data or marketplace information may includefinancial data, market ratings, product ratings, customer data, marketresearch to understand customer needs or preferences, competitiveintelligence re. competitors, suppliers, and the like, entities sales,transactions, customer acquisition cost, customer lifetime value, brandawareness, churn rate, and the like. The term may occur in manydifferent contexts of contracts or loans, such as lending, refinancing,consolidation, factoring, brokering, foreclosure, and data processing(e.g. data collection), or combinations thereof, without limitation.Market value data or marketplace information may be used as a noun toidentify a single figure or a plurality of figures or data. For example,market value data or marketplace information may be utilized by a lenderto determine if a property or asset will serve as collateral for asecured loan, or may alternatively be utilized in the determination offoreclosure if a loan is in default, without limitation to thesecircumstances in use of the term. Marketplace value data or marketplaceinformation may also be used to determine loan-to-value figures orcalculations. In certain embodiments, a collection service, smartcontract circuit, and/or robotic process automation system may estimateor calculate market value data or marketplace information from one ormore sources of data or information. In some cases market data value ormarketplace information, depending upon the data/information containedtherein, may enable automated action, or trigger other conditions orterms. One of skill in the art, having the benefit of the disclosureherein and knowledge ordinarily available about a contemplated systemand available relevant marketplace information, can readily determinethe purposes and use of this term in various forms, embodiments andcontexts disclosed herein.

The terms similar collateral, similar to collateral, off-set collateral,and other forms or variations as utilized herein may be understoodbroadly to describe a property, asset or valuable item that may be likein nature to a collateral (e.g. an article of value held in security)regarding a loan or other transaction. Similar collateral may refer to aproperty, asset, collateral or other valuable item which may beaggregated, substituted, or otherwise referred to in conjunction withother collateral, whether the similarity comes in the form of a commonattribute such as type of item of collateral, category of the item ofcollateral, an age of the item of collateral, a condition of the item ofcollateral, a history of the item of collateral, an ownership of theitem of collateral, a caretaker of the item of collateral, a security ofthe item of collateral, a condition of an owner of the item ofcollateral, a lien on the item of collateral, a storage condition of theitem of collateral, a geolocation of the item of collateral, and ajurisdictional location of the item of collateral, and the like. Incertain embodiments, an offset collateral references an item that has avalue correlation with an item of collateral—for example, an offsetcollateral may exhibit similar price movements, volatility, storagerequirements, or the like for an item of collateral. In certainembodiments, similar collateral may be aggregated to form a largersecurity interest or collateral for an additional loan or distribution,or transaction. In certain embodiments, offset collateral may beutilized to inform a valuation of the collateral. In certainembodiments, a smart contract circuit or robotic process automationsystem may estimate or calculate figures, data or information relatingto similar collateral, or may perform a function with respect toaggregating similar collateral. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system can readily determine the purposes and useof similar collateral, offset collateral, or related terms as theyrelate to collateral in various forms, embodiments, and contextsdisclosed herein.

The term restructure (and other forms such as restructuring) as utilizedherein may be understood broadly to describe a modification of terms orconditions, properties, collateral, or other considerations affecting aloan or transaction. Restructuring may result in a successful outcomewhere amended terms or conditions are adopted between parties, or anunsuccessful outcome where no modification or restructure occurs,without limitation. Restructuring can occur in many contexts ofcontracts or loans, such as application, lending, refinancing,collection, consolidation, factoring, brokering, foreclosure, andcombinations thereof, without limitation. Debt may also be restructured,which may indicate that debts owed to a party are modified as to timing,amounts, collateral, or other terms. For example, a borrower mayrestructure debt of a loan to accommodate a change of financialconditions, or a lender may offer to a borrower the restructuring of adebt for its own needs or prudence. In certain embodiments, a smartcontract circuit or robotic process automation system may automaticallyor manually restructure debt based on a monitored condition, or createoptions for restructuring a debt, administrate the process ofnegotiating or effecting the restructuring of a debt, or other actionsin connection with restructuring or modifying terms of a loan ortransaction. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the purposes and use of thisterm, whether in the context of debt or otherwise, in variousembodiments and contexts disclosed herein.

The term social network data collection, social network monitoringservices, and social network data collection and monitoring services(and its various forms or derivatives) as utilized herein may beunderstood broadly to describe services relating to the acquisition,organizing, observing, or otherwise acting upon data or informationderived from one or more social networks. The social network datacollection and monitoring services may be a part of a related system ofservices or a standalone set of services. Social network data collectionand monitoring services may be provided by a platform or system, withoutlimitation. Social network data collection and monitoring services maybe used in a variety of contexts such as lending, refinancing,negotiation, collection, consolidation, factoring, brokering,foreclosure, and combinations thereof, without limitation. Requests ofsocial network data collection and monitoring, with configurationparameters, may be requested by other services, automatically initiated,or triggered to occur based on conditions or circumstances that occur.An interface may be provided to configure, initiate, display, orotherwise interact with social network data collection and monitoringservices. Social networks, as utilized herein, reference any massplatform where data and communications occur between individuals and/orentities, where the data and communications are at least partiallyaccessible to an embodiment system. In certain embodiments, the socialnetwork data includes publicly available (e.g., accessible without anyauthorization) information. In certain embodiments, the social networkdata includes information that is properly accessible to an embodimentsystem, but may include subscription access or other access toinformation that is not freely available to the public, but may beaccessible (e.g., consistent with a privacy policy of the social networkwith its users). A social network may be primarily social in nature, butmay additionally or alternatively include professional networks, alumninetworks, industry related networks, academically oriented networks, orthe like. In certain embodiments, a social network may be acrowdsourcing platform, such as a platform configured to accept queriesor requests directed to users (and/or a subset of users, potentiallymeeting specified criteria), where users may be aware that certaincommunications will be shared and accessible to requestors, at least aportion of users of the platform, and/or publicly available. In certainembodiments, without limitation, social network data collection andmonitoring services may be performed by a smart contract circuit or arobotic process automation system. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system, can readily determine the purposes and useof social network data collection and monitoring services in variousembodiments and contexts disclosed herein.

The term crowdsource and social network information as utilized hereinmay further be understood broadly to describe information acquired orprovided in conjunction with a crowdsourcing model or transaction, orinformation acquired or provided on or in conjunction with a socialnetwork. Crowdsource and social network information may be provided by aplatform or system, without limitation. Crowdsource and social networkinformation may be acquired, provided, or communicated to or from agroup of information suppliers and by which responses to the request maybe collected and processed. Crowdsource and social network informationmay provide information, conditions or factors relating to a loan oragreement. Crowdsource and social network information may be private orpublished, or combinations thereof, without limitation. In certainembodiments, without limitation, crowdsource and social networkinformation may be acquired, provided, organized, or processed, withoutlimitation, by a smart contract circuit, wherein the crowdsource andsocial network information may be managed by a smart contract circuitthat processes the information to satisfy a set of configuredparameters. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system can readily determine the purposes and use of thisterm in various embodiments and contexts disclosed herein.

The term negotiate (and other forms such as negotiating or negotiation)as utilized herein may be understood broadly to describe discussions orcommunications to bring about or obtain a compromise, outcome, oragreement between parties or entities. Negotiation may result in asuccessful outcome where terms are agreed between parties, or anunsuccessful outcome where the parties do not agree to specific terms,or combinations thereof, without limitation. A negotiation may besuccessful in one aspect or for a particular purpose, and unsuccessfulin another aspect or for another purpose. Negotiation can occur in manycontexts of contracts or loans, such as lending, refinancing,collection, consolidation, factoring, brokering, foreclosure, andcombinations thereof, without limitation. For example, a borrower maynegotiate an interest rate or loan terms with a lender. In anotherexample, a borrower in default may negotiate an alternative resolutionto avoid foreclosure with a lender. In certain embodiments, a smartcontract circuit or robotic process automation system may negotiate forone or more of the parties, and process appropriate tasks for completingor attempting to complete a negotiation of terms. In some casesnegotiation by the smart contract or robotic process automation systemmay not complete or be successful. Successful negotiation may enableautomated action or trigger other conditions or terms to be implementedby the smart contract circuit or robotic process automation system. Oneof skill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system, can readilydetermine the purposes and use of negotiation in various embodiments andcontexts disclosed herein.

The term negotiate in various forms may more specifically be utilizedherein in verb form (e.g., to negotiate) or in noun forms (e.g., anegotiation), or other forms to describe a context of mutual discussionleading to an outcome. For example, a robotic process automation systemmay negotiate terms and conditions on behalf of a party, which would bea use as a verb clause. In another example, a robotic process automationsystem may be negotiating terms and conditions for modification of aloan, or negotiating a consolidation offer, or other terms. As a nounclause, a negotiation (e.g., an event) may be performed by a roboticprocess automation system. Thus, in some circumstances a smart contractcircuit or robotic process automation system may negotiate (e.g., as averb clause) terms and conditions, or the description of doing so may beconsidered a negotiation (e.g., as a noun clause). One of skill in theart, having the benefit of the disclosure herein and knowledge aboutnegotiating and negotiation, or other forms of the word negotiate, canreadily determine the purposes and use of this term in variousembodiments and contexts disclosed herein.

The term negotiate in various forms may also specifically be utilized todescribe an outcome, such as a mutual compromise or completion ofnegotiation leading to an outcome. For example, a loan may, by roboticprocess automation system or otherwise, be considered negotiated as asuccessful outcome that has resulted in an agreement between parties,where the negotiation has reached completion. Thus, in somecircumstances a smart contract circuit or robotic process automationsystem may have negotiated to completion a set of terms and conditions,or a negotiated loan. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available for a contemplatedsystem, can readily determine the purposes and use of this term as itrelates to a mutually agreed outcome through completion of negotiationin various embodiments and contexts disclosed herein.

The term negotiate in various forms may also specifically be utilized tocharacterize an event such as a negotiating event, or an eventnegotiation, including reaching a set of agreeable terms betweenparties. An event requiring mutual agreement or compromise betweenparties may be considered a negotiating event, without limitation. Forexample, during the procurement of a loan, the process of reaching amutually acceptable set of terms and conditions between parties could beconsidered a negotiating event. Thus, in some circumstances a smartcontract circuit or robotic process automation system may accommodatethe communications, actions, or behaviors of the parties for anegotiated event.

The term collection (and other forms such as collect or collecting) asutilized herein may be understood broadly to describe the acquisition ofa tangible (e.g., physical item), intangible (e.g., data, a license, ora right), or monetary (e.g., payment) item, or other obligation or assetfrom a source. The term generally may relate to the entire prospectiveacquisition of such an item from related tasks in early stages torelated tasks in late stages or full completion of the acquisition ofthe item. Collection may result in a successful outcome where the itemis tendered to a party, or may or an unsuccessful outcome where the itemis not tendered or acquired to a party, or combinations thereof (e.g., alate or otherwise deficient tender of the item), without limitation.Collection may occur in many different contexts of contracts or loans,such as lending, refinancing, consolidation, factoring, brokering,foreclosure, and data processing (e.g., data collection), orcombinations thereof, without limitation. Collection may be used in theform of a noun (e.g., data collection or the collection of an overduepayment where it refers to an event or characterizes an event), mayrefer as a noun to an assortment of items (e.g., a collection ofcollateral for a loan where it refers to a number of items in atransaction), or may be used in the form of a verb (e.g., collecting apayment from the borrower). For example, a lender may collect an overduepayment from a borrower through an online payment, or may have asuccessful collection of overdue payments acquired through a customerservice telephone call. In certain embodiments, a smart contract circuitor robotic process automation system may perform collection for one ormore of the parties, and process appropriate tasks for completing orattempting collection for one or more items (e.g., an overdue payment).In some cases negotiation by the smart contract or robotic processautomation system may not complete or be successful, and depending uponsuch outcomes this may enable automated action or trigger otherconditions or terms. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the purposes and use ofcollection in various forms, embodiments, and contexts disclosed herein.

The term collection in various forms may also more specifically beutilized herein in noun form to describe a context for an event orthing, such as a collection event, or a collection payment. For example,a collection event may refer to a communication to a party or otheractivity that relates to acquisition of an item in such an activity,without limitation. A collection payment, for example, may relate to apayment made by a borrower that has been acquired through the process ofcollection, or through a collection department with a lender. Althoughnot limited to an overdue, delinquent, or defaulted loan, collection maycharacterize an event, payment or department, or other noun associatedwith a transaction or loan, as being a remedy for something that hasbecome overdue. Thus, in some circumstances a smart contract circuit orrobotic process automation system may collect a payment or installmentfrom a borrower, and the activity of doing so may be considered acollection event, without limitation.

The term collection in various forms may also more specifically beutilized herein as an adjective or other forms to describe a contextrelating to litigation, such as the outcome of a collection litigation(e.g., litigation regarding overdue or default payments on a loan). Forexample, the outcome of a collection litigation may be related todelinquent payments which are owed by a borrower or other party, andcollection efforts relating to those delinquent payments may belitigated by parties. Thus, in some circumstances a smart contractcircuit or robotic process automation system may receive, determine, orotherwise administrate the outcome of collection litigation.

The term collection in various forms may also more specifically beutilized herein as an adjective or other forms to describe a contextrelating to an action of acquisition, such as a collection action (e.g.,actions to induce tendering or acquisition of overdue or defaultpayments on a loan or other obligation). The terms collection yield,financial yield of collection, and/or collection financial yield may beused. The result of such a collection action may or may not have afinancial yield. For example, a collection action may result in thepayment of one or more outstanding payments on a loan, which may rendera financial yield to another party such as the lender. Thus, in somecircumstances a smart contract circuit or robotic process automationsystem may render a financial yield from a collection action, orotherwise administrate or in some manner assist in a financial yield ofa collection action. In embodiments, a collection action may include theneed for collection litigation.

The term collection in various forms (collection ROI, ROI on collection,ROI on collection activity, collection activity ROI, and the like) mayalso more specifically be utilized herein to describe a context relatingto an action of receiving value, such as a collection action (e.g.actions to induce tendering or acquisition of overdue or defaultpayments on a loan or other obligation), wherein there is a return oninvestment (ROI). The result of such a collection action may or may nothave an ROI, either with respect to the collection action itself (as anROI on the collection action) or as an ROI on the broader loan ortransaction that is the subject of the collection action. For example,an ROI on a collection action may be prudent or not with respect to adefault loan, without limitation, depending upon whether the ROI will beprovided to a party such as the lender. A projected ROI on collectionmay be estimated, or may also be calculated given real events thattranspire. In some circumstances, a smart contract circuit or roboticprocess automation system may render an estimated ROI for a collectionaction or collection event, or may calculate an ROI for actual eventstranspiring in a collection action or collection event, withoutlimitation. In embodiments, such a ROI may be a positive or negativefigure, whether estimated or actual.

The term reputation, measure of reputation, lender reputation, borrowerreputation, entity reputation, and the like may include general, widelyheld beliefs, opinions, and/or perceptions that are generally held aboutan individual, entity, collateral, and the like. A measure forreputation may be determined based on social data includinglikes/dislikes, review of entity or products and services provided bythe entity, rankings of the company or product, current and historicmarket and financial data include price, forecast, buy/sellrecommendations, financial news regarding entity, competitors, andpartners. Reputations may be cumulative in that a product reputation andthe reputation of a company leader or lead scientist may influence theoverall reputation of the entity. Reputation of an institute associatedwith an entity (e.g., a school being attended by a student) mayinfluence the reputation of the entity. In some circumstances, a smartcontract circuit or robotic process automation system may collect, orinitiate collection of data related to the above and determine a measureor ranking of reputation. A measure or ranking of an entity's reputationmay be used by a smart contract circuit or robotic process automationsystem in determining whether to enter into an agreement with theentity, determination of terms and conditions of a loan, interest rates,and the like. In certain embodiments, indicia of a reputationdetermination may be related to outcomes of one or more transactions(e.g., a comparison of “likes” on a particular social media data set toan outcome index, such as successful payments, successful negotiationoutcomes, ability to liquidate a particular type of collateral, etc.) todetermine the measure or ranking of an entity's reputation. One of skillin the art, having the benefit of the disclosure herein and knowledgeordinarily available about a contemplated system, can readily determinethe purposes and use of the reputation, a measure or ranking of thereputation, and/or utilization of the reputation in negotiations,determination of terms and conditions, determination of whether toproceed with a transaction, and other various embodiments and contextsdisclosed herein.

The term collection in various forms (e.g., collector) may also morespecifically be utilized herein to describe a party or entity thatinduces, administrates, or facilitates a collection action, collectionevent, or other collection related context. The measure of reputation ofa party involved, such as a collector, or during the context of acollection, may be estimated or calculated using objective, subjective,or historical metrics or data. For example, a collector may be involvedin a collection action, and the reputation of that collector may be usedto determine decisions, actions, or conditions. Similarly, a collectionmay be also used to describe objective, subjective or historical metricsor data to measure the reputation of a party involved, such as a lender,borrower, or debtor. In some circumstances, a smart contract circuit orrobotic process automation system may render a collection or measures,or implement a collector, within the context of a transaction or loan.

The term collection and data collection in various forms, including datacollection systems, may also more specifically be utilized herein todescribe a context relating to the acquisition, organization, orprocessing of data, or combinations thereof, without limitation. Theresult of such a data collection may be related or wholly unrelated to acollection of items (e.g., grouping of the items, either physically orlogically), or actions taken for delinquent payments (e.g., collectionof collateral, a debt, or the like), without limitation. For example, adata collection may be performed by a data collection system, whereindata is acquired, organized, or processed for decision-making,monitoring, or other purposes of prospective or actual transaction orloan. In some circumstances, a smart contract or robotic processautomation system may incorporate data collection or a data collectionsystem, to perform portions or entire tasks of data collection, withoutlimitation. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available for a contemplatedsystem, can readily determine and distinguish the purposes and use ofcollection in the context of data or information as used herein.

The terms refinance, refinancing activity(ies), refinancinginteractions, refinancing outcomes, and similar terms, as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure refinance andrefinancing activities include replacing an existing mortgage, loan,bond, debt transaction, or the like with a new mortgage, loan, bond, ordebt transaction that pays off or ends the previous financialarrangement. In certain embodiments, any change to terms and conditionsof a loan, and/or any material change to terms and conditions of a loan,may be considered a refinancing activity. In certain embodiments, arefinancing activity is considered only those changes to a loanagreement that result in a different financial outcome for the loanagreement. Typically, the new loan should be advantageous to theborrower or issuer, and/or mutually agreeable (e.g., improving a rawfinancial outcome of one, and a security or other outcome for theother). Refinancing may be done to reduce interest rates, lower regularpayments, change the loan term, change the collateral associated withthe loan, consolidate debt into a single loan, restructure debt, changea type of loan (e.g., variable rate to fixed rate), pay off a loan thatis due, in response to an improved credit score, to enlarge the loan,and/or in response to a change in market conditions (e.g., interestrates, value of collateral, and the like).

Refinancing activity may include initiating an offer to refinance,initiating a request to refinance, configuring a refinancing interestrate, configuring a refinancing payment schedule, configuring arefinancing balance in a response to the amount or terms of therefinanced loan, configuring collateral for a refinancing includingchanges in collateral used, changes in terms and conditions for thecollateral, a change in the amount of collateral and the like, managinguse of proceeds of a refinancing, removing or placing a lien ondifferent items of collateral as appropriate given changes in terms andconditions as part of a refinancing, verifying title for a new orexisting item of collateral to be used to secure the refinanced loan,managing an inspection process title for a new or existing item ofcollateral to be used to secure the refinanced loan, populating anapplication to refinance a loan, negotiating terms and conditions for arefinanced loan and closing a refinancing. Refinance and refinancingactivities may be disclosed in the context of data collection andmonitoring services that collect a training set of interactions betweenentities for a set of loan refinancing activities. Refinance andrefinancing activities may be disclosed in the context of an artificialintelligence system that is trained using the collected training set ofinteractions that includes both refinancing activities and outcomes. Thetrained artificial intelligence may then be used to recommend arefinance activity, evaluate a refinance activity, make a predictionaround an expected outcome of refinancing activity, and the like.Refinance and refinancing activities may be disclosed in the context ofsmart contract systems which may automate a subset of the interactionsand activities of refinancing. In an example, a smart contract systemmay automatically adjust an interest rate for a loan based oninformation collected via at least one of an Internet of Things system,a crowdsourcing system, a set of social network analytic services and aset of data collection and monitoring services. The interest rate may beadjusted based on rules, thresholds, model parameters that determine, orrecommend, an interest rate for refinancing a loan based on interestrates available to the lender from secondary lenders, risk factors ofthe borrower (including predicted risk based on one or more predictivemodels using artificial intelligence), marketing factors (such ascompeting interest rates offered by other lenders), and the like.Outcomes and events of a refinancing activity may be recorded in adistributed ledger. Based on the outcome of a refinance activity, asmart contract for the refinance loan may be automatically reconfiguredto define the terms and conditions for the new loan such as a principalamount of debt, a balance of debt, a fixed interest rate, a variableinterest rate, a payment amount, a payment schedule, a balloon paymentschedule, a specification of collateral, a specification ofsubstitutability of collateral, a party, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

One of skill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system can readilydetermine which aspects of the present disclosure will benefit from aparticular application of a refinance activity, how to choose or combinerefinance activities, how to implement systems, services, or circuits toautomatically perform of one or more (or all) aspects of a refinanceactivity, and the like. Certain considerations for the person of skillin the art, or embodiments of the present disclosure in choosing anappropriate training sets of interactions with which to train anartificial intelligence to take action, recommend or predict the outcomeof certain refinance activities. While specific examples of refinanceand refinancing activities are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms consolidate, consolidation activity(ies), loan consolidation,debt consolidation, consolidation plan, and similar terms, as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure consolidate,consolidation activity(ies), loan consolidation, debt consolidation, orconsolidation plan are related to the use of a single large loan to payoff several smaller loans, and/or the use of one or more of a set ofloans to pay off at least a portion of one or more of a second set ofloans. In embodiments, loan consolidation may be secured (i.e., backedby collateral) or unsecured. Loans may be consolidated to obtain a lowerinterest rate than one or more of the current loans, to reduce totalmonthly loan payments, and/or to bring a debtor into compliance on theconsolidated loans or other debt obligations of the debtor. Loans thatmay be classified as candidates for consolidation may be determinedbased on a model that processes attributes of entities involved in theset of loans including identity of a party, interest rate, paymentbalance, payment terms, payment schedule, type of loan, type ofcollateral, financial condition of party, payment status, condition ofcollateral, and value of collateral. Consolidation activities mayinclude managing at least one of identification of loans from a set ofcandidate loans, preparation of a consolidation offer, preparation of aconsolidation plan, preparation of content communicating a consolidationoffer, scheduling a consolidation offer, communicating a consolidationoffer, negotiating a modification of a consolidation offer, preparing aconsolidation agreement, executing a consolidation agreement, modifyingcollateral for a set of loans, handling an application workflow forconsolidation, managing an inspection, managing an assessment, settingan interest rate, deferring a payment requirement, setting a paymentschedule, and closing a consolidation agreement. In embodiments, theremay be systems, circuits, and/or services configured to create,configure (such as using one or more templates or libraries), modify,set, or otherwise handle (such as in a user interface) various rules,thresholds, conditional procedures, workflows, model parameters, and thelike to determine, or recommend, a consolidation action or plan for alending transaction or a set of loans based on one or more events,conditions, states, actions, or the like. In embodiments, aconsolidation plan may be based on various factors, such as the statusof payments, interest rates of the set of loans, prevailing interestrates in a platform marketplace or external marketplace, the status ofthe borrowers of a set of loans, the status of collateral or assets,risk factors of the borrower, the lender, one or more guarantors, marketrisk factors and the like. Consolidation and consolidation activitiesmay be disclosed in the context of data collection and monitoringservices that collect a training set of interactions between entitiesfor a set of loan consolidation activities. consolidation andconsolidation activities may be disclosed in the context of anartificial intelligence system that is trained using the collectedtraining set of interactions that includes both consolidation activitiesand outcomes associated with those activities. The trained artificialintelligence may then be used to recommend a consolidation activity,evaluate a consolidation activity, make a prediction around an expectedoutcome of consolidation activity, and the like based models includingstatus of debt, condition of collateral or assets used to secure or backa set of loans, the state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties (such as networth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatingdebt preferences), and others. Debt consolidation, loan consolidationand associated consolidation activities may be disclosed in the contextof smart contract systems which may automate a subset of theinteractions and activities of consolidation. In embodiments,consolidation may include consolidation with respect to terms andconditions of sets of loans, selection of appropriate loans,configuration of payment terms for consolidated loans, configuration ofpayoff plans for pre-existing loans, communications to encourageconsolidation, and the like. In embodiments, the artificial intelligenceof a smart contract may automatically recommend or set rules,thresholds, actions, parameters and the like (optionally by learning todo so based on a training set of outcomes over time), resulting in arecommended consolidation plan, which may specify a series of actionsrequired to accomplish a recommended or desired outcome of consolidation(such as within a range of acceptable outcomes), which may be automatedand may involve conditional execution of steps based on monitoredconditions and/or smart contract terms, which may be created,configured, and/or accounted for by the consolidation plan.Consolidation plans may be determined and executed based at least onepart on market factors (such as competing interest rates offered byother lenders, values of collateral, and the like) as well as regulatoryand/or compliance factors. Consolidation plans may be generated and/orexecuted for creation of new consolidated loans, for secondary loansrelated to consolidated loans, for modifications of existing loansrelated to consolidation, for refinancing terms of a consolidated loan,for foreclosure situations (e.g., changing from secured loan rates tounsecured loan rates), for bankruptcy or insolvency situations, forsituations involving market changes (e.g., changes in prevailinginterest rates) and others. consolidation.

Certain of the activities related to loans, collateral, entities, andthe like may apply to a wide variety of loans and may not applyexplicitly to consolidation activities. The categorization of theactivities as consolidation activities may be based on the context ofthe loan for which the activities are taking place. However, one ofskill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system can readilydetermine which aspects of the present disclosure will benefit from aparticular application of a consolidation activity, how to choose orcombine consolidation activities, how to implement selected services,circuits, and/or systems described herein to perform certain loanconsolidation operations, and the like. While specific examples ofconsolidation and consolidation activities are described herein forpurposes of illustration, any embodiment benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The terms factoring a loan, factoring a loan transaction, factors,factoring a loan interaction, factoring assets or sets of assets usedfor factoring and similar terms, as utilized herein should be understoodbroadly. Without limitation to any other aspect or description of thepresent disclosure factoring may be applied to factoring assets such asinvoices, inventory, accounts receivable, and the like, where therealized value of the item is in the future. For example, the accountsreceivable is worth more when it has been paid and there is less risk ofdefault. Inventory and Work in Progress (WIP) may be worth more as finalproduct rather than components. References to accounts receivable shouldbe understood to encompass these terms and not be limiting. Factoringmay include a sale of accounts receivable at a discounted rate for valuein the present (often cash). Factoring may also include the use ofaccounts receivable as collateral for a short term loan. In both casesthe value of the accounts receivable or invoices may be discounted formultiple reasons including the future value of money, a term of theaccounts receivable (e.g., 30 day net payment vs. 90 day net payment), adegree of default risk on the accounts receivable, a status ofreceivables, a status of work-in-progress (WIP), a status of inventory,a status of delivery and/or shipment, financial condition(s) of partiesowing against the accounts receivable, a status of shipped and/orbilled, a status of payments, a status of the borrower, a status ofinventory, a risk factor of a borrower, a lender, one or moreguarantors, market risk factors, a status of debt (are there other lienspresent on the accounts receivable or payment owed on the inventory, acondition of collateral assets (e.g. the condition of the inventory, isit current or out of date, are invoices in arrears), a state of abusiness or business operation, a condition of a party to thetransaction (such as net worth, wealth, debt, location, and otherconditions), a behavior of a party to the transaction (such as behaviorsindicating preferences, behaviors indicating negotiation styles, and thelike), current interest rates, any current regulatory and complianceissues associated with the inventory or accounts receivable (e.g., ifinventory is being factored, has the intended product receivedappropriate approvals), and there legal actions against the borrower,and many others, including predicted risk based on one or morepredictive models using artificial intelligence). A factor is anindividual, business, entity, or groups thereof which agree to providevalue in exchange for either the outright acquisition of the invoices ina sale or the use of the invoices as collateral for a loan for thevalue. Factoring a loan may include the identification of candidates(both lenders and borrowers) for factoring, a plan for factoringspecifying the proposed receivables (e.g., all, some, only those meetingcertain criteria), and a proposed discount factor, communication of theplan to potential parties, proffering an offer and receiving an offer,verification of quality of receivables, conditions regarding treatmentof the receivables for the term of the loan. While specific examples offactoring and factoring activities are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms mortgage, brokering a mortgage, mortgage collateral, mortgageloan activities, and/or mortgage related activities as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a mortgage is an interactionwhere a borrower provides the title or a lien on the title of an item ofvalue, typically property, to a lender as security in exchange for moneyor another item of value, to be repaid, typically with interest, to thelender. The exchange includes the condition that, upon repayment of theloan, the title reverts to the borrower and/or the lien on the propertyis removed. The brokering of a mortgage may include the identificationof potential properties, lenders, and other parties to the loan, andarranging or negotiating the terms of the mortgage. Certain componentsor activities may not be considered mortgage related individually, butmay be considered mortgage related when used in conjunction with amortgage, act upon a mortgage, are related to an entity or party to amortgage, and the like. For example, brokering may apply to the offeringof a variety of loans including unsecured loans, outright sale ofproperty and the like. Mortgage activities and mortgage interactions mayinclude mortgage marketing activity, identification of a set ofprospective borrowers, identification of property to mortgage,identification of collateral property to mortgage, qualification ofborrower, title search and/or title verification for prospectivemortgage property, property assessment, property inspection, or propertyvaluation for prospective mortgage property, income verification,borrower demographic analysis, identification of capital providers,determination of available interest rates, determination of availablepayment terms and conditions, analysis of existing mortgage(s),comparative analysis of existing and new mortgage terms, completion ofapplication workflow (e.g., keep the application moving forward byinitiating next steps in the process as appropriate), population offields of application, preparation of mortgage agreement, completion ofschedule for mortgage agreement, negotiation of mortgage terms andconditions with capital provider, negotiation of mortgage terms andconditions with borrower, transfer of title, placement of lien onmortgaged property and closing of mortgage agreement, and similar terms,as utilized herein should be understood broadly. While specific examplesof mortgages and mortgage brokering are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms debt management, debt transactions, debt actions, debt termsand conditions, syndicating debt, consolidating debt, and/or debtportfolios, as utilized herein should be understood broadly. Withoutlimitation to any other aspect or description of the present disclosurea debt includes something of monetary value that is owed to another. Aloan typically results in the borrower holding the debt (e.g., the moneythat must be paid back according to the terms of the loan, which mayinclude interest). Consolidation of debt includes the use of a new,single loan to pay back multiple loans (or various other configurationsof debt structuring as described herein, and as understood to one ofskill in the art). Often the new loan may have better terms or lowerinterest rates. Debt portfolios include a number of pieces or groups ofdebt, often having different characteristics including term, risk, andthe like. Debt portfolio management may involve decisions regarding thequantity and quality of the debt being held and how best to balance thevarious debts to achieve a desired risk/reward position based on:investment policy, return on risk determinations for individual piecesof debt, or groups of debt. Debt may be syndicated where multiplelenders fund a single loan (or set of loans) to a borrower. Debtportfolios may be sold to a third party (e.g., at a discounted rate).Debt compliance includes the various measures taken to ensure that debtis repaid. Demonstrating compliance may include documentation of theactions taken to repay the debt.

Transactions related to a debt (debt transactions) and actions relatedto the debt (debt actions) may include offering a debt transaction,underwriting a debt transaction, setting an interest rate, deferring apayment requirement, modifying an interest rate, validating title,managing inspection, recording a change in title, assessing the value ofan asset, calling a loan, closing a transaction, setting terms andconditions for a transaction, providing notices required to be provided,foreclosing on a set of assets, modifying terms and conditions, settinga rating for an entity, syndicating debt, and/or consolidating debt.Debt terms and conditions may include a balance of debt, a principalamount of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of assets that back the bond, a specification ofsubstitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default. While specific examples of debt management anddebt management activities are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms condition, condition classification, classification models,condition management, and similar terms, as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure condition, conditionclassification, classification models, condition management, includeclassifying or determining a condition of an asset, issuer, borrower,loan, debt, bond, regulatory status, term or condition for a bond, loanor debt transaction that is specified and monitored in the contract, andthe like. Based on a classified condition of an asset, conditionmanagement may include actions to maintain or improve a condition of theasset or the use of that asset as collateral. Based on a classifiedcondition of an issuer, borrower, party regulatory status, and the like,condition management may include actions to alter the terms orconditions of a loan or bond. Condition classification may includevarious rules, thresholds, conditional procedures, workflows, modelparameters, and the like to classify a condition of an asset, issuer,borrower, loan, debt, bond, regulatory status, term or condition for abond, loan or debt transaction, and the like based on data from Internetof Things devices, data from a set of environmental condition sensors,data from a set of social network analytic services and a set ofalgorithms for querying network domains, social media data, crowdsourceddata, and the like. Condition classification may include grouping orlabeling entities, or clustering the entities, as similarly positionedwith regard to some aspect of the classified condition (e.g., a risk,quality, ROI, likelihood for recovery, likelihood to default, or someother aspect of the related debt).

Various classification models are disclosed where the classification andclassification model may be tied to a geographic location relating tothe collateral, the issuer, the borrower, the distribution of the fundsor other geographic locations. Classification and classification modelsare disclosed where artificial intelligence is used to improve aclassification model (e.g. refine a model by making refinements usingartificial intelligence data). Thus artificial intelligence may beconsidered, in some instances, as a part of a classification model andvice versa. Classification and classification models are disclosed wheresocial media data, crowdsourced data, or IoT data is used as input forrefining a model, or as input to a classification model. Examples of IoTdata may include images, sensor data, location data, and the like.Examples of social media data or crowdsourced data may include behaviorof parties to the loan, financial condition of parties, adherence to aparties to a term or condition of the loan, or bond, or the like.Parties to the loan may include issuers of a bond, related entities,lender, borrower, 3rd parties with an interest in the debt. Conditionmanagement may be discussed in connection with smart contract serviceswhich may include condition classification, data collection andmonitoring, and bond, loan, and debt transaction management. Datacollection and monitoring services are also discussed in conjunctionwith classification and classification models which are related whenclassifying an issuer of a bond issuer, an asset or collateral assetrelated to the bond, collateral assets backing the bond, parties to thebond, and sets of the same. In some embodiments a classification modelmay be included when discussing bond types. Specific steps, factors orrefinements may be considered a part of a classification model. Invarious embodiments, the classification model may change both in anembodiment, or in the same embodiment which is tied to a specificjurisdiction. Different classification models may use different datasets (e.g., based on the issuer, the borrower, the collateral assets,the bond type, the loan type, and the like) and multiple classificationmodels may be used in a single classification. For example, one type ofbond, such as a municipal bond, may allow a classification model that isbased on bond data from municipalities of similar size and economicprosperity, whereas another classification model may emphasize data fromIoT sensors associated with a collateral asset. Accordingly, differentclassification models will offer benefits or risks over otherclassification models, depending upon the embodiment and the specificsof the bond, loan, or debt transaction. A classification model includesan approach or concept for classification. Conditions classified for abond, loan, or debt transaction may include a principal amount of debt,a balance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of assets that back the bond, loan or debt transaction, aspecification of substitutability of assets, a party, an issuer, apurchaser, a guarantee, a guarantor, a security, a personal guarantee, alien, a duration, a covenant, a foreclose condition, a defaultcondition, and/or a consequence of default. Conditions classified mayinclude type of bond issuer such as a municipality, a corporation, acontractor, a government entity, a non-governmental entity, and anon-profit entity. Entities may include a set of issuers, a set ofbonds, a set of parties, and/or a set of assets. Conditions classifiedmay include an entity condition such as net worth, wealth, debt,location, and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating debt preferences), and thelike. Conditions classified may include an asset or type of collateralsuch as a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property. Conditions classified mayinclude a bond type where bond type may include a municipal bond, agovernment bond, a treasury bond, an asset-backed bond, and a corporatebond. Conditions classified may include a default condition, aforeclosure condition, a condition indicating violation of a covenant, afinancial risk condition, a behavioral risk condition, a policy riskcondition, a financial health condition, a physical defect condition, aphysical health condition, an entity risk condition, and an entityhealth condition. Conditions classified may include an environment whereenvironment may include an environment selected from among a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle. Actions based on thecondition of an asset, issuer, borrower, loan, debt, bond, regulatorystatus and the like, may include managing, reporting on, syndicating,consolidating, or otherwise handling a set of bonds (such as municipalbonds, corporate bonds, performance bonds, and others), a set of loans(subsidized and unsubsidized, debt transactions and the like,monitoring, classifying, predicting, or otherwise handling thereliability, quality, status, health condition, financial condition,physical condition or other information about a guarantee, a guarantor,a set of collateral supporting a guarantee, a set of assets backing aguarantee, or the like. Bond transaction activities in response to acondition of the bond may include offering a debt transaction,underwriting a debt transaction, setting an interest rate, deferring apayment requirement, modifying an interest rate, validating title,managing inspection, recording a change in title, assessing the value ofan asset, calling a loan, closing a transaction, setting terms andconditions for a transaction, providing notices required to be provided,foreclosing on a set of assets, modifying terms and conditions, settinga rating for an entity, syndicating debt, and/or consolidating debt.

One of skill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system, can readilydetermine which aspects of the present disclosure will benefit aparticular application for a classification model, how to choose orcombine classification models to arrive at a condition, and/or calculatea value of collateral given the required data. Certain considerationsfor the person of skill in the art, or embodiments of the presentdisclosure in choosing an appropriate condition to manage, include,without limitation: the legality of the condition given the jurisdictionof the transaction, the data available for a given collateral, theanticipated transaction type (loan, bond or debt), the specific type ofcollateral, the ratio of the loan to value, the ratio of the collateralto the loan, the gross transaction/loan amount, the credit scores of theborrower and the lender, and other considerations. While specificexamples of conditions, condition classification, classification models,and condition management are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms classify, classifying, classification, categorization,categorizing, categorize (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, classifying a condition or itemmay include actions to sort the condition or item into a group orcategory based on some aspect, attribute, or characteristic of thecondition or item where the condition or item is common or similar forall the items placed in that classification, despite divergentclassifications or categories based on other aspects or conditions atthe time. Classification may include recognition of one or moreparameters, features, characteristics, or phenomena associated with acondition or parameter of an item, entity, person, process, item,financial construct, or the like. Conditions classified by a conditionclassifying system may include a default condition, a foreclosurecondition, a condition indicating violation of a covenant, a financialrisk condition, a behavioral risk condition, a contractual performancecondition, a policy risk condition, a financial health condition, aphysical defect condition, a physical health condition, an entity riskcondition, and/or an entity health condition. A classification model mayautomatically classify or categorize items, entities, process, items,financial constructs or the like based on data received from a varietyof sources. The classification model may classify items based on asingle attribute or a combination of attributes, and/or may utilize dataregarding the items to be classified and a model. The classificationmodel may classify individual items, entities, financial constructs, orgroups of the same. A bond may be classified based on the type of bond(e.g., municipal bonds, corporate bonds, performance bonds, and thelike), rate of return, bond rating (3rd party indicator of bond qualitywith respect to bond issuer's financial strength, and/or ability to bapbond's principal and interest, and the like. Lenders or bond issuers maybe classified based on the type of lender or issuer, permittedattributes (e.g. based on income, wealth, location (domestic orforeign), various risk factors, status of issuers, and the like.Borrowers may be classified based on permitted attributes (e.g. income,wealth, total assets, location, credit history), risk factors, currentstatus (e.g., employed, a student), behaviors of parties (such asbehaviors indicating preferences, reliability, and the like), and thelike. A condition classifying system may classify a student recipient ofa loan based on progress of the student toward a degree, the student'sgrades or standing in their classes, students' status at the school(matriculated, on probation and the like), the participation of astudent in a non-profit activity, a deferment status of the student, andthe participation of the student in a public interest activity.Conditions classified by a condition classifying system may include astate of a set of collateral for a loan or a state of an entity relevantto a guarantee for a loan. Conditions classified by a conditionclassifying system may include a medical condition of a borrower,guarantor, subsidizer, or the like. Conditions classified by a conditionclassifying system may include compliance with at least one of a law, aregulation, or a policy related to a lending transaction or lendinginstitute. Conditions classified by a condition classifying system mayinclude a condition of an issuer for a bond, a condition of a bond, arating of a loan-related entity, and the like. Conditions classified bya condition classifying system may include an identify of a machine, acomponent, or an operational mode. Conditions classified by a conditionclassifying system may include a state or context (such as a state of amachine, a process, a workflow, a marketplace, a storage system, anetwork, a data collector, or the like). A condition classifying systemmay classify a process involving a state or context (e.g., a datastorage process, a network coding process, a network selection process,a data marketplace process, a power generation process, a manufacturingprocess, a refining process, a digging process, a boring process, and/orother process described herein. A condition classifying system mayclassify a set of loan refinancing actions based on a predicted outcomeof the set of loan refinancing actions. A condition classifying systemmay classify a set of loans as candidates for consolidation based onattributes such as identity of a party, an interest rate, a paymentbalance, payment terms, payment schedule, a type of loan, a type ofcollateral, a financial condition of party, a payment status, acondition of collateral, a value of collateral, and the like. Acondition classifying system may classify the entities involved in a setof factoring loans, bond issuance activities, mortgage loans, and thelike. A condition classifying system may classify a set of entitiesbased on projected outcomes from various loan management activities. Acondition classifying system may classify a condition of a set ofissuers based on information from Internet of Things data collection andmonitoring services, a set of parameters associated with an issuer, aset of social network monitoring and analytic services, and the like. Acondition classifying system may classify a set of loan collectionactions, loan consolidation actions, loan negotiation actions, loanrefinancing actions and the like based on a set of projected outcomesfor those activities and entities.

The term subsidized loan, subsidizing a loan, (and similar terms) asutilized herein should be understood broadly. Without limitation to anyother aspect or description of the present disclosure, a subsidized loanis the loan of money or an item of value wherein payment of interest onthe value of the loan may be deferred, postponed, or delayed, with orwithout accrual, such as while the borrower is in school, is unemployed,is ill, and the like. In embodiments, a loan may be subsidized when thepayment of interest on a portion or subset of the loan is borne orguaranteed by someone other than the borrower. Examples of subsidizedloans may include a municipal subsidized loan, a government subsidizedloan, a student loan, an asset-backed subsidized loan, and a corporatesubsidized loan. An example of a subsidized student loan may includestudent loans which may be subsidized by the government and on whichinterest may be deferred or not accrue based on progress of the studenttoward a degree, the participation of a student in a non-profitactivity, a deferment status of the student, and the participation ofthe student in a public interest activity. An example of a governmentsubsidized housing loan may include governmental subsidies which mayexempt the borrower from paying closing costs, first mortgage paymentand the like. Conditions for such subsidized loans may include locationof the property (rural or urban), income of the borrower, militarystatus of the borrower, ability of the purchased home to meet health andsafety standards, a limit on the profits you can earn on the sale ofyour home, and the like. Certain usages of the word loan may not applyto a subsidized loan but rather to a regular loan. One of skill in theart, having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit fromconsideration of a subsidized loan (e.g., in determining the value ofthe loan, negotiations related to the loan, terms and conditions relatedto the loan, etc.) wherein the borrower may be relieved of some of theloan obligations common for non-subsidized loans, where the subsidy mayinclude forgiveness, delay or deferment of interest on a loan, or thepayment of the interest by a third party. The subsidy may include thepayment of closing costs including points, first payment and the like bya person or entity other than the borrower, and/or how to combineprocesses and systems from the present disclosure to enhance or benefitfrom title validation.

The term subsidized loan management (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, subsidized loanmanagement may include a plurality of activities and solutions formanaging or responding to one or more events related to a subsidizedloan wherein such events may include requests for a subsidized loan,offering a subsidized loan, accepting a subsidized loan, providingunderwriting information for a subsidized loan, providing a creditreport on a borrower seeking a subsidized loan, deferring a requiredpayment as part of the loan subsidy, setting an interest rate for asubsidized loan where a lower interest rate may be part of the subsidy,deferring a payment requirement as part of the loan subsidy, identifyingcollateral for a loan, validating title for collateral or security for aloan, recording a change in title of property, assessing the value ofcollateral or security for a loan, inspecting property that is involvedin a loan, identifying a change in condition of an entity relevant to aloan, a change in value of an entity that is relevant to a loan, achange in job status of a borrower, a change in financial rating of alender, a change in financial value of an item offered as a security,providing insurance for a loan, providing evidence of insurance forproperty related to a loan, providing evidence of eligibility for aloan, identifying security for a loan, underwriting a loan, making apayment on a loan, defaulting on a loan, calling a loan, closing a loan,setting terms and conditions for a loan, foreclosing on property subjectto a loan, modifying terms and conditions for a loan, for setting termsand conditions for a loan (such as a principal amount of debt, a balanceof debt, a fixed interest rate, a variable interest rate, a paymentamount, a payment schedule, a balloon payment schedule, a specificationof collateral, a specification of substitutability of collateral, aparty, a guarantee, a guarantor, a security, a personal guarantee, alien, a duration, a covenant, a foreclose condition, a defaultcondition, and a consequence of default), or managing loan-relatedactivities (such as, without limitation, finding parties interested inparticipating in a loan transaction, handling an application for a loan,underwriting a loan, forming a legal contract for a loan, monitoringperformance of a loan, making payments on a loan, restructuring oramending a loan, settling a loan, monitoring collateral for a loan,forming a syndicate for a loan, foreclosing on a loan, collecting on aloan, consolidating a set of loans, analyzing performance of a loan,handling a default of a loan, transferring title of assets orcollateral, and closing a loan transaction), and the like. Inembodiments, a system for handling a subsidized loan may includeclassifying a set of parameters of a set of subsidized loans on thebasis of data relating to those parameters obtained from an Internet ofThings data collection and monitoring service. Classifying the set ofparameters of the set of subsidized loans may also be on the bases ofdata obtained from one or more configurable data collection andmonitoring services that leverage social network analytic services,crowd sourcing services, and the like for obtaining parameter data(e.g., determination that a person or entity is qualified for thesubsidized loan, determining a social value of providing the subsidizedloan or removing a subsidization from a loan, determining that asubsidizing entity is legitimate, determining appropriate subsidizationterms based on characteristics of the buyer and/or subsidizer, etc.).

The term foreclose, foreclosure, foreclose or foreclosure condition,default foreclosure collateral, default collateral, (and similar terms)as utilized herein should be understood broadly. Without limitation toany other aspect or description of the present disclosure, foreclosecondition, default and the like describe the failure of a borrower tomeet the terms of a loan. Without limitation to any other aspect ordescription of the present disclosure foreclose and foreclosure includeprocesses by which a lender attempts to recover, from a borrower in aforeclose or default condition, the balance of a loan or take away inlieu, the right of a borrower to redeem a mortgage held in security forthe loan. Failure to meet the terms of the loan may include failure tomake specified payments, failure to adhere to a payment schedule,failure to make a balloon payment, failure to appropriately secure thecollateral, failure to sustain collateral in a specified condition (e.g.in good repair), acquisition of a second loan, and the like. Foreclosuremay include a notification to the borrower, the public, jurisdictionalauthorities of the forced sale of an item collateral such as through aforeclosure auction. Upon foreclosure, an item of collateral may beplaced on a public auction site (such as eBay, Ñ¢ or an auction siteappropriate for a particular type of property. The minimum opening bidfor the item of collateral may be set by the lender and may cover thebalance of the loan, interest on the loan, fees associated with theforeclosure and the like. Attempts to recover the balance of the loanmay include the transfer of the deed for an item of collateral in lieuof foreclosure (e.g., a real-estate mortgage where the borrower holdsthe deed for a property which acts as collateral for the mortgage loan).Foreclosure may include taking possession of or repossessing thecollateral (e.g., a car, a sports vehicle such as a boat, ATV,ski-mobile, jewelry). Foreclosure may include securing an item ofcollateral associated with the loan (such as by locking a connecteddevice, such as a smart lock, smart container, or the like that containsor secures collateral). Foreclosure may include arranging for theshipping of an item of collateral by a carrier, freight forwarder of thelike. Foreclosure may include arranging for the transport of an item ofcollateral by a drone, a robot, or the like for transporting collateral.In embodiments, a loan may allow for the substitution of collateral orthe shifting of the lien from an item of collateral initially used tosecure the loan to a substitute collateral where the substitutecollateral is of higher value (to the lender) than the initialcollateral or is an item in which the borrower has a greater equity. Theresult of the substitution of collateral is that when the loan goes intoforeclosure, it is the substitute collateral that may be the subject ofa forced sale or seizure. Certain usages of the word default may notapply to such as to foreclose but rather to a regular or defaultcondition of an item. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit from foreclosure, and/or how to combineprocesses and systems from the present disclosure to enhance or benefitfrom foreclosure. Certain considerations for the person of skill in theart, in determining whether the term foreclosure, foreclose condition,default and the like is referring to failure of a borrower to meet theterms of a loan and the related attempts by the lender to recover thebalance of the loan or obtain ownership of the collateral.

The terms validation of tile, title validation, validating title, andsimilar terms, as utilized herein should be understood broadly. Withoutlimitation to any other aspect or description of the present disclosurevalidation of title and title validation include any efforts to verifyor confirm the ownership or interest by an individual or entity in anitem of property such as a vehicle, a ship, a plane, a building, a home,real estate property, undeveloped land, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property. Efforts to verify ownership may include reference tobills of sale, government documentation of transfer of ownership, alegal will transferring ownership, documentation of retirement of lienson the item of property, verification of assignment of IntellectualProperty to the proposed borrower in the appropriate jurisdiction, andthe like. For real-estate property validation may include a review ofdeeds and records at a courthouse of a country, a state, a county, or adistrict in which a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a vehicle, a ship, a plane,or a warehouse is located or registered. Certain usages of the wordvalidation may not apply to validation of a title or title validationbut rather to confirmation that a process is operating correctly, thatan individual has been correctly identified using biometric data, thatintellectual property rights are in effect, that data is correct andmeaningful, and the like. One of skill in the art, having the benefit ofthe disclosure herein and knowledge about a contemplated systemordinarily available to that person, can readily determine which aspectsof the present disclosure will benefit from title validation, and/or howto combine processes and systems from the present disclosure to enhanceor benefit from title validation. Certain considerations for the personof skill in the art, in determining whether the term validation isreferring to title validation, are specifically contemplated within thescope of the present disclosure.

Without limitation to any other aspect or description of the presentdisclosure, validation includes any validating system including, withoutlimitation, validating title for collateral or security for a loan,validating conditions of collateral for security or a loan, validatingconditions of a guarantee for a loan, and the like. For instance, avalidation service may provide lenders a mechanism to deliver loans withmore certainty, such as through validating loan or security informationcomponents (e.g., income, employment, title, conditions for a loan,conditions of collateral, and conditions of an asset). In a non-limitingexample, a validation service circuit may be structured to validate aplurality of loan information components with respect to a financialentity configured to determine a loan condition for an asset. Certaincomponents may not be considered a validating system individually, butmay be considered validating in an aggregated system—for example, anInternet of Things component may not be considered a validatingcomponent on its own, however an Internet of Things component utilizedfor asset data collection and monitoring may be considered a validatingcomponent when applied to validating a reliability parameter of apersonal guarantee for a load when the Internet of Things component isassociated with a collateralized asset. In certain embodiments,otherwise similar looking systems may be differentiated in determiningwhether such systems are for validation. For example, a blockchain-basedledger may be used to validate identities in one instance and tomaintain confidential information in another instance. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered a system for validationherein, while in certain embodiments a given system may not beconsidered a validating system herein. One of skill in the art, havingthe benefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit a particular system,and/or how to combine processes and systems from the present disclosureto enhance operations of the contemplated system. Certain considerationsfor the person of skill in the art, in determining whether acontemplated system is a validating system and/or whether aspects of thepresent disclosure can benefit or enhance the contemplated systeminclude, without limitation: a lending platform having a social networkmonitoring system for validating the reliability of a guarantee for aloan; a lending platform having an Internet of Things data collectionand monitoring system for validating reliability of a guarantee for aloan; a lending platform having a crowdsourcing and automatedclassification system for validating conditions of an issuer for a bond;a crowdsourcing system for validating quality, title, or otherconditions of collateral for a loan; a biometric identify validationapplication such as utilizing DNA or fingerprints; IoT devices utilizedto collectively validate location and identity of a fixed asset that istagged by a virtual asset tag; validation systems utilizing voting orconsensus protocols; artificial intelligence systems trained torecognize and validate events; validating information such as titlerecords, video footage, photographs, or witnessed statements; validationrepresentations related to behavior, such as to validate occurrence ofconditions of compliance, to validate occurrence of conditions ofdefault, to deter improper behavior or misrepresentations, to reduceuncertainty, or to reduce asymmetries of information; and the like.

The term underwriting (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, underwriting includes anyunderwriting, including, without limitation, relating to underwriters,providing underwriting information for a loan, underwriting a debttransaction, underwriting a bond transaction, underwriting a subsidizedloan transaction, underwriting a securities transaction, and the like.Underwriting services may be provided by financial entities, such asbanks, insurance or investment houses, and the like, whereby thefinancial entity guarantees payment in case of a determination of a losscondition (e.g., damage or financial loss) and accept the financial riskfor liability arising from the guarantee. For instance, a bank mayunderwrite a loan through a mechanism to perform a credit analysis thatmay lead to a determination of a loan to be granted, such as throughanalysis of personal information components related to an individualborrower requesting a consumer loan (e.g., employment history, salaryand financial statements publicly available information such as theborrower's credit history), analysis of business financial informationcomponents from a company requesting a commercial load (e.g., tangiblenet worth, ratio of debt to worth (leverage), and available liquidity(current ratio)), and the like. In a non-limiting example, anunderwriting services circuit may be structured to underwrite afinancial transaction including a plurality of financial informationcomponents with respect to a financial entity configured to determine afinancial condition for an asset. In certain embodiments, underwritingcomponents may be considered underwriting for some purposes but not forother purposes—for example, an artificial intelligence system to collectand analyze transaction data may be utilized in conjunction with a smartcontract platform to monitor loan transactions, but alternately used tocollect and analyze underwriting data, such as utilizing a model trainedby human expert underwriters. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of systems, and any suchsystems may be considered underwriting herein, while in certainembodiments a given system may not be considered underwriting herein.One of skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, and/or how to combine processes andsystems from the present disclosure to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is underwritingand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation: a lending platformhaving an underwriting system for a loan with a set of data-integratedmicroservices such as including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions;underwriting processes, operations, and services; underwriting data,such as data relating to identities of prospective and actual partiesinvolved insurance and other transactions, actuarial data, data relatingto probability of occurrence and/or extent of risk associated withactivities, data relating to observed activities and other data used tounderwrite or estimate risk; an underwriting application, such as,without limitation, for underwriting any insurance offering, any loan,or any other transaction, including any application for detecting,characterizing or predicting the likelihood and/or scope of a risk, anunderwriting or inspection flow about an entity serving a lendingsolution, an analytics solution, or an asset management solution;underwriting of insurance policies, loans, warranties, or guarantees; ablockchain and smart contract platform for aggregating identity andbehavior information for insurance underwriting, such as with anoptional distributed ledger to record a set of events, transactions,activities, identities, facts, and other information associated with anunderwriting process; a crowdsourcing platform such as for underwritingof various types of loans, and guarantees; an underwriting system for aloan with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions; an underwriting solution to create,configure, modify, set or otherwise handle various rules, thresholds,conditional procedures, workflows, or model parameters; an underwritingaction or plan for management a set of loans of a given type or typesbased on one or more events, conditions, states, actions, secondaryloans or transactions to back loans, for collection, consolidation,foreclosure, situations of bankruptcy or insolvency, modifications ofexisting loans, situations involving market changes, foreclosureactivities; adaptive intelligent systems including artificialintelligent models trained on a training set of underwriting activitiesby experts and/or on outcomes of underwriting actions to generate a setof predictions, classifications, control instructions, plans, models;underwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions;and the like.

The term insuring (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, insuring includes any insuring,including, without limitation, providing insurance for a loan, providingevidence of insurance for an asset related to a loan, a first entityaccepting a risk or liability for another entity, and the like.Insuring, or insurance, may be a mechanism through which a holder of theinsurance is provided protection from a financial loss, such as in aform of risk management against the risk of a contingent or uncertainloss. The insuring mechanism may provide for an insurance, determine theneed for an insurance, determine evidence of insurance, and the like,such as related to an asset, transaction for an asset, loan for anasset, security, and the like. An entity which provides insurance may beknown as an insurer, insurance company, insurance carrier, underwriter,and the like. For instance, a mechanism for insuring may provide afinancial entity with a mechanism to determine evidence of insurance foran asset related to a loan. In a non-limiting example, an insuranceservice circuit may be structured to determine an evidence condition ofinsurance for an asset based on a plurality of insurance informationcomponents with respect to a financial entity configured to determine aloan condition for an asset. In certain embodiments, components may beconsidered insuring for some purposes but not for other purposes, forexample, a blockchain and smart contract platform may be utilized tomanage aspects of a loan transaction such as for identity andconfidentiality, but may alternately be utilized to aggregate identityand behavior information for insurance underwriting. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered insuring herein, whilein certain embodiments a given system may not be considered insuringherein. One of skill in the art, having the benefit of the disclosureherein and knowledge about a contemplated system ordinarily available tothat person, can readily determine which aspects of the presentdisclosure will benefit a particular system, and/or how to combineprocesses and systems from the present disclosure to enhance operationsof the contemplated system. Certain considerations for the person ofskill in the art, in determining whether a contemplated system isinsuring and/or whether aspects of the present disclosure can benefit orenhance the contemplated system include, without limitation: insurancefacilities such as branches, offices, storage facilities, data centers,underwriting operations and others; insurance claims, such as forbusiness interruption insurance, product liability insurance, insuranceon goods, facilities, or equipment, flood insurance, insurance forcontract-related risks, and many others, as well as claims data relatingto product liability, general liability, workers compensation, injuryand other liability claims and claims data relating to contracts, suchas supply contract performance claims, product delivery requirements,contract claims, claims for damages, claims to redeem points or rewards,claims of access rights, warranty claims, indemnification claims, energyproduction requirements, delivery requirements, timing requirements,milestones, key performance indicators and others; insurance-relatedlending; an insurance service, an insurance brokerage service, a lifeinsurance service, a health insurance service, a retirement insuranceservice, a property insurance service, a casualty insurance service, afinance and insurance service, a reinsurance service; a blockchain andsmart contract platform for aggregating identity and behaviorinformation for insurance underwriting; identities of applicants forinsurance, identities of parties that may be willing to offer insurance,information regarding risks that may be insured (of any type, withoutlimitation, such as property, life, travel, infringement, health, home,commercial liability, product liability, auto, fire, flood, casualty,retirement, unemployment; distributed ledger may be utilized tofacilitate offering and underwriting of microinsurance, such as fordefined risks related to defined activities for defined time periodsthat are narrower than for typical insurance policies; providinginsurance for a loan, providing evidence of insurance for propertyrelated to a loan; and the like.

The term aggregation (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, an aggregation or to aggregateincludes any aggregation including, without limitation, aggregatingitems together, such as aggregating or linking similar items together(e.g., collateral to provide collateral for a set of loans, collateralitems for a set of loans is aggregated in real time based on asimilarity in status of the set of items, and the like), collecting datatogether (e.g., for storage, for communication, for analysis, astraining data for a model, and the like), summarizing aggregated itemsor data into a simpler description, or any other method for creating awhole formed by combining several (e.g., disparate) elements. Further,an aggregator may be any system or platform for aggregating, such asdescribed. Certain components may not be considered aggregationindividually but may be considered aggregation in an aggregatedsystem—for example, a collection of loans may not be considered anaggregation of loans of itself but may be an aggregation if collected assuch. In a non-limiting example, an aggregation circuit may bestructured to provide lenders a mechanism to aggregate loans togetherfrom a plurality of loans, such as based on a loan attribute, parameter,term or condition, financial entity, and the like, to become anaggregation of loans. In certain embodiments, an aggregation may beconsidered an aggregation for some purposes but not for other purposes,for example, an aggregation of asset collateral conditions may becollected for the purpose of aggregating loans together in one instanceand for the purpose of determining a default action in another instance.Additionally, in certain embodiments, otherwise similar looking systemsmay be differentiated in determining whether such systems areaggregators, and/or which type of aggregating systems. For example, afirst and second aggregator may both aggregate financial entity data,where the first aggregator aggregates for the sake of building atraining set for an analysis model circuit and where the secondaggregator aggregates financial entity data for storage in ablockchain-based distributed ledger. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered as aggregation herein, while in certainembodiments a given system may not be considered aggregation herein. Oneof skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, and/or how to combine processes andsystems from the present disclosure to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is aggregationand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation forward marketdemand aggregation (e.g., blockchain and smart contract platform forforward market demand aggregation, interest expressed or committed in ademand aggregation interface, blockchain used to aggregate future demandin a forward market with respect to a variety of products and services,process a set of potential configurations having different parametersfor a subset of configurations that are consistent with each other andthe subset of configurations used to aggregate committed future demandfor the offering that satisfies a sufficiently large subset at aprofitable price, and the like); correlated aggregated data (includingtrend information) on worker ages, credentials, experience (including byprocess type) with data on the processes in which those workers areinvolved; demand for accommodations aggregated in advance andconveniently fulfilled by automatic recognition of conditions thatsatisfy pre-configured commitments represented on a blockchain (e.g.,distributed ledger); transportation offerings aggregated and fulfilled(e.g., with a wide range of pre-defined contingencies); aggregation ofgoods and services on the blockchain (e.g., a distributed ledger usedfor demand planning); with respect to a demand aggregation interface(e.g., presented to one or more consumers); aggregation of multiplesubmissions; aggregating identity and behavior information (e.g.,insurance underwriting); accumulation and aggregation of multipleparties; aggregation of data for a set of collateral; aggregated valueof collateral or assets (e.g., based on real time condition monitoring,rea-time market data collection and integration, and the like);aggregated tranches of loans; collateral for smart contract aggregatedwith other similar collateral; and the like.

The term linking (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, linking includes any linking,including, without limitation, linking as a relationship between twothings or situations (e.g., where one thing affects the other). Forinstance, linking a subset of similar items such as collateral toprovide collateral for a set of loans. Certain components may not beconsidered linked individually, but may be considered in a process oflinking in an aggregated system—for example, a smart contracts circuitmay be structured to operate in conjunction with a blockchain circuit aspart of a loan processing platform but where the smart contracts circuitprocesses contracts without storing information through the blockchaincircuit, however the two circuits could be linked through the smartcontracts circuit linking financial entity information through adistributed ledger on the blockchain circuit. In certain embodiments,linking may be considered linking for some purposes but not for otherpurposes, for example, linking goods and services for users and radiofrequency linking between access points are different forms of linking,where the linking of goods and services for users links thinks togetherwhile an RF link is a communications link between transceivers.Additionally, in certain embodiments, otherwise similar looking systemsmay be differentiated in determining whether such system are linking,and/or which type of linking. For example, linking similar data togetherfor analysis is different from linking similar data together forgraphing. Accordingly, the benefits of the present disclosure may beapplied in a wide variety of systems, and any such systems may beconsidered linking herein, while in certain embodiments a given systemmay not be considered a linking herein. One of skill in the art, havingthe benefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit a particular system,and/or how to combine processes and systems from the present disclosureto enhance operations of the contemplated system. Certain considerationsfor the person of skill in the art, in determining whether acontemplated system is linking and/or whether aspects of the presentdisclosure can benefit or enhance the contemplated system include,without limitation linking marketplaces or external marketplaces with asystem or platform; linking data (e.g., data cluster including links andnodes); storage and retrieval of data linked to local processes; links(e.g. with respect to nodes) in a common knowledge graph; data linked toproximity or location (e.g., of the asset); linking to an environment(e.g., goods, services, assets, and the like); linking events (e.g., forstorage such as in a blockchain, for communication or analysis); linkingownership or access rights; linking to access tokens (e.g., travelofferings linked to access tokens); links to one or more resources(e.g., secured by cryptographic or other techniques); linking a messageto a smart contract; and the like.

The term indicator of interest (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an indicator of interest includesany indicator of interest including, without limitation, an indicator ofinterest from a user or plurality of users or parties related to atransaction and the like (e.g., parties interested in participating in aloan transaction), the recording or storing of such an interest (e.g., acircuit for recording an interest input from a user, entity, circuit,system, and the like), a circuit analyzing interest related data andsetting an indicator of interest (e.g., a circuit setting orcommunicating an indicator based on inputs to the circuit, such as fromusers, parties, entities, systems, circuits, and the like), a modeltrained to determine an indicator of interest from input data related toan interest by one of a plurality of inputs from users, parties, orfinancial entities, and the like. Certain components may not beconsidered indicators of interest individually, but may be considered anindicator of interest in an aggregated system, for example, a party mayseek information relating to a transaction such as though a translationmarketplace where the party is interested in seeking information, butthat may not be considered an indicator of interest in a transaction.However, when the party asserts a specific interest (e.g., through auser interface with control inputs for indicating interest) the party'sinterest may be recorded (e.g., in a storage circuit, in a blockchaincircuit), analyzed (e.g., through an analysis circuit, a data collectioncircuit), monitored (e.g., through a monitoring circuit), and the like.In a non-limiting example, indicators of interest may be recorded (e.g.,in a blockchain through a distributed ledger) from a set of parties withrespect to the product, service, or the like, such as ones that defineparameters under which a party is willing to commit to purchase aproduct or service. In certain embodiments, an indicator of interest maybe considered an indicator of interest for some purposes but not forother purposes—for example, a user may indicate an interest for a loantransaction but that does not necessarily mean the user is indicating aninterest in providing a type of collateral related to the loantransaction. For instance, a data collection circuit may record anindicator of interest for the transaction but may have a separatecircuit structure for determining an indication of interest forcollateral. Additionally, in certain embodiments, otherwise similarlooking systems may be differentiated in determining whether such systemare determining an indication of interest, and/or which type ofindicator of interest exists. For example, one circuit or system maycollect data from a plurality of parties to determine an indicator ofinterest in securing a loan and a second circuit or system may collectdata from a plurality of parties to determine an indicator of interestin determining ownership rights related to a loan. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered an indicator of interestherein, while in certain embodiments a given system may not beconsidered an indicator of interest herein. One of skill in the art,having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.Certain considerations for the person of skill in the art, indetermining whether a contemplated system is an indicator of interestand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation parties indicatingan interest in participating in a transaction (e.g., a loantransaction), parties indicating an interest in securing in a product orservice, recording or storing an indication of interest (e.g., through astorage circuit or blockchain circuit), analyzing an indication ofinterest (e.g., through a data collection and/or monitoring circuit),and the like.

The term accommodations (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, an accommodation includes anyservice, activity, event, and the like such as including, withoutlimitation, a room, group of rooms, table, seating, building, event,shared spaces offered by individuals (e.g., Airbnb spaces),bed-and-breakfasts, workspaces, conference rooms, convention spaces,fitness accommodations, health and wellness accommodations, diningaccommodations, and the like, in which someone may live, stay, sit,reside, participate, and the like. As such, an accommodation may bepurchased (e.g., a ticket through a sports ticketing application),reserved or booked (e.g., a reservation through a hotel reservationapplication), provided as a reward or gift, traded or exchanged (e.g.,through a marketplace), provided as an access right (e.g., offering byway of an aggregation demand), provided based on a contingency (e.g., areservation for a room being contingent on the availability of a nearbyevent), and the like. Certain components may not be considered anaccommodation individually but may be considered an accommodation in anaggregated system—for example, a resource such as a room in a hotel maynot in itself be considered an accommodation but a reservation for theroom may be. For instance, a blockchain and smart contract platform forforward market rights for accommodations may provide a mechanism toprovide access rights with respect to accommodations. In a non-limitingexample, a blockchain circuit may be structured to store access rightsin a forward demand market, where the access rights may be stored in adistributed ledger with related shared access to a plurality ofactionable entities. In certain embodiments, an accommodation may beconsidered an accommodation for some purposes but not for otherpurposes, for example, a reservation for a room may be an accommodationon its own, but may not be accommodation that is satisfied if a relatedcontingency is not met as agreed upon at the time of the e.g.,reservation. Additionally, in certain embodiments, otherwise similarlooking systems may be differentiated in determining whether suchsystems are related to an accommodation, and/or which type ofaccommodation. For example, an accommodation offering may be made basedon different systems, such as one where the accommodation offering isdetermined by a system collecting data related to forward demand and asecond one where the accommodation offering is provided as a rewardbased on a system processing a performance parameter. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered as related to anaccommodation herein, while in certain embodiments a given system maynot be considered related to an accommodation herein. One of skill inthe art, having the benefit of the disclosure herein and knowledge abouta contemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.Certain considerations for the person of skill in the art, indetermining whether a contemplated system is related to accommodationand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation accommodationsprovided as determined through a service circuit, trading or exchangingservices (e.g., through an application and/or user interface), as anaccommodation offering such as with respect to a combination ofproducts, services, and access rights, processed (e.g., aggregationdemand for the offering in a forward market), accommodation throughbooking in advance, accommodation through booking in advance uponmeeting a certain condition (e.g., relating to a price within a giventime window), and the like.

The term contingencies (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a contingency includes anycontingency including, without limitation, any action that is dependentupon a second action. For instance, a service may be provided ascontingent on a certain parameter value, such as collecting data ascondition upon an asset tag indication from an Internet of Thingscircuit. In another instance, an accommodation such as a hotelreservation may be contingent upon a concert (local to the hotel and atthe same time as the reservation) proceeding as scheduled. Certaincomponents may not be considered as relating to a contingencyindividually, but may be considered related to a contingency in anaggregated system—for example, a data input collected from a datacollection service circuit may be stored, analyzed, processed, and thelike, and not be considered with respect to a contingency, however asmart contracts service circuit may apply a contract term as beingcontingent upon the collected data. For instance, the data may indicatea collateral status with respect to a loan transaction, and the smartcontracts service circuit may apply that data to a term of contract thatdepends upon the collateral. In certain embodiments, a contingency maybe considered contingency for some purposes but not for otherpurposes—for example, a delivery of contingent access rights for afuture event may be contingent upon a loan condition being satisfied,but the loan condition on its own may not be considered a contingency inthe absence of the contingency linkage between the condition and theaccess rights. Additionally, in certain embodiments, otherwise similarlooking systems may be differentiated in determining whether suchsystems are related to a contingency, and/or which type of contingency.For example, two algorithms may both create a forward market eventaccess right token, but where the first algorithm creates the token freeof contingencies and the second algorithm creates a token with acontingency for delivery of the token. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered a contingency herein, while in certainembodiments a given system may not be considered a contingency herein.One of skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, and/or how to combine processes andsystems from the present disclosure to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is a contingencyand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation a forward marketoperated within or by the platform may be a contingent forward market,such as one where a future right is vested, is triggered, or emergesbased on the occurrence of an event, satisfaction of a condition, or thelike; a blockchain used to make a contingent market in any form of eventor access token by securely storing access rights on a distributedledger; setting and monitoring pricing for contingent access rights,underlying access rights, tokens, fees and the like; optimizingofferings, timing, pricing, or the like, to recognize and predictpatterns, to establish rules and contingencies; exchanging contingentaccess rights or underlying access rights or tokens access tokens and/orcontingent access tokens; creating a contingent forward market eventaccess right token where a token may be created and stored on ablockchain for contingent access right that could result in theownership of a ticket; discovery and delivery of contingent accessrights to future events; contingencies that influence or representfuture demand for an offering, such as including a set of products,services, or the like; pre-defined contingencies; optimized offerings,timing, pricing, or the like, to recognize and predict patterns, toestablish rules and contingencies; creation of a contingent futureoffering within the dashboard; contingent access rights that may resultin the ownership of the virtual good or each smart contract to purchasethe virtual good if and when it becomes available under definedconditions; and the like.

The term level of service (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a level of service includes anylevel of service including, without limitation, any qualitative orquantitative measure of the extent to which a service is provided, suchas, and without limitation, a first class vs. business class service(e.g., travel reservation or postal delivery), the degree to which aresource is available (e.g., service level A indicating that theresource is highly available vs. service level C indicating that theresource is constrained, such as in terms of traffic flow restrictionson a roadway), the degree to which an operational parameter isperforming (e.g., a system is operating at a high state of service vs alow state of service, and the like. In embodiments, level of service maybe multi-modal such that the level of service is variable where a systemor circuit provides a service rating (e.g., where the service rating isused as an input to an analytical circuit for determining an outcomebased on the service rating). Certain components may not be consideredrelative to a level of service individually, but may be consideredrelative to a level of service in an aggregated system, for example asystem for monitoring a traffic flow rate may provide data on a currentrate but not indicate a level of service, but when the determinedtraffic flow rate is provided to a monitoring circuit the monitoringcircuit may compare the determined traffic flow rate to past trafficflow rates and determine a level of service based on the comparison. Incertain embodiments, a level of service may be considered a level ofservice for some purposes but not for other purposes, for example, theavailability of first class travel accommodation may be considered alevel of service for determining whether a ticket will be purchased butnot to project a future demand for the flight. Additionally, in certainembodiments, otherwise similar looking systems may be differentiated indetermining whether such system utilizes a level of service, and/orwhich type of level of service. For example, an artificial intelligencecircuit may be trained on past level of service with respect to trafficflow patterns on a certain freeway and used to predict future trafficflow patterns based on current flow rates, but a similar artificialintelligence circuit may predict future traffic flow patterns based onthe time of day. Accordingly, the benefits of the present disclosure maybe applied in a wide variety of systems, and any such systems may beconsidered with respect to levels of service herein, while in certainembodiments a given system may not be considered with respect to levelsof service herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, and/or how tocombine processes and systems from the present disclosure to enhanceoperations of the contemplated system. Certain considerations for theperson of skill in the art, in determining whether a contemplated systemis a level of service and/or whether aspects of the present disclosurecan benefit or enhance the contemplated system include, withoutlimitation transportation or accommodation offerings with predefinedcontingencies and parameters such as with respect to price, mode ofservice, and level of service; warranty or guarantee application,transportation marketplace, and the like.

The term payment (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a payment includes any paymentincluding, without limitation, an action or process of paying (e.g., apayment to a loan) or of being paid (e.g., a payment from insurance), anamount paid or payable (e.g., a payment of $1000 being made), arepayment (e.g., to pay back a loan), a mode of payment (e.g., use ofloyalty programs, rewards points, or particular currencies, includingcryptocurrencies) and the like. Certain components may not be consideredpayments individually, but may be considered payments in an aggregatedsystem—for example, submitting an amount of money may not be considereda payment as such, but when applied to a payment to satisfy therequirement of a loan may be considered a payment (or repayment). Forinstance, a data collection circuit may provide lenders a mechanism tomonitor repayments of a loan. In a non-limiting example, the datacollection circuit may be structured to monitor the payments of aplurality of loan components with respect to a financial loan contractconfigured to determine a loan condition for an asset. In certainembodiments, a payment may be considered a payment for some purposes butnot for other purposes—for example, a payment to a financial entity maybe for a repayment amount to pay back the loan, or it may be to satisfya collateral obligation in a loan default condition. Additionally, incertain embodiments, otherwise similar looking systems may bedifferentiated in determining whether such system are related to apayment, and/or which type of payment. For example, funds may be appliedto reserve an accommodation or to satisfy the delivery of services afterthe accommodation has been satisfied. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered a payment herein, while in certainembodiments a given system may not be considered a payment herein. Oneof skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, and/or how to combine processes andsystems from the present disclosure to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is a paymentand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation, deferring arequired payment; deferring a payment requirement; payment of a loan; apayment amount; a payment schedule; a balloon payment schedule; paymentperformance and satisfaction; modes of payment; and the like.

The term location (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a location includes any locationincluding, without limitation, a particular place or position of aperson, place, or item, or location information regarding the positionof a person, place, or item, such as a geolocation (e.g., geolocation ofa collateral), a storage location (e.g., the storage location of anasset), a location of a person (e.g., lender, borrower, worker),location information with respect to the same, and the like. Certaincomponents may not be considered with respect to location individually,but may be considered with respect to location in an aggregated system,for example, a smart contract circuit may be structured to specify arequirement for a collateral to be stored at a fixed location but notspecify the specific location for a specific collateral. In certainembodiments, a location may be considered a location for some purposesbut not for other purposes—for example, the address location of aborrower may be required for processing a loan in one instance, and aspecific location for processing a default condition in anotherinstance. Additionally, in certain embodiments, otherwise similarlooking systems may be differentiated in determining whether such systemare a location, and/or which type of location. For example, the locationof a music concert may be required to be in a concert hall seating10,000 people in one instance but specify the location of an actualconcert hall in another. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of systems, and any suchsystems may be considered with respect to a location herein, while incertain embodiments a given system may not be considered with respect toa location herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, and/or how tocombine processes and systems from the present disclosure to enhanceoperations of the contemplated system. Certain considerations for theperson of skill in the art, in determining whether a contemplated systemis considered with respect to a location and/or whether aspects of thepresent disclosure can benefit or enhance the contemplated systeminclude, without limitation a geolocation of an item or collateral; astorage location of item or asset; location information; location of alender or a borrower; location-based product or service targetingapplication; a location-based fraud detection application; indoorlocation monitoring systems (e.g., cameras, IR systems, motion-detectionsystems); locations of workers (including routes taken through alocation); location parameters; event location; specific location of anevent; and the like.

The term route (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a route includes any routeincluding, without limitation, a way or course taken in getting from astarting point to a destination, to send or direct along a specifiedcourse, and the like. Certain components may not be considered withrespect to a route individually, but may be considered a route in anaggregated system—for example, a mobile data collector may specify arequirement for a route for collecting data based on an input from amonitoring circuit, but only in receiving that input does the mobiledata collector determine what route to take and begin traveling alongthe route. In certain embodiments, a route may be considered a route forsome purposes but not for other purposes—for example possible routesthrough a road system may be considered differently than specific routestaken through from one location to another location. Additionally, incertain embodiments, otherwise similar looking systems may bedifferentiated in determining whether such systems are specified withrespect to a location, and/or which types of locations. For example,routes depicted on a map may indicate possible routes or actual routestaken by individuals. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of systems, and any suchsystems may be considered with respect to a route herein, while incertain embodiments a given system may not be considered with respect toa route herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, and/or how tocombine processes and systems from the present disclosure to enhanceoperations of the contemplated system. Certain considerations for theperson of skill in the art, in determining whether a contemplated systemis utilizing a route and/or whether aspects of the present disclosurecan benefit or enhance the contemplated system include, withoutlimitation delivery routes; routes taken through a location; heat mapshowing routes traveled by customers or workers within an environment;determining what resources are deployed to what routes or types oftravel; direct route or multi-stop route, such as from the destinationof the consumer to a specific location or to wherever an event takesplace; a route for a mobile data collector; and the like.

The term future offering (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a future offing includes anyoffer of an item or service in the future including, without limitation,a future offer to provide an item or service, a future offer withrespect to a proposed purchase, a future offering made through a forwardmarket platform, a future offering determined by a smart contractcircuit, and the like. Further, a future offering may be a contingentfuture offer, or an offer based on conditions resulting on the offerbeing a future offering, such as where the future offer is contingentupon or with the conditions imposed by a predetermined condition (e.g.,a security may be purchased for $1000 at a set future date contingentupon a predetermined state of a market indicator). Certain componentsmay not be considered a future offering individually, but may beconsidered a future offering in an aggregated system—for example, anoffer for a loan may not be considered a future offering if the offer isnot authorized through a collective agreement amongst a plurality ofparties related to the offer, but may be considered a future offer oncea vote has been collected and stored through a distributed ledger, suchas through a blockchain circuit. In certain embodiments, a futureoffering may be considered a future offering for some purposes but notfor other purposes—for example, a future offering may be contingent upona condition being met in the future, and so the future offering may notbe considered a future offer until the condition is met. Additionally,in certain embodiments, otherwise similar looking systems may bedifferentiated in determining whether such systems are future offerings,and/or which type of future offerings. For example, two securityofferings may be determined to be offerings to be made at a future time,however, one may have immediate contingences to be met and thus may notbe considered to be a future offering but rather an immediate offeringwith future declarations. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of systems, and any suchsystems may be considered in association with a future offering herein,while in certain embodiments a given system may not be considered inassociation with a future offering herein. One of skill in the art,having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.Certain considerations for the person of skill in the art, indetermining whether a contemplated system is in association with afuture offering and/or whether aspects of the present disclosure canbenefit or enhance the contemplated system include, without limitation aforward offering, a contingent forward offering, a forward offing in aforward market platform (e.g., for creating a future offering orcontingent future offering associated with identifying offering datafrom a platform-operated marketplace or external marketplace); a futureoffering with respect to entering into a smart contract (e.g., byexecuting an indication of a commitment to purchase, attend, orotherwise consume a future offering), and the like.

The term access right (and derivatives or variations) as utilized hereinmay be understood broadly to describe an entitlement to acquire orpossess a property, article, or other thing of value. A contingentaccess right may be conditioned upon a trigger or condition being metbefore such an access right becomes entitled, vested or otherwisedefensible. An access right or contingent access right may also servespecific purposes or be configured for different applications orcontexts, such as, without limitation, loan-related actions or anyservice or offering. Without limitation, notices may be required to beprovided to the owner of a property, article, or item of value beforesuch access rights or contingent access rights are exercised. Accessrights and contingent access rights in various forms may be includedwhere discussing a legal action, a delinquent or defaulted loan oragreement, or other circumstances where a lender may be seeking remedy,without limitation. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the value of such rightsimplemented in an embodiment. While specific examples of access rightsand contingent access rights are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term smart contract (and other forms or variations) as utilizedherein may be understood broadly to describe a method, system, connectedresource or wide area network offering one or more resources useful toassist or perform actions, tasks or things by embodiments disclosedherein. A smart contract may be a set of steps or a process tonegotiate, administrate, restructure, or implement an agreement or loanbetween parties. A smart contract may also be implemented as anapplication, website, FTP site, server, appliance or other connectedcomponent or Internet related system that renders resources tonegotiate, administrate, restructure, or implement an agreement or loanbetween parties. A smart contract may be a self-contained system, or maybe part of a larger system or component that may also be a smartcontract. For example, a smart contract may refer to a loan or anagreement itself, conditions or terms, or may refer to a system toimplement such a loan or agreement. In certain embodiments, a smartcontract circuit or robotic process automation system may incorporate orbe incorporated into automatic robotic process automation system toperform one or more purposes or tasks, whether part of a loan ortransaction process, or otherwise. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system can readily determine the purposes and useof this term as it relates to a smart contract in various forms,embodiments and contexts disclosed herein.

The term allocation of reward (and variations) as utilized herein may beunderstood broadly to describe a thing or consideration allocated orprovided as consideration, or provided for a purpose. The allocation ofrewards can be of a financial type, or non-financial type, withoutlimitation. A specific type of allocation of reward may also serve anumber of different purposes or be configured for different applicationsor contexts, such as, without limitation: a reward event, claims forrewards, monetary rewards, rewards captured as a data set, rewardspoints, and other forms of rewards. Thus an allocation of rewards may beprovided as a consideration within the context of a loan or agreement.Systems may be utilized to allocate rewards. The allocation of rewardsin various forms may be included where discussing a particular behavior,or encouragement of a particular behavior, without limitation. Anallocation of a reward may include an actual dispensation of the award,and/or a recordation of the reward. The allocation of rewards may beperformed by a smart contract circuit or a robotic processing automationsystem. One of skill in the art, having the benefit of the disclosureherein and knowledge ordinarily available about a contemplated system,can readily determine the value of the allocation of rewards in anembodiment. While specific examples of the allocation of rewards aredescribed herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term satisfaction of parameters or conditions (and otherderivatives, forms, or variations) as utilized herein may be understoodbroadly to describe completion, presence or proof of parameters orconditions that have been met. The term generally may relate to aprocess of determining such satisfaction of parameters or conditions, ormay relate to the completion of such a process with a result, withoutlimitation. Satisfaction may result in a successful outcome of othertriggers or conditions or terms that may come into execution, withoutlimitation. Satisfaction of parameters or conditions may occur in manydifferent contexts of contracts or loans, such as lending, refinancing,consolidation, factoring, brokering, foreclosure, and data processing(e.g., data collection), or combinations thereof, without limitation.Satisfaction of parameters or conditions may be used in the form of anoun (e.g., the satisfaction of the debt repayment), or may be used in averb form to describe the process of determining a result to parametersor conditions. For example, a borrower may have satisfaction ofparameters by making a certain number of payments on time, or may causesatisfaction of a condition allowing access rights to an owner if a loandefaults, without limitation. In certain embodiments, a smart contractor robotic process automation system may perform or determinesatisfaction of parameters or conditions for one or more of the partiesand process appropriate tasks for satisfaction of parameters orconditions. In some cases satisfaction of parameters or conditions bythe smart contract or robotic process automation system may not completeor be successful, and depending upon such outcomes, this may enableautomated action or trigger other conditions or terms. One of skill inthe art, having the benefit of the disclosure herein and knowledgeordinarily available about a contemplated system can readily determinethe purposes and use of this term in various forms, embodiments andcontexts disclosed herein.

The term information (and other forms such as info or informational,without limitation) as utilized herein may be understood broadly in avariety of contexts with respect to an agreement or a loan. The termgenerally may relate to a large context, such as information regardingan agreement or loan, or may specifically relate to a finite piece ofinformation (e.g., a specific detail of an event that happened on aspecific date). Thus, information may occur in many different contextsof contracts or loans, and may be used in the contexts, withoutlimitation of evidence, transactions, access, and the like. Or, withoutlimitation, information may be used in conjunction with stages of anagreement or transaction, such as lending, refinancing, consolidation,factoring, brokering, foreclosure, and information processing (e.g.,data or information collection), or combinations thereof. For example,information as evidence, transaction, access, etc. may be used in theform of a noun (e.g., the information was acquired from the borrower),or may refer as a noun to an assortment of informational items (e.g.,the information about the loan may be found in the smart contract), ormay be used in the form of characterizing as an adjective (e.g., theborrower was providing an information submission). For example, a lendermay collect an overdue payment from a borrower through an onlinepayment, or may have a successful collection of overdue paymentsacquired through a customer service telephone call. In certainembodiments, a smart contract circuit or robotic process automationsystem may perform collection, administration, calculating, providing,or other tasks for one or more of the parties and process appropriatetasks relating to information (e.g., providing notice of an overduepayment). In some cases information by the smart contract circuit orrobotic process automation system may be incomplete, and depending uponsuch outcomes this may enable automated action or trigger otherconditions or terms. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system can readily determine the purposes and use ofinformation as evidence, transaction, access, etc. in various forms,embodiments and contexts disclosed herein.

Information may be linked to external information (e.g., externalsources). The term more specifically may relate to the acquisition,parsing, receiving, or other relation to an external origin or source,without limitation. Thus, information linked to external information orsources may be used in conjunction with stages of an agreement ortransaction, such as lending, refinancing, consolidation, factoring,brokering, foreclosure, and information processing (e.g., data orinformation collection), or combinations thereof. For example,information linked to external information may change as the externalinformation changes, such as a borrower's credit score, which is basedon an external source. In certain embodiments, a smart contract circuitor robotic process automation system may perform acquisition,administration, calculating, receiving, updating, providing or othertasks for one or more of the parties and process appropriate tasksrelating to information that is linked to external information. In somecases information that is linked to external information by the smartcontract or robotic process automation system may be incomplete, anddepending upon such outcomes this may enable automated action or triggerother conditions or terms. One of skill in the art, having the benefitof the disclosure herein and knowledge ordinarily available about acontemplated system can readily determine the purposes and use of thisterm in various forms, embodiments and contexts disclosed herein.

Information that is a part of a loan or agreement may be separated frominformation presented in an access location. The term more specificallymay relate to the characterization that information can be apportioned,split, restricted, or otherwise separated from other information withinthe context of a loan or agreement. Thus, information presented orreceived on an access location may not necessarily be the wholeinformation available for a given context. For example, informationprovided to a borrower may be different information received by a lenderfrom an external source, and may be different than information receivedor presented from an access location. In certain embodiments, a smartcontract circuit or robotic process automation system may performseparation of information or other tasks for one or more of the partiesand process appropriate tasks. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system, can readily determine the purposes and useof this term in various forms, embodiments and contexts disclosedherein.

The term encryption of information and control of access (and otherrelated terms) as utilized herein may be understood broadly to describegenerally whether a party or parties may observe or possess certaininformation, actions, events, or activities relating to a transaction orloan. Encryption of information may be utilized to prevent a party fromaccessing, observing, or receiving information, or may alternatively beused to prevent parties outside the transaction or loan from being ableto access, observe or receive confidential (or other) information.Control of access to information relates to the determination of whethera party is entitled to such access of information. Encryption ofinformation or control of access may occur in many different contexts ofloans, such as lending, refinancing, consolidation, factoring,brokering, foreclosure, administration, negotiating, collecting,procuring, enforcing, and data processing (e.g., data collection), orcombinations thereof, without limitation. An encryption of informationor control of access to information may refer to a single instance, ormay characterize a larger amount of information, actions, events, oractivities, without limitation. For example, a borrower or lender mayhave access to information about a loan, but other parties outside theloan or agreement may not be able to access the loan information due toencryption of the information, or a control of access to the loandetails. In certain embodiments, a smart contract circuit or roboticprocess automation system may perform encryption of information orcontrol of access to information for one or more of the parties andprocess appropriate tasks for encryption or control of access ofinformation. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system can readily determine the purposes and use of thisterm in various forms, embodiments and contexts disclosed herein.

The term potential access party list (and other related terms) asutilized herein may be understood broadly to describe generally whethera party or parties may observe or possess certain information, actions,events, or activities relating to a transaction or loan. A potentialaccess party list may be utilized to authorize one or more parties toaccess, observe or receive information, or may alternatively be used toprevent parties from being able to do so. A potential access party listinformation relates to the determination of whether a party (either onthe potential access party list or not on the list) is entitled to suchaccess of information. A potential access party list may occur in manydifferent contexts of loans, such as lending, refinancing,consolidation, factoring, brokering, foreclosure, administration,negotiating, collecting, procuring, enforcing and data processing (e.g.,data collection), or combinations thereof, without limitation. Apotential access party list may refer to a single instance, or maycharacterize a larger amount of parties or information, actions, events,or activities, without limitation. For example, a potential access partylist may grant (or deny) access to information about a loan, but otherparties outside potential access party list may not be able to (or maybe granted) access the loan information. In certain embodiments, a smartcontract circuit or robotic process automation system may performadministration or enforcement of a potential access party list for oneor more of the parties and process appropriate tasks for encryption orcontrol of access of information. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system can readily determine the purposes and useof this term in various forms, embodiments and contexts disclosedherein.

The term offering, making an offer, and similar terms as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an offering includes any offer ofan item or service including, without limitation, an insurance offering,a security offering, an offer to provide an item or service, an offerwith respect to a proposed purchase, an offering made through a forwardmarket platform, a future offering, a contingent offering, offersrelated to lending (e.g., lending, refinancing, collection,consolidation, factoring, brokering, foreclosure), an offeringdetermined by a smart contract circuit, an offer directed to acustomer/debtor, an offering directed to a provider/lender, a 3rd partyoffer (e.g., regulator, auditor, partial owner, tiered provider) and thelike. Offerings may include physical goods, virtual goods, software,physical services, access rights, entertainment content, accommodations,or many other items, services, solutions, or considerations. In anexample, a third party offer may be to schedule a band instead of justan offer of tickets for sale. Further, an offer may be based onpre-determined conditions or contingencies. Certain components may notbe considered an offering individually, but may be considered anoffering in an aggregated system—for example, an offer for insurance maynot be considered an offering if the offer is not approved by one ormore parties related to the offer, however once approval has beengranted, it may be considered an offer. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered in association with an offering herein,while in certain embodiments a given system may not be considered inassociation with an offering herein. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit a particular system,and/or how to combine processes and systems from the present disclosureto enhance operations of the contemplated system. Certain considerationsfor the person of skill in the art, in determining whether acontemplated system is in association with an offering and/or whetheraspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation the item or servicebeing offered, a contingency related to the offer, a way of tracking ifa contingency or condition has been met, an approval of the offering, anexecution of an exchange of consideration for the offering, and thelike.

The term artificial intelligence (AI) solution should be understoodbroadly. Without limitation to any other aspect of the presentdisclosure, an AI solution includes a coordinated group of AI relatedaspects to perform one or more tasks or operations as set forththroughout the present disclosure. An example AI solution includes oneor more AI components, including any AI components set forth herein,including at least a neural network, an expert system, and/or a machinelearning component. The example AI solution may include as an aspect thetypes of components of the solution, such as a heuristic AI component, amodel based AI component, a neural network of a selected type (e.g.,recursive, convolutional, perceptron, etc.), and/or an AI component ofany type having a selected processing capability (e.g., signalprocessing, frequency component analysis, auditory processing, visualprocessing, speech processing, text recognition, etc.). Withoutlimitation to any other aspect of the present disclosure, a given AIsolution may be formed from the number and type of AI components of theAI solution, the connectivity of the AI components (e.g., to each other,to inputs from a system including or interacting with the AI solution,and/or to outputs to the system including or interacting with the AIsolution). The given AI solution may additionally be formed from theconnection of the AI components to each other within the AI solution,and to boundary elements (e.g., inputs, outputs, stored intermediarydata, etc.) in communication with the AI solution. The given AI solutionmay additionally be formed from a configuration of each of the AIcomponents of the AI solution, where the configuration may includeaspects such as: model calibrations for an AI component; connectivityand/or flow between AI components (e.g., serial and/or parallelcoupling, feedback loops, logic junctions, etc.); the number, selectedinput data, and/or input data processing of inputs to an AI component; adepth and/or complexity of a neural network or other components; atraining data description of an AI component (e.g., training dataparameters such as content, amount of training data, statisticaldescription of valid training data, etc.); and/or a selection and/orhybrid description of a type for an AI component. An AI solutionincludes a selection of AI elements, flow connectivity of those AIelements, and/or configuration of those AI elements.

One of skill in the art, having the benefit of the present disclosure,can readily determine an AI solution for a given system, and/orconfigure operations to perform a selection and/or configurationoperation for an AI solution for a given system. Certain considerationsto determining an AI solution, and/or configuring operations to performa selection and/or configuration operation for an AI solution include,without limitation: an availability of AI components and/or componenttypes for a given implementation; an availability of supportinginfrastructure to implement given AI components (e.g., data input valuesavailable, including data quality, level of service, resolution,sampling rate, etc.; availability of suitable training data for a givenAI solution; availability of expert inputs, such as for an expert systemand/or to develop a model training data set; regulatory and/or policybased considerations including permitted action by the AI solution,requirements for acquisition and/or retention of sensitive data,difficult to obtain data, and/or expensive data); operationalconsiderations for a system including or interacting with the AIsolution, including response time specifications, safety considerations,liability considerations, etc.; available computing resources such asprocessing capability, network communication capability, and/or memorystorage capability (e.g., to support initial data, training data, inputdata such as cached, buffered, or stored input data, iterativeimprovement state data, output data such as cached, buffered, or storedoutput data, and/or intermediate data storage, such as data to supportongoing calculations, historical data, and/or accumulation data); thetypes of tasks to be performed by the AI solution, and the suitabilityof AI components for those tasks, sensitivity of AI componentsperforming the tasks (e.g., variability of the output space relative toa disturbance size of the input space); the interactions of AIcomponents within the entire AI solution (e.g., a low capabilityrationality AI component may be coupled with a higher capability AIcomponent that may provide high sensitivity and/or unbounded response toinputs); and/or model implementation considerations (e.g., requirementsto re-calibrate, aging constraints of a model, etc.).

A selected and/or configured AI solution may be utilized with any of thesystems, procedures, and/or aspects of embodiments as set forththroughout the present disclosure. For example, a system utilizing anexpert system may include the expert system as all or a part of aselected, configured AI solution. In another example, a system utilizinga neural network, and/or a combination of neural networks, may includethe neural network(s) as all or a part of a selected, configured AIsolution. The described aspects of an AI solution, including theselection and configuration of the AI solution, are non-limitingillustrations.

Transaction Platform

Referring to FIGS. 1, 2A and 2B, a set of systems, methods, components,modules, machines, articles, blocks, circuits, services, programs,applications, hardware, software and other elements are provided,collectively referred to herein interchangeably as the system 100 or theplatform 100, The platform 100 enables a wide range of improvements ofand for various machines, systems, and other components that enabletransactions involving the exchange of value (such as using currency,cryptocurrency, tokens, rewards or the like, as well as a wide range ofin-kind and other resources) in various markets, including current orspot markets 170, forward markets 130 and the like, for various goods,services, and resources. As used herein, “currency” should be understoodto encompass fiat currency issued or regulated by governments,cryptocurrencies, tokens of value, tickets, loyalty points, rewardspoints, coupons, and other elements that represent or may be exchangedfor value. Resources, such as ones that may be exchanged for value in amarketplace, should be understood to encompass goods, services, naturalresources, energy resources, computing resources, energy storageresources, data storage resources, network bandwidth resources,processing resources and the like, including resources for which valueis exchanged and resources that enable a transaction to occur (such asnecessary computing and processing resources, storage resources, networkresources, and energy resources that enable a transaction). The platform100 may include a set of forward purchase and sale machines 110, each ofwhich may be configured as an expert system or automated intelligentagent for interaction with one or more of the set of spot markets 170and forward markets 130. Enabling the set of forward purchase and salemachines 110 are an intelligent resource purchasing system 164 having aset of intelligent agents for purchasing resources in spot and forwardmarkets; an intelligent resource allocation and coordination system 168for the intelligent sale of allocated or coordinated resources, such ascompute resources, energy resources, and other resources involved in orenabling a transaction; an intelligent sale engine 172 for intelligentcoordination of a sale of allocated resources in spot and futuresmarkets; and an automated spot market testing and arbitrage transactionexecution engine 194 for performing spot testing of spot and forwardmarkets, such as with micro-transactions and, where conditions indicatefavorable arbitrage conditions, automatically executing transactions inresources that take advantage of the favorable conditions. Each of theengines may use model-based or rule-based expert systems, such as basedon rules or heuristics, as well as deep learning systems by which rulesor heuristics may be learned over trials involving a large set ofinputs. The engines may use any of the expert systems and artificialintelligence capabilities described throughout this disclosure.Interactions within the platform 100, including of all platformcomponents, and of interactions among them and with various markets, maybe tracked and collected, such as by a data aggregation system 144, suchas for aggregating data on purchases and sales in various marketplacesby the set of machines described herein. Aggregated data may includetracking and outcome data that may be fed to artificial intelligence andmachine learning systems, such as to train or supervise the same. Thevarious engines may operate on a range of data sources, includingaggregated data from marketplace transactions, tracking data regardingthe behavior of each of the engines, and a set of external data sources182, which may include social media data sources 180 (such as socialnetworking sites like Facebook™ and Twitter™), Internet of Things (IoT)data sources (including from sensors, cameras, data collectors, andinstrumented machines and systems), such as IoT sources that provideinformation about machines and systems that enable transactions andmachines and systems that are involved in production and consumption ofresources. External data sources 182 may include behavioral datasources, such as automated agent behavioral data sources 188 (such astracking and reporting on behavior of automated agents that are used forconversation and dialog management, agents used for control functionsfor machines and systems, agents used for purchasing and sales, agentsused for data collection, agents used for advertising, and others),human behavioral data sources (such as data sources tracking onlinebehavior, mobility behavior, energy consumption behavior, energyproduction behavior, network utilization behavior, compute andprocessing behavior, resource consumption behavior, resource productionbehavior, purchasing behavior, attention behavior, social behavior, andothers), and entity behavioral data sources 190 (such as behavior ofbusiness organizations and other entities, such as purchasing behavior,consumption behavior, production behavior, market activity, merger andacquisition behavior, transaction behavior, location behavior, andothers). The IoT, social and behavioral data from and about sensors,machines, humans, entities, and automated agents may collectively beused to populate expert systems, machine learning systems, and otherintelligent systems and engines described throughout this disclosure,such as being provided as inputs to deep learning systems and beingprovided as feedback or outcomes for purposes of training, supervision,and iterative improvement of systems for prediction, forecasting,classification, automation and control. The data may be organized as astream of events. The data may be stored in a distributed ledger orother distributed system. The data may be stored in a knowledge graphwhere nodes represent entities and links represent relationships. Theexternal data sources may be queried via various database queryfunctions. The external data sources 182 may be accessed via APIs,brokers, connectors, protocols like REST and SOAP, and other dataingestion and extraction techniques. Data may be enriched with metadataand may be subject to transformation and loading into suitable forms forconsumption by the engines, such as by cleansing, normalization,de-duplication, and the like.

The platform 100 may include a set of intelligent forecasting engines192 for forecasting events, activities, variables, and parameters ofspot markets 170, forward markets 130, resources that are traded in suchmarkets, resources that enable such markets, behaviors (such as any ofthose tracked in the external data sources 182), transactions, and thelike. The intelligent forecasting engines 192 may operate on data fromthe data aggregation systems 144 about elements of the platform 100 andon data from the external data sources 182. The platform may include aset of intelligent transaction engines 136 for automatically executingtransactions in spot markets 170 and forward markets 130. This mayinclude executing intelligent cryptocurrency transactions with anintelligent cryptocurrency execution engine 183 as described in moredetail below. The platform 100 may make use of asset of improveddistributed ledgers 113 and improved smart contracts 103, including onesthat embed and operate on proprietary information, instruction sets andthe like that enable complex transactions to occur among individualswith reduced (or without) reliance on intermediaries. These and othercomponents are described in more detail throughout this disclosure.

Referring to the block diagrams of FIGS. 2A and 2B, further details andadditional components of the platform 100 and interactions among themare depicted. The set of forward purchase and sale machines 110 mayinclude a regeneration capacity allocation engine 102 (such as forallocating energy generation or regeneration capacity, such as within ahybrid vehicle or system that includes energy generation or regenerationcapacity, a renewable energy system that has energy storage, or otherenergy storage system, where energy is allocated for one or more of saleon a forward market 130, sale in a spot market 170, use in completing atransaction (e.g., mining for cryptocurrency), or other purposes. Forexample, the regeneration capacity allocation engine 102 may exploreavailable options for use of stored energy, such as sale in current andforward energy markets that accept energy from producers, keeping theenergy in storage for future use, or using the energy for work (whichmay include processing work, such as processing activities of theplatform like data collection or processing, or processing work forexecuting transactions, including mining activities forcryptocurrencies).

The set of forward purchase and sale machines 110 may include an energypurchase and sale machine 104 for purchasing or selling energy, such asin an energy spot market 148 or an energy forward market 122. The energypurchase and sale machine 104 may use an expert system, neural networkor other intelligence to determine timing of purchases, such as based oncurrent and anticipated state information with respect to pricing andavailability of energy and based on current and anticipated stateinformation with respect to needs for energy, including needs for energyto perform computing tasks, cryptocurrency mining, data collectionactions, and other work, such as work done by automated agents andsystems and work required for humans or entities based on theirbehavior. For example, the energy purchase machine may recognize, bymachine learning, that a business is likely to require a block of energyin order to perform an increased level of manufacturing based on anincrease in orders or market demand and may purchase the energy at afavorable price on a futures market, based on a combination of energymarket data and entity behavioral data. Continuing the example, marketdemand may be understood by machine learning, such as by processinghuman behavioral data sources 184, such as social media posts,e-commerce data and the like that indicate increasing demand. The energypurchase and sale machine 104 may sell energy in the energy spot market148 or the energy forward market 122. Sale may also be conducted by anexpert system operating on the various data sources described herein,including with training on outcomes and human supervision.

The set of forward purchase and sale machines 110 may include arenewable energy credit (REC) purchase and sale machine 108, which maypurchase renewable energy credits, pollution credits, and otherenvironmental or regulatory credits in a spot market 150 or forwardmarket 124 for such credits. Purchasing may be configured and managed byan expert system operating on any of the external data sources 182 or ondata aggregated by the set of data aggregation systems 144 for theplatform. Renewable energy credits and other credits may be purchased byan automated system using an expert system, including machine learningor other artificial intelligence, such as where credits are purchasedwith favorable timing based on an understanding of supply and demandthat is determined by processing inputs from the data sources. Theexpert system may be trained on a data set of outcomes from purchasesunder historical input conditions. The expert system may be trained on adata set of human purchase decisions and/or may be supervised by one ormore human operators. The renewable energy credit (REC) purchase andsale machine 108 may also sell renewable energy credits, pollutioncredits, and other environmental or regulatory credits in a spot market150 or forward market 124 for such credits. Sale may also be conductedby an expert system operating on the various data sources describedherein, including with training on outcomes and human supervision.

The set of forward purchase and sale machines 110 may include anattention purchase and sale machine 112, which may purchase one or moreattention-related resources, such as advertising space, search listing,keyword listing, banner advertisements, participation in a panel orsurvey activity, participation in a trial or pilot, or the like in aspot market for attention 152 or a forward market for attention 128.Attention resources may include the attention of automated agents, suchas bots, crawlers, dialog managers, and the like that are used forsearching, shopping, and purchasing. Purchasing of attention resourcesmay be configured and managed by an expert system operating on any ofthe external data sources 182 or on data aggregated by the set of dataaggregation systems 144 for the platform. Attention resources may bepurchased by an automated system using an expert system, includingmachine learning or other artificial intelligence, such as whereresources are purchased with favorable timing, such as based on anunderstanding of supply and demand, that is determined by processinginputs from the various data sources. For example, the attentionpurchase and sale machine 112 may purchase advertising space in aforward market for advertising based on learning from a wide range ofinputs about market conditions, behavior data, and data regardingactivities of agent and systems within the platform 100. The expertsystem may be trained on a data set of outcomes from purchases underhistorical input conditions. The expert system may be trained on a dataset of human purchase decisions and/or may be supervised by one or morehuman operators. The attention purchase and sale machine 112 may alsosell one or more attention-related resources, such as advertising space,search listing, keyword listing, banner advertisements, participation ina panel or survey activity, participation in a trial or pilot, or thelike in a spot market for attention 152 or a forward market forattention 128, which may include offering or selling access to, orattention or, one or more automated agents of the platform 100. Sale mayalso be conducted by an expert system operating on the various datasources described herein, including with training on outcomes and humansupervision.

The set of forward purchase and sale machines 110 may include a computepurchase and sale machine 114, which may purchase one or morecomputation-related resources, such as processing resources, databaseresources, computation resources, server resources, disk resources,input/output resources, temporary storage resources, memory resources,virtual machine resources, container resources, and others in a spotmarket for compute 154 or a forward market for compute 132. Purchasingof compute resources may be configured and managed by an expert systemoperating on any of the external data sources 182 or on data aggregatedby the set of data aggregation systems 144 for the platform. Computeresources may be purchased by an automated system using an expertsystem, including machine learning or other artificial intelligence,such as where resources are purchased with favorable timing, such asbased on an understanding of supply and demand, that is determined byprocessing inputs from the various data sources. For example, thecompute purchase and sale machine 114 may purchase or reserve computeresources on a cloud platform in a forward market for compute resourcesbased on learning from a wide range of inputs about market conditions,behavior data, and data regarding activities of agent and systems withinthe platform 100, such as to obtain such resources at favorable pricesduring surge periods of demand for computing. The expert system may betrained on a data set of outcomes from purchases under historical inputconditions. The expert system may be trained on a data set of humanpurchase decisions and/or may be supervised by one or more humanoperators. The compute purchase and sale machine 114 may also sell oneor more computation-related resources that are connected to, part of, ormanaged by the platform 100, such as processing resources, databaseresources, computation resources, server resources, disk resources,input/output resources, temporary storage resources, memory resources,virtual machine resources, container resources, and others in a spotmarket for compute 154 or a forward market for compute 132. Sale mayalso be conducted by an expert system operating on the various datasources described herein, including with training on outcomes and humansupervision.

The set of forward purchase and sale machines 110 may include a datastorage purchase and sale machine 118, which may purchase one or moredata-related resources, such as database resources, disk resources,server resources, memory resources, RAM resources, network attachedstorage resources, storage attached network (SAN) resources, taperesources, time-based data access resources, virtual machine resources,container resources, and others in a spot market for storage resources158 or a forward market for data storage 134. Purchasing of data storageresources may be configured and managed by an expert system operating onany of the external data sources 182 or on data aggregated by the set ofdata aggregation systems 144 for the platform. Data storage resourcesmay be purchased by an automated system using an expert system,including machine learning or other artificial intelligence, such aswhere resources are purchased with favorable timing, such as based on anunderstanding of supply and demand, that is determined by processinginputs from the various data sources. For example, the compute purchaseand sale machine 114 may purchase or reserve compute resources on acloud platform in a forward market for compute resources based onlearning from a wide range of inputs about market conditions, behaviordata, and data regarding activities of agent and systems within theplatform 100, such as to obtain such resources at favorable pricesduring surge periods of demand for storage. The expert system may betrained on a data set of outcomes from purchases under historical inputconditions. The expert system may be trained on a data set of humanpurchase decisions and/or may be supervised by one or more humanoperators. The data storage purchase and sale machine 118 may also sellone or more data storage-related resources that are connected to, partof, or managed by the platform 100 in a spot market for storageresources 158 or a forward market for data storage 134. Sale may also beconducted by an expert system operating on the various data sourcesdescribed herein, including with training on outcomes and humansupervision.

The set of forward purchase and sale machines 110 may include abandwidth purchase and sale machine 120, which may purchase one or morebandwidth-related resources, such as cellular bandwidth, Wi-Fibandwidth, radio bandwidth, access point bandwidth, beacon bandwidth,local area network bandwidth, wide area network bandwidth, enterprisenetwork bandwidth, server bandwidth, storage input/output bandwidth,advertising network bandwidth, market bandwidth, or other bandwidth, ina spot market for bandwidth resources 160 or a forward market forbandwidth 138. Purchasing of bandwidth resources may be configured andmanaged by an expert system operating on any of the external datasources 182 or on data aggregated by the set of data aggregation systems144 for the platform. Bandwidth resources may be purchased by anautomated system using an expert system, including machine learning orother artificial intelligence, such as where resources are purchasedwith favorable timing, such as based on an understanding of supply anddemand, that is determined by processing inputs from the various datasources. For example, the bandwidth purchase and sale machine 120 maypurchase or reserve bandwidth on a network resource for a futurenetworking activity managed by the platform based on learning from awide range of inputs about market conditions, behavior data, and dataregarding activities of agent and systems within the platform 100, suchas to obtain such resources at favorable prices during surge periods ofdemand for bandwidth. The expert system may be trained on a data set ofoutcomes from purchases under historical input conditions. The expertsystem may be trained on a data set of human purchase decisions and/ormay be supervised by one or more human operators. The bandwidth purchaseand sale machine 120 may also sell one or more bandwidth-relatedresources that are connected to, part of, or managed by the platform 100in a spot market for bandwidth resources 160 or a forward market forbandwidth 138. Sale may also be conducted by an expert system operatingon the various data sources described herein, including with training onoutcomes and human supervision.

The set of forward purchase and sale machines 110 may include a spectrumpurchase and sale machine 142, which may purchase one or morespectrum-related resources, such as cellular spectrum, 3G spectrum, 4Gspectrum, LTE spectrum, 5G spectrum, cognitive radio spectrum,peer-to-peer network spectrum, emergency responder spectrum and the likein a spot market for spectrum resources 162 or a forward market forspectrum/bandwidth 140. Purchasing of spectrum resources may beconfigured and managed by an expert system operating on any of theexternal data sources 182 or on data aggregated by the set of dataaggregation systems 144 for the platform. Spectrum resources may bepurchased by an automated system using an expert system, includingmachine learning or other artificial intelligence, such as whereresources are purchased with favorable timing, such as based on anunderstanding of supply and demand, that is determined by processinginputs from the various data sources. For example, the spectrum purchaseand sale machine 142 may purchase or reserve spectrum on a networkresource for a future networking activity managed by the platform basedon learning from a wide range of inputs about market conditions,behavior data, and data regarding activities of agent and systems withinthe platform 100, such as to obtain such resources at favorable pricesduring surge periods of demand for spectrum. The expert system may betrained on a data set of outcomes from purchases under historical inputconditions. The expert system may be trained on a data set of humanpurchase decisions and/or may be supervised by one or more humanoperators. The spectrum purchase and sale machine 142 may also sell oneor more spectrum-related resources that are connected to, part of, ormanaged by the platform 100 in a spot market for spectrum resources 162or a forward market for spectrum/bandwidth 140. Sale may also beconducted by an expert system operating on the various data sourcesdescribed herein, including with training on outcomes and humansupervision.

In embodiments, the intelligent resource allocation and coordinationsystem 168, including the intelligent resource purchasing system 164,the intelligent sale engine 172 and the automated spot market testingand arbitrage transaction execution engine 194, may provide coordinatedand automated allocation of resources and coordinated execution oftransactions across the various forward markets 130 and spot markets 170by coordinating the various purchase and sale machines, such as by anexpert system, such as a machine learning system (which may model-basedor a deep learning system, and which may be trained on outcomes and/orsupervised by humans). For example, the intelligent resource allocationand coordination system 168 may coordinate purchasing of resources for aset of assets and coordinated sale of resources available from a set ofassets, such as a fleet of vehicles, a data center of processing anddata storage resources, an information technology network (on premises,cloud, or hybrids), a fleet of energy production systems (renewable ornon-renewable), a smart home or building (including appliances,machines, infrastructure components and systems, and the like thereofthat consume or produce resources), and the like. The platform 100 mayoptimize allocation of resource purchasing, sale and utilization basedon data aggregated in the platform, such as by tracking activities ofvarious engines and agents, as well as by taking inputs from externaldata sources 182. In embodiments, outcomes may be provided as feedbackfor training the intelligent resource allocation and coordination system168, such as outcomes based on yield, profitability, optimization ofresources, optimization of business objectives, satisfaction of goals,satisfaction of users or operators, or the like. For example, as theenergy for computational tasks becomes a significant fraction of anenterprise's energy usage, the platform 100 may learn to optimize how aset of machines that have energy storage capacity allocate that capacityamong computing tasks (such as for cryptocurrency mining, application ofneural networks, computation on data and the like), other useful tasks(that may yield profits or other benefits), storage for future use, orsale to the provider of an energy grid. The platform 100 may be used byfleet operators, enterprises, governments, municipalities, militaryunits, first responder units, manufacturers, energy producers, cloudplatform providers, and other enterprises and operators that own oroperate resources that consume or provide energy, computation, datastorage, bandwidth, or spectrum. The platform 100 may also be used inconnection with markets for attention, such as to use available capacityof resources to support attention-based exchanges of value, such as inadvertising markets, micro-transaction markets, and others.

Referring still to FIGS. 2A and 2B, the platform 100 may include a setof intelligent forecasting engines 192 that forecast one or moreattributes, parameters, variables, or other factors, such as for use asinputs by the set of forward purchase and sale machines, the intelligenttransaction engines 126 (such as for intelligent cryptocurrencyexecution) or for other purposes. Each of the set of intelligentforecasting engines 192 may use data that is tracked, aggregated,processed, or handled within the platform 100, such as by the dataaggregation system 144, as well as input data from external data sources182, such as social media data sources 180, automated agent behavioraldata sources 188, human behavioral data sources 184, entity behavioraldata sources 190 and IoT data sources 198. These collective inputs maybe used to forecast attributes, such as using a model (e.g., Bayesian,regression, or other statistical model), a rule, or an expert system,such as a machine learning system that has one or more classifiers,pattern recognizers, and predictors, such as any of the expert systemsdescribed throughout this disclosure. In embodiments, the set ofintelligent forecasting engines 192 may include one or more specializedengines that forecast market attributes, such as capacity, demand,supply, and prices, using particular data sources for particularmarkets. These may include an energy price forecasting engine 215 thatbases its forecast on behavior of an automated agent, a network spectrumprice forecasting engine 217 that bases its forecast on behavior of anautomated agent, a REC price forecasting engine 219 that bases itsforecast on behavior of an automated agent, a compute price forecastingengine 221 that bases its forecast on behavior of an automated agent, anetwork spectrum price forecasting engine 223 that bases its forecast onbehavior of an automated agent. In each case, observations regarding thebehavior of automated agents, such as ones used for conversation, fordialog management, for managing electronic commerce, for managingadvertising and others may be provided as inputs for forecasting to theengines. The intelligent forecasting engines 192 may also include arange of engines that provide forecasts at least in part based on entitybehavior, such as behavior of business and other organizations, such asmarketing behavior, sales behavior, product offering behavior,advertising behavior, purchasing behavior, transactional behavior,merger and acquisition behavior, and other entity behavior. These mayinclude an energy price forecasting engine 225 using entity behavior, anetwork spectrum price forecasting engine 227 using entity behavior, aREC price forecasting engine 229 using entity behavior, a compute priceforecasting engine 231 using entity behavior, and a network spectrumprice forecasting engine 233 using entity behavior.

The intelligent forecasting engines 192 may also include a range ofengines that provide forecasts at least in part based on human behavior,such as behavior of consumers and users, such as purchasing behavior,shopping behavior, sales behavior, product interaction behavior, energyutilization behavior, mobility behavior, activity level behavior,activity type behavior, transactional behavior, and other humanbehavior. These may include an energy price forecasting engine 235 usinghuman behavior, a network spectrum price forecasting engine 237 usinghuman behavior, a REC price forecasting engine 239 using human behavior,a compute price forecasting engine 241 using human behavior, and anetwork spectrum price forecasting engine 243 using human behavior.

Referring still to FIGS. 2A and 2B, the platform 100 may include a setof intelligent transaction engines 136 that automate execution oftransactions in forward markets 130 and/or spot markets 170 based ondetermination that favorable conditions exist, such as by theintelligent resource allocation and coordination system 168 and/or withuse of forecasts form the intelligent forecasting engines 192. Theintelligent transaction engines 136 may be configured to automaticallyexecute transactions, using available market interfaces, such as APIs,connectors, ports, network interfaces, and the like, in each of themarkets noted above. In embodiments, the intelligent transaction enginesmay execute transactions based on event streams that come from externaldata sources, such as IoT data sources 198 and social media data sources180. The engines may include, for example, an IoT forward energytransaction engine 195 and/or an IoT compute market transaction engine106, either or both of which may use data from the Internet of Things todetermine timing and other attributes for market transaction in a marketfor one or more of the resources described herein, such as an energymarket transaction, a compute resource transaction or other resourcetransaction. IoT data may include instrumentation and controls data forone or more machines (optionally coordinated as a fleet) that use orproduce energy or that use or have compute resources, weather data thatinfluences energy prices or consumption (such as wind data influencingproduction of wind energy), sensor data from energy productionenvironments, sensor data from points of use for energy or computeresources (such as vehicle traffic data, network traffic data, ITnetwork utilization data, Internet utilization and traffic data, cameradata from work sites, smart building data, smart home data, and thelike), and other data collected by or transferred within the Internet ofThings, including data stored in IoT platforms and of cloud servicesproviders like Amazon, IBM, and others. The intelligent transactionengines 136 may include engines that use social data to determine timingof other attributes for a market transaction in one or more of theresources described herein, such as a social data forward energytransaction engine 199 and/or a social data compute market transactionengine 116. Social data may include data from social networking sites(e.g., Facebook™, YouTube™, Twitter™, Snapchat™, Instagram™, andothers), data from websites, data from e-commerce sites, and data fromother sites that contain information that may be relevant to determiningor forecasting behavior of users or entities, such as data indicatinginterest or attention to particular topics, goods or services, dataindicating activity types and levels such as may be observed by machineprocessing of image data showing individuals engaged in activities,including travel, work activities, leisure activities, and the like.Social data may be supplied to machine learning, such as for learninguser behavior or entity behavior, and/or as an input to an expertsystem, a model, or the like, such as one for determining, based on thesocial data, the parameters for a transaction. For example, an event orset of events in a social data stream may indicate the likelihood of asurge of interest in an online resource, a product, or a service, andcompute resources, bandwidth, storage, or like may be purchased inadvance (avoiding surge pricing) to accommodate the increased interestreflected by the social data stream.

Referring to FIG. 3 , the platform 100 may include capabilities fortransaction execution that involve one or more distributed ledgers 113and one or more smart contracts 103, where the distributed ledgers 113and smart contracts 103 are configured to enable specialized transactionfeatures for specific transaction domains. One such domain isintellectual property, which transactions are highly complex, involvinglicensing terms and conditions that are somewhat difficult to manage, ascompared to more straightforward sales of goods or services. Inembodiments, a smart contract wrapper 105, such as wrapper aggregatingintellectual property, is provided, using a distributed ledger, whereinthe smart contract embeds IP licensing terms for intellectual propertythat is embedded in the distributed ledger and wherein executing anoperation on the distributed ledger provides access to the intellectualproperty and commits the executing party to the IP licensing terms.Licensing terms for a wide range of goods and services, includingdigital goods like video, audio, video game, video game element, music,electronic book, and other digital goods may be managed by trackingtransactions involving them on a distributed ledger, whereby publishersmay verify a chain of licensing and sublicensing. The distributed ledgermay be configured to add each licensee to the ledger, and the ledger maybe retrieved at the point of use of a digital item, such as in astreaming platform, to validate that licensing has occurred.

In embodiments, an improved distributed ledger is provided with thesmart contract wrapper 105, such as an IP wrapper, container, smartcontract, or similar mechanism for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In many cases, intellectualproperty builds on other intellectual property, such as where softwarecode is derived from other code, where trade secrets or know-how forelements of a process are combined to enable a larger process, wherepatents covering sub-components of a system or steps in a process arepooled, where elements of a video game include sub-component assets fromdifferent creators, where a book contains contributions from multipleauthors, and the like. In embodiments, a smart IP wrapper aggregateslicensing terms for different intellectual property items (includingdigital goods, including ones embodying different types of intellectualproperty rights, and transaction data involving the item, as well asoptionally one or more portions of the item corresponding to thetransaction data, are stored in a distributed ledger that is configuredto enable validation of agreement to the licensing terms (such as atappoint of use) and/or access control to the item. In embodiments, aroyalty apportionment wrapper 115 may be provided in a system having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property and to agree toan apportionment of royalties among the parties in the ledger. Thus, aledger may accumulate contributions to the ledger along with evidence ofagreement to the apportionment of any royalties among the contributorsof the IP that is embedded in and/or controlled by the ledger. Theledger may record licensing terms and automatically vary them as newcontributions are made, such as by one or more rules. For example,contributors may be given a share of a royalty stack according to arule, such as based on a fractional contribution, such as based on linesof code contributed, lines of authorship, contribution to components ofa system, and the like. In embodiments, a distributed ledger may beforked into versions that represent varying combinations ofsub-components of IP, such as to allow users to select combinations thatare of most use, thereby allowing contributors who have contributed themost value to be rewarded. Variation and outcome tracking may beiteratively improved, such as by machine learning.

In embodiments, a distributed ledger is provided for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property.

In embodiments, the platform 100 may have an improved distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term via an IP transactionwrapper 119 of the ledger. This may include operations involvingcryptocurrencies, tokens, or other operations, as well as conventionalpayments and in-kind transfers, such as of various resources describedherein. The ledger may accumulate evidence of commitments to IPtransactions by parties, such as entering into royalty terms, revenuesharing terms, IP ownership terms, warranty and liability terms, licensepermissions and restrictions, field of use terms, and many others.

In embodiments, improved distributed ledgers may include ones having atokenized instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set. A party wishing toshare permission to know how, a trade secret or other valuableinstructions may thus share the instruction set via a distributed ledgerthat captures and stores evidence of an action on the ledger by a thirdparty, thereby evidencing access and agreement to terms and conditionsof access. In embodiments, the platform 100 may have a distributedledger that tokenizes executable algorithmic logic 121, such thatoperation on the distributed ledger provides provable access to theexecutable algorithmic logic. A variety of instruction sets may bestored by a distributed ledger, such as to verify access and verifyagreement to terms (such as smart contract terms). In embodiments,instruction sets that embody trade secrets may be separated intosub-components, so that operations must occur on multiple ledgers to get(provable) access to a trade secret. This may permit parties wishing toshare secrets, such as with multiple sub-contractors or vendors, to mainprovable access control, while separating components among differentvendors to avoid sharing an entire set with a single party. Variouskinds of executable instruction sets may be stored on specializeddistributed ledgers that may include smart wrappers for specific typesof instruction sets, such that provable access control, validation ofterms, and tracking of utilization may be performed by operations on thedistributed ledger (which may include triggering access controls withina content management system or other systems upon validation of actionstaken in a smart contract on the ledger. In embodiments, the platform100 may have a distributed ledger that tokenizes a 3D printerinstruction set 123, such that operation on the distributed ledgerprovides provable access to the instruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a coating process 125, such thatoperation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process129, such that operation on the distributed ledger provides provableaccess to the fabrication process.

In embodiments, the platform 100 may have a distributed ledger thattokenizes a firmware program 131, such that operation on the distributedledger provides provable access to the firmware program.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for an FPGA 133, such that operation on thedistributed ledger provides provable access to the FPGA.

In embodiments, the platform 100 may have a distributed ledger thattokenizes serverless code logic 135, such that operation on thedistributed ledger provides provable access to the serverless codelogic.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a crystal fabrication system 139, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a food preparation process 141, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a polymer production process 143, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for chemical synthesis process 145, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a biological production process 149,such that operation on the distributed ledger provides provable accessto the instruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes a trade secret with an expert wrapper 151, such that operationon the distributed ledger provides provable access to the trade secretand the wrapper provides validation of the trade secret by the expert.An interface may be provided by which an expert accesses the tradesecret on the ledger and verifies that the information is accurate andsufficient to allow a third party to use the secret.

In embodiments, the platform 100 may have a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret. Views may be used toallocate value to creators of the trade secret, to operators of theplatform 100, or the like.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set 111, such that operation on the distributedledger provides provable access 155 to the instruction set and executionof the instruction set on a system results in recording a transaction inthe distributed ledger.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property.

In embodiments, the platform 100 may have a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions 161 to provide a modified set of instructions.

Referring still to FIG. 3 , an intelligent cryptocurrency executionengine 183 may provide intelligence for the timing, location, and otherattributes of a cryptocurrency transaction, such as a miningtransaction, an exchange transaction, a storage transaction, a retrievaltransaction, or the like. Cryptocurrencies like Bitcoin™ areincreasingly widespread, with specialized coins having emerged for awide variety of purposes, such as exchanging value in variousspecialized market domains. Initial offerings of such coins, or ICOs,are increasingly subject to regulations, such as securities regulations,and in some cases to taxation. Thus, while cryptocurrency transactionstypically occur within computer networks, jurisdictional factors may beimportant in determining where, when, and how to execute a transaction,store a cryptocurrency, exchange it for value. In embodiments,intelligent cryptocurrency execution engine 183 may use featuresembedded in or wrapped around the digital object representing a coin,such as features that cause the execution of transactions in the coin tobe undertaken with awareness of various conditions, including geographicconditions, regulatory conditions, tax conditions, market conditions,and the like.

In embodiments, the platform 100 may include a tax aware coin 165 orsmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location.

In embodiments, the platform 100 may include a location-aware coin 169or smart wrapper that enables a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment.

In embodiments, the platform 100 may include an expert system or AIagent 171 that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status. Machine learning mayuse one or more models or heuristics, such as populated with relevantjurisdictional tax data, may be trained on a training set of humantrading operations, may be supervised by human supervisors, and/or mayuse a deep learning technique based on outcomes over time, such as whenoperating on a wide range of internal system data and external datasources 182 as described throughout this disclosure.

In embodiments, the platform 100 may include regulation aware coin 173having a coin, a smart wrapper, and/or an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. Machine learning may use one or more models orheuristics, such as populated with relevant jurisdictional regulatorydata, may be trained on a training set of human trading operations, maybe supervised by human supervisors, and/or may use a deep learningtechnique based on outcomes over time, such as when operating on a widerange of internal system data and external data sources 182 as describedthroughout this disclosure.

In embodiments, the platform 100 may include an energy price-aware coin175, wrapper, or expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source. Cryptocurrencytransactions, such as coin mining and blockchain operations, may behighly energy intensive. An energy price-aware coin may be configured totime such operations based on energy price forecasts, such as with oneor more of the intelligent forecasting engines 192 described throughoutthis disclosure.

In embodiments, the platform 100 may include an energy source aware coin179, wrapper, or expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on an understandingof available energy sources to power computing resources to execute thetransaction. For example, coin mining may be performed only whenrenewable energy sources are available. Machine learning foroptimization of a transaction may use one or more models or heuristics,such as populated with relevant energy source data (such as may becaptured in a knowledge graph, which may contain energy sourceinformation by type, location and operating parameters), may be trainedon a training set of input-output data for human-initiated transactions,may be supervised by human supervisors, and/or may use a deep learningtechnique based on outcomes over time, such as when operating on a widerange of internal system data and external data sources 182 as describedthroughout this disclosure.

In embodiments, the platform 100 may include a charging cycle aware coin181, wrapper, or an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Forexample, a battery may be discharged for a cryptocurrency transactiononly if a minimum threshold of battery charge is maintained for otheroperational use, if re-charging resources are known to be readilyavailable, or the like. Machine learning for optimization of chargingand recharging may use one or more models or heuristics, such aspopulated with relevant battery data (such as may be captured in aknowledge graph, which may contain energy source information by type,location and operating parameters), may be trained on a training set ofhuman operations, may be supervised by human supervisors, and/or may usea deep learning technique based on outcomes over time, such as whenoperating on a wide range of internal system data and external datasources 182 as described throughout this disclosure.

Optimization of various intelligent coin operations may occur withmachine learning that is trained on outcomes, such as financialprofitability. Any of the machine learning systems described throughoutthis disclosure may be used for optimization of intelligentcryptocurrency transaction management.

In embodiments, compute resources, such as those mentioned throughoutthis disclosure, may be allocated to perform a range of computing tasks,both for operations that occur within the platform 100, ones that aremanaged by the platform, and ones that involve the activities, workflowsand processes of various assets that may be owned, operated or managedin conjunction with the platform, such as sets or fleets of assets thathave or use computing resources. Examples of compute tasks include,without limitation, cryptocurrency mining, distributed ledgercalculations and storage, forecasting tasks, transaction executiontasks, spot market testing tasks, internal data collection tasks,external data collection, machine learning tasks, and others. As notedabove, energy, compute resources, bandwidth, spectrum, and otherresources may be coordinated, such as by machine learning, for thesetasks. Outcome and feedback information may be provided for the machinelearning, such as outcomes for any of the individual tasks and overalloutcomes, such as yield and profitability for business or otheroperations involving the tasks.

In embodiments, networking resources, such as those mentioned throughoutthis disclosure, may be allocated to perform a range of networkingtasks, both for operations that occur within the platform 100, ones thatare managed by the platform, and ones that involve the activities,workflows and processes of various assets that may be owned, operated ormanaged in conjunction with the platform, such as sets or fleets ofassets that have or use networking resources. Examples of networkingtasks include cognitive network coordination, network coding, peerbandwidth sharing (including, for example cost-based routing,value-based routing, outcome-based routing, and the like), distributedtransaction execution, spot market testing, randomization (e.g., usinggenetic programming with outcome feedback to vary network configurationsand transmission paths), internal data collection and external datacollection. As noted above, energy, compute resources, bandwidth,spectrum, and other resources may be coordinated, such as by machinelearning, for these networking tasks. Outcome and feedback informationmay be provided for the machine learning, such as outcomes for any ofthe individual tasks and overall outcomes, such as yield andprofitability for business or other operations involving the tasks.

In embodiments, data storage resources, such as those mentionedthroughout this disclosure, may be allocated to perform a range of datastorage tasks, both for operations that occur within the platform 100,ones that are managed by the platform, and ones that involve theactivities, workflows and processes of various assets that may be owned,operated or managed in conjunction with the platform, such as sets orfleets of assets that have or use networking resources. Examples of datastorage tasks include distributed ledger storage, storage of internaldata (such as operational data with the platform), cryptocurrencystorage, smart wrapper storage, storage of external data, storage offeedback and outcome data, and others. As noted above, data storage,energy, compute resources, bandwidth, spectrum, and other resources maybe coordinated, such as by machine learning, for these data storagetasks. Outcome and feedback information may be provided for the machinelearning, such as outcomes for any of the individual tasks and overalloutcomes, such as yield and profitability for business or otheroperations involving the tasks.

In embodiments, smart contracts, such as ones embodying terms relatingto intellectual property, trade secrets, know how, instruction sets,algorithmic logic, and the like may embody or include contract terms,which may include terms and conditions for options, royalty stackingterms, field exclusivity, partial exclusivity, pooling of intellectualproperty, standards terms (such as relating to essential andnon-essential patent usage), technology transfer terms, consultingservice terms, update terms, support terms, maintenance terms,derivative works terms, copying terms, and performance-related rights ormetrics, among many others.

In embodiments, where an instruction set is embodied in digital form,such as contained in or managed by a distributed ledger transactionssystem, various systems may be configured with interfaces that allowthem to access and use the instruction sets. In embodiments, suchsystems may include access control features that validate properlicensing by inspection of a distributed ledger, a key, a token, or thelike that indicates the presence of access rights to an instruction set.Such systems that execute distributed instruction sets may includesystems for 3D printing, crystal fabrication, semiconductor fabrication,coating items, producing polymers, chemical synthesis, and biologicalproduction, among others.

Networking capabilities and network resources should be understood toinclude a wide range of networking systems, components and capabilities,including infrastructure elements for 3G, 4G, LTE, 5G and other cellularnetwork types, access points, routers, and other Wi-Fi elements,cognitive networking systems and components, mobile networking systemsand components, physical layer, MAC layer and application layer systemsand components, cognitive networking components and capabilities,peer-to-peer networking components and capabilities, optical networkingcomponents and capabilities, and others.

Neural Net Systems

Referring to FIG. 4 through FIG. 31 , embodiments of the presentdisclosure, including ones involving expert systems, self-organization,machine learning, artificial intelligence, and the like, may benefitfrom the use of a neural net, such as a neural net trained for patternrecognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes. References to a neural net throughout this disclosureshould be understood to encompass a wide range of different types ofneural networks, machine learning systems, artificial intelligencesystems, and the like, such as feed forward neural networks, radialbasis function neural networks, self-organizing neural networks (e.g.,Kohonen self-organizing neural networks), recurrent neural networks,modular neural networks, artificial neural networks, physical neuralnetworks, multi-layered neural networks, convolutional neural networks,hybrids of neural networks with other expert systems (e.g., hybrid fuzzylogic—neural network systems), Autoencoder neural networks,probabilistic neural networks, time delay neural networks, convolutionalneural networks, regulatory feedback neural networks, radial basisfunction neural networks, recurrent neural networks, Hopfield neuralnetworks, Boltzmann machine neural networks, self-organizing map (SOM)neural networks, learning vector quantization (LVQ) neural networks,fully recurrent neural networks, simple recurrent neural networks, echostate neural networks, long short-term memory neural networks,bi-directional neural networks, hierarchical neural networks, stochasticneural networks, genetic scale RNN neural networks, committee ofmachines neural networks, associative neural networks, physical neuralnetworks, instantaneously trained neural networks, spiking neuralnetworks, neocognitron neural networks, dynamic neural networks,cascading neural networks, neuro-fuzzy neural networks, compositionalpattern-producing neural networks, memory neural networks, hierarchicaltemporal memory neural networks, deep feed forward neural networks,gated recurrent unit (GCU) neural networks, auto encoder neuralnetworks, variational auto encoder neural networks, de-noising autoencoder neural networks, sparse auto-encoder neural networks, Markovchain neural networks, restricted Boltzmann machine neural networks,deep belief neural networks, deep convolutional neural networks,de-convolutional neural networks, deep convolutional inverse graphicsneural networks, generative adversarial neural networks, liquid statemachine neural networks, extreme learning machine neural networks, echostate neural networks, deep residual neural networks, support vectormachine neural networks, neural Turing machine neural networks, and/orholographic associative memory neural networks, or hybrids orcombinations of the foregoing, or combinations with other expertsystems, such as rule-based systems, model-based systems (including onesbased on physical models, statistical models, flow-based models,biological models, biomimetic models, and the like).

In embodiments, FIGS. 5-31 depict exemplary neural networks and FIG. 4depicts a legend showing the various components of the neural networksdepicted throughout FIGS. 5-31 . FIG. 4 depicts various neural netcomponents depicted in cells that are assigned functions andrequirements. In embodiments, the various neural net examples mayinclude back fed data/sensor cells, data/sensor cells, noisy inputcells, and hidden cells. The neural net components also includeprobabilistic hidden cells, spiking hidden cells, output cells, matchinput/output cells, recurrent cells, memory cells, different memorycells, kernels, and convolution or pool cells.

In embodiments, FIG. 5 depicts an exemplary perceptron neural networkthat may connect to, integrate with, or interface with the platform 100.The platform may also be associated with further neural net systems suchas a feed forward neural network (FIG. 6 ), a radial basis neuralnetwork (FIG. 7 ), a deep feed forward neural network (FIG. 8 ), arecurrent neural network (FIG. 9 ), a long/short term neural network(FIG. 10 ), and a gated recurrent neural network (FIG. 11 ). Theplatform may also be associated with further neural net systems such asan auto encoder neural network (FIG. 12 ), a variational neural network(FIG. 13 ), a denoising neural network (FIG. 14 ), a sparse neuralnetwork (FIG. 15 ), a Markov chain neural network (FIG. 16 ), and aHopfield network neural network (FIG. 17 ). The platform may further beassociated with additional neural net systems such as a Boltzmannmachine neural network (FIG. 18 ), a restricted BM neural network (FIG.19 ), a deep belief neural network (FIG. 20 ), a deep convolutionalneural network (FIG. 21 ), a deconvolutional neural network (FIG. 22 ),and a deep convolutional inverse graphics neural network (FIG. 23 ). Theplatform may also be associated with further neural net systems such asa generative adversarial neural network (FIG. 24 ), a liquid statemachine neural network (FIG. 25 ), an extreme learning machine neuralnetwork (FIG. 26 ), an echo state neural network (FIG. 27 ), a deepresidual neural network (FIG. 28 ), a Kohonen neural network (FIG. 29 ),a support vector machine neural network (FIG. 30 ), and a neural Turingmachine neural network (FIG. 31 ).

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes.Functions may involve weights, features, feature vectors, and the like.Neurons may include perceptrons, neurons that mimic biological functions(such as of the human senses of touch, vision, taste, hearing, andsmell), and the like. Continuous neurons, such as with sigmoidalactivation, may be used in the context of various forms of neural net,such as where back propagation is involved.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training may include presenting the neural networkwith one or more training data sets that represent values, such assensor data, event data, parameter data, and other types of data(including the many types described throughout this disclosure), as wellas one or more indicators of an outcome, such as an outcome of aprocess, an outcome of a calculation, an outcome of an event, an outcomeof an activity, or the like. Training may include training inoptimization, such as training a neural network to optimize one or moresystems based on one or more optimization approaches, such as Bayesianapproaches, parametric Bayes classifier approaches, k-nearest-neighborclassifier approaches, iterative approaches, interpolation approaches,Pareto optimization approaches, algorithmic approaches, and the like.Feedback may be provided in a process of variation and selection, suchas with a genetic algorithm that evolves one or more solutions based onfeedback through a series of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more transactionalenvironments and transmitted to the cloud platform over one or morenetworks, including using network coding to provide efficienttransmission. In the cloud platform, optionally using massively parallelcomputational capability, a plurality of different neural networks ofvarious types (including modular forms, structure-adaptive forms,hybrids, and the like) may be used to undertake prediction,classification, control functions, and provide other outputs asdescribed in connection with expert systems disclosed throughout thisdisclosure. The different neural networks may be structured to competewith each other (optionally including use evolutionary algorithms,genetic algorithms, or the like), such that an appropriate type ofneural network, with appropriate input sets, weights, node types andfunctions, and the like, may be selected, such as by an expert system,for a specific task involved in a given context, workflow, environmentprocess, system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like a data source related to at least one resource orparameter related to a transactional environment, such as any of thedata sources mentioned throughout this disclosure, through a series ofneurons or nodes, to an output. Data may move from the input nodes tothe output nodes, optionally passing through one or more hidden nodes,without loops. In embodiments, feed forward neural networks may beconstructed with various types of units, such as binary McCulloch-Pittsneurons, the simplest of which is a perceptron.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a capsule neuralnetwork, such as for prediction, classification, or control functionswith respect to a transactional environment, such as relating to one ormore of the machines and automated systems described throughout thisdisclosure.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, which may be preferred in some situationsinvolving interpolation in a multi-dimensional space (such as whereinterpolation is helpful in optimizing a multi-dimensional function,such as for optimizing a data marketplace as described here, optimizingthe efficiency or output of a power generation system, a factory system,or the like, or other situation involving multiple dimensions. Inembodiments, each neuron in the RBF neural network stores an examplefrom a training set as a “prototype.” Linearity involved in thefunctioning of this neural network offers RBF the advantage of nottypically suffering from problems with local minima or maxima.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, such as one that employs a distancecriterion with respect to a center (e.g., a Gaussian function). A radialbasis function may be applied as a replacement for a hidden layer, suchas a sigmoidal hidden layer transfer, in a multi-layer perceptron. AnRBF network may have two layers, such as where an input is mapped ontoeach RBF in a hidden layer. In embodiments, an output layer may comprisea linear combination of hidden layer values representing, for example, amean predicted output. The output layer value may provide an output thatis the same as or similar to that of a regression model in statistics.In classification problems, the output layer may be a sigmoid functionof a linear combination of hidden layer values, representing a posteriorprobability. Performance in both cases is often improved by shrinkagetechniques, such as ridge regression in classical statistics. Thiscorresponds to a prior belief in small parameter values (and thereforesmooth output functions) in a Bayesian framework. RBF networks may avoidlocal minima, because the only parameters that are adjusted in thelearning process are the linear mapping from hidden layer to outputlayer. Linearity ensures that the error surface is quadratic andtherefore has a single minimum. In regression problems, this may befound in one matrix operation. In classification problems, the fixednon-linearity introduced by the sigmoid output function may be handledusing an iteratively re-weighted least squares function or the like. RBFnetworks may use kernel methods such as support vector machines (SVM)and Gaussian processes (where the RBF is the kernel function). Anon-linear kernel function may be used to project the input data into aspace where the learning problem may be solved using a linear model.

In embodiments, an RBF neural network may include an input layer, ahidden layer, and a summation layer. In the input layer, one neuronappears in the input layer for each predictor variable. In the case ofcategorical variables, N−1 neurons are used, where N is the number ofcategories. The input neurons may, in embodiments, standardize the valueranges by subtracting the median and dividing by the interquartilerange. The input neurons may then feed the values to each of the neuronsin the hidden layer. In the hidden layer, a variable number of neuronsmay be used (determined by the training process). Each neuron mayconsist of a radial basis function that is centered on a point with asmany dimensions as a number of predictor variables. The spread (e.g.,radius) of the RBF function may be different for each dimension. Thecenters and spreads may be determined by training. When presented withthe vector of input values from the input layer, a hidden neuron maycompute a Euclidean distance of the test case from the neuron's centerpoint and then apply the RBF kernel function to this distance, such asusing the spread values. The resulting value may then be passed to thesummation layer. In the summation layer, the value coming out of aneuron in the hidden layer may be multiplied by a weight associated withthe neuron and may add to the weighted values of other neurons. This sumbecomes the output. For classification problems, one output is produced(with a separate set of weights and summation units) for each targetcategory. The value output for a category is the probability that thecase being evaluated has that category. In training of an RBF, variousparameters may be determined, such as the number of neurons in a hiddenlayer, the coordinates of the center of each hidden-layer function, thespread of each function in each dimension, and the weights applied tooutputs as they pass to the summation layer. Training may be used byclustering algorithms (such as k-means clustering), by evolutionaryapproaches, and the like.

In embodiments, a recurrent neural network may have a time-varying,real-valued (more than just zero or one) activation (output). Eachconnection may have a modifiable real-valued weight. Some of the nodesare called labeled nodes, some output nodes, and others hidden nodes.For supervised learning in discrete time settings, training sequences ofreal-valued input vectors may become sequences of activations of theinput nodes, one input vector at a time. At each time step, eachnon-input unit may compute its current activation as a nonlinearfunction of the weighted sum of the activations of all units from whichit receives connections. The system may explicitly activate (independentof incoming signals) some output units at certain time steps.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingneural network, such as a Kohonen self-organizing neural network, suchas for visualization of views of data, such as low-dimensional views ofhigh-dimensional data. The self-organizing neural network may applycompetitive learning to a set of input data, such as from one or moresensors or other data inputs from or associated with a transactionalenvironment, including any machine or component that relates to thetransactional environment. In embodiments, the self-organizing neuralnetwork may be used to identify structures in data, such as unlabeleddata, such as in data sensed from a range of data sources about orsensors in or about in a transactional environment, where sources of thedata are unknown (such as where events may be coming from any of a rangeof unknown sources). The self-organizing neural network may organizestructures or patterns in the data, such that they may be recognized,analyzed, and labeled, such as identifying market behavior structures ascorresponding to other events and signals.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a recurrent neuralnetwork, which may allow for a bi-directional flow of data, such aswhere connected units (e.g., neurons or nodes) form a directed cycle.Such a network may be used to model or exhibit dynamic temporalbehavior, such as involved in dynamic systems, such as a wide variety ofthe automation systems, machines and devices described throughout thisdisclosure, such as an automated agent interacting with a marketplacefor purposes of collecting data, testing spot market transactions,execution transactions, and the like, where dynamic system behaviorinvolves complex interactions that a user may desire to understand,predict, control and/or optimize. For example, the recurrent neuralnetwork may be used to anticipate the state of a market, such as oneinvolving a dynamic process or action, such as a change in state of aresource that is traded in or that enables a marketplace oftransactional environment. In embodiments, the recurrent neural networkmay use internal memory to process a sequence of inputs, such as fromother nodes and/or from sensors and other data inputs from or about thetransactional environment, of the various types described herein. Inembodiments, the recurrent neural network may also be used for patternrecognition, such as for recognizing a machine, component, agent, orother item based on a behavioral signature, a profile, a set of featurevectors (such as in an audio file or image), or the like. In anon-limiting example, a recurrent neural network may recognize a shiftin an operational mode of a marketplace or machine by learning toclassify the shift from a training data set consisting of a stream ofdata from one or more data sources of sensors applied to or about one ormore resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a modular neuralnetwork, which may comprise a series of independent neural networks(such as ones of various types described herein) that are moderated byan intermediary. Each of the independent neural networks in the modularneural network may work with separate inputs, accomplishing subtasksthat make up the task the modular network as whole is intended toperform. For example, a modular neural network may comprise a recurrentneural network for pattern recognition, such as to recognize what typeof machine or system is being sensed by one or more sensors that areprovided as input channels to the modular network and an RBF neuralnetwork for optimizing the behavior of the machine or system onceunderstood. The intermediary may accept inputs of each of the individualneural networks, process them, and create output for the modular neuralnetwork, such an appropriate control parameter, a prediction of state,or the like.

Combinations among any of the pairs, triplets, or larger combinations,of the various neural network types described herein, are encompassed bythe present disclosure. This may include combinations where an expertsystem uses one neural network for recognizing a pattern (e.g., apattern indicating a problem or fault condition) and a different neuralnetwork for self-organizing an activity or workflow based on therecognized pattern (such as providing an output governing autonomouscontrol of a system in response to the recognized condition or pattern).This may also include combinations where an expert system uses oneneural network for classifying an item (e.g., identifying a machine, acomponent, or an operational mode) and a different neural network forpredicting a state of the item (e.g., a fault state, an operationalstate, an anticipated state, a maintenance state, or the like). Modularneural networks may also include situations where an expert system usesone neural network for determining a state or context (such as a stateof a machine, a process, a workflow, a marketplace, a storage system, anetwork, a data collector, or the like) and a different neural networkfor self-organizing a process involving the state or context (e.g., adata storage process, a network coding process, a network selectionprocess, a data marketplace process, a power generation process, amanufacturing process, a refining process, a digging process, a boringprocess, or other process described herein).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a physical neuralnetwork where one or more hardware elements is used to perform orsimulate neural behavior. In embodiments, one or more hardware neuronsmay be configured to stream voltage values, current values, or the likethat represent sensor data, such as to calculate information from analogsensor inputs representing energy consumption, energy production, or thelike, such as by one or more machines providing energy or consumingenergy for one or more transactions. One or more hardware nodes may beconfigured to stream output data resulting from the activity of theneural net. Hardware nodes, which may comprise one or more chips,microprocessors, integrated circuits, programmable logic controllers,application-specific integrated circuits, field-programmable gatearrays, or the like, may be provided to optimize the machine that isproducing or consuming energy, or to optimize another parameter of somepart of a neural net of any of the types described herein. Hardwarenodes may include hardware for acceleration of calculations (such asdedicated processors for performing basic or more sophisticatedcalculations on input data to provide outputs, dedicated processors forfiltering or compressing data, dedicated processors for de-compressingdata, dedicated processors for compression of specific file or datatypes (e.g., for handling image data, video streams, acoustic signals,thermal images, heat maps, or the like), and the like. A physical neuralnetwork may be embodied in a data collector, including one that may bereconfigured by switching or routing inputs in varying configurations,such as to provide different neural net configurations within the datacollector for handling different types of inputs (with the switching andconfiguration optionally under control of an expert system, which mayinclude a software-based neural net located on the data collector orremotely). A physical, or at least partially physical, neural networkmay include physical hardware nodes located in a storage system, such asfor storing data within a machine, a data storage system, a distributedledger, a mobile device, a server, a cloud resource, or in atransactional environment, such as for accelerating input/outputfunctions to one or more storage elements that supply data to or takedata from the neural net. A physical, or at least partially physical,neural network may include physical hardware nodes located in a network,such as for transmitting data within, to or from an industrialenvironment, such as for accelerating input/output functions to one ormore network nodes in the net, accelerating relay functions, or thelike. In embodiments, of a physical neural network, an electricallyadjustable resistance material may be used for emulating the function ofa neural synapse. In embodiments, the physical hardware emulates theneurons, and software emulates the neural network between the neurons.In embodiments, neural networks complement conventional algorithmiccomputers. They are versatile and may be trained to perform appropriatefunctions without the need for any instructions, such as classificationfunctions, optimization functions, pattern recognition functions,control functions, selection functions, evolution functions, and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a multilayeredfeed forward neural network, such as for complex pattern classificationof one or more items, phenomena, modes, states, or the like. Inembodiments, a multilayered feed forward neural network may be trainedby an optimization technique, such as a genetic algorithm, such as toexplore a large and complex space of options to find an optimum, ornear-optimum, global solution. For example, one or more geneticalgorithms may be used to train a multilayered feed forward neuralnetwork to classify complex phenomena, such as to recognize complexoperational modes of machines, such as modes involving complexinteractions among machines (including interference effects, resonanceeffects, and the like), modes involving non-linear phenomena, modesinvolving critical faults, such as where multiple, simultaneous faultsoccur, making root cause analysis difficult, and others. In embodiments,a multilayered feed forward neural network may be used to classifyresults from monitoring of a marketplace, such as monitoring systems,such as automated agents, that operate within the marketplace, as wellas monitoring resources that enable the marketplace, such as computing,networking, energy, data storage, energy storage, and other resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed-forward,back-propagation multi-layer perceptron (MLP) neural network, such asfor handling one or more remote sensing applications, such as for takinginputs from sensors distributed throughout various transactionalenvironments. In embodiments, the MLP neural network may be used forclassification of transactional environments and resource environments,such as spot markets, forward markets, energy markets, renewable energycredit (REC) markets, networking markets, advertising markets, spectrummarkets, ticketing markets, rewards markets, compute markets, and othersmentioned throughout this disclosure, as well as physical resources andenvironments that produce them, such as energy resources (includingrenewable energy environments, mining environments, explorationenvironments, drilling environments, and the like, includingclassification of geological structures (including underground featuresand above ground features), classification of materials (includingfluids, minerals, metals, and the like), and other problems. This mayinclude fuzzy classification.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use astructure-adaptive neural network, where the structure of a neuralnetwork is adapted, such as based on a rule, a sensed condition, acontextual parameter, or the like. For example, if a neural network doesnot converge on a solution, such as classifying an item or arriving at aprediction, when acting on a set of inputs after some amount oftraining, the neural network may be modified, such as from a feedforward neural network to a recurrent neural network, such as byswitching data paths between some subset of nodes from unidirectional tobi-directional data paths. The structure adaptation may occur undercontrol of an expert system, such as to trigger adaptation uponoccurrence of a trigger, rule, or event, such as recognizing occurrenceof a threshold (such as an absence of a convergence to a solution withina given amount of time) or recognizing a phenomenon as requiringdifferent or additional structure (such as recognizing that a system isvarying dynamically or in a non-linear fashion). In one non-limitingexample, an expert system may switch from a simple neural networkstructure like a feed forward neural network to a more complex neuralnetwork structure like a recurrent neural network, a convolutionalneural network, or the like upon receiving an indication that acontinuously variable transmission is being used to drive a generator,turbine, or the like in a system being analyzed.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an autoencoder,autoassociator or Diabolo neural network, which may be similar to amultilayer perceptron (MLP) neural network, such as where there may bean input layer, an output layer and one or more hidden layers connectingthem. However, the output layer in the auto-encoder may have the samenumber of units as the input layer, where the purpose of the MLP neuralnetwork is to reconstruct its own inputs (rather than just emitting atarget value). Therefore, the auto encoders may operate as anunsupervised learning model. An auto encoder may be used, for example,for unsupervised learning of efficient codings, such as fordimensionality reduction, for learning generative models of data, andthe like. In embodiments, an auto-encoding neural network may be used toself-learn an efficient network coding for transmission of analog sensordata from a machine over one or more networks or of digital data fromone or more data sources. In embodiments, an auto-encoding neuralnetwork may be used to self-learn an efficient storage approach forstorage of streams of data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a probabilisticneural network (PNN), which, in embodiments, may comprise a multi-layer(e.g., four-layer) feed forward neural network, where layers may includeinput layers, hidden layers, pattern/summation layers and an outputlayer. In an embodiment of a PNN algorithm, a parent probabilitydistribution function (PDF) of each class may be approximated, such asby a Parzen window and/or a non-parametric function. Then, using the PDFof each class, the class probability of a new input is estimated, andBayes' rule may be employed, such as to allocate it to the class withthe highest posterior probability. A PNN may embody a Bayesian networkand may use a statistical algorithm or analytic technique, such asKernel Fisher discriminant analysis technique. The PNN may be used forclassification and pattern recognition in any of a wide range ofembodiments disclosed herein. In one non-limiting example, aprobabilistic neural network may be used to predict a fault condition ofan engine based on collection of data inputs from sensors andinstruments for the engine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a time delayneural network (TDNN), which may comprise a feed forward architecturefor sequential data that recognizes features independent of sequenceposition. In embodiments, to account for time shifts in data, delays areadded to one or more inputs, or between one or more nodes, so thatmultiple data points (from distinct points in time) are analyzedtogether. A time delay neural network may form part of a larger patternrecognition system, such as using a perceptron network. In embodiments,a TDNN may be trained with supervised learning, such as where connectionweights are trained with back propagation or under feedback. Inembodiments, a TDNN may be used to process sensor data from distinctstreams, such as a stream of velocity data, a stream of accelerationdata, a stream of temperature data, a stream of pressure data, and thelike, where time delays are used to align the data streams in time, suchas to help understand patterns that involve understanding of the variousstreams (e.g., changes in price patterns in spot or forward markets).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a convolutionalneural network (referred to in some cases as a CNN, a ConvNet, a shiftinvariant neural network, or a space invariant neural network), whereinthe units are connected in a pattern similar to the visual cortex of thehuman brain. Neurons may respond to stimuli in a restricted region ofspace, referred to as a receptive field. Receptive fields may partiallyoverlap, such that they collectively cover the entire (e.g., visual)field. Node responses may be calculated mathematically, such as by aconvolution operation, such as using multilayer perceptrons that useminimal preprocessing. A convolutional neural network may be used forrecognition within images and video streams, such as for recognizing atype of machine in a large environment using a camera system disposed ona mobile data collector, such as on a drone or mobile robot. Inembodiments, a convolutional neural network may be used to provide arecommendation based on data inputs, including sensor inputs and othercontextual information, such as recommending a route for a mobile datacollector. In embodiments, a convolutional neural network may be usedfor processing inputs, such as for natural language processing ofinstructions provided by one or more parties involved in a workflow inan environment. In embodiments, a convolutional neural network may bedeployed with a large number of neurons (e.g., 100,000, 500,000 ormore), with multiple (e.g., 4, 5, 6 or more) layers, and with many(e.g., millions) of parameters. A convolutional neural net may use oneor more convolutional nets.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a regulatoryfeedback network, such as for recognizing emergent phenomena (such asnew types of behavior not previously understood in a transactionalenvironment).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingmap (SOM), involving unsupervised learning. A set of neurons may learnto map points in an input space to coordinates in an output space. Theinput space may have different dimensions and topology from the outputspace, and the SOM may preserve these while mapping phenomena intogroups.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a learning vectorquantization neural net (LVQ). Prototypical representatives of theclasses may parameterize, together with an appropriate distance measure,in a distance-based classification scheme.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an echo statenetwork (ESN), which may comprise a recurrent neural network with asparsely connected, random hidden layer. The weights of output neuronsmay be changed (e.g., the weights may be trained based on feedback). Inembodiments, an ESN may be used to handle time series patterns, such as,in an example, recognizing a pattern of events associated with a market,such as the pattern of price changes in response to stimuli.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a Bi-directional,recurrent neural network (BRNN), such as using a finite sequence ofvalues (e.g., voltage values from a sensor) to predict or label eachelement of the sequence based on both the past and the future context ofthe element. This may be done by adding the outputs of two RNNs, such asone processing the sequence from left to right, the other one from rightto left. The combined outputs are the predictions of target signals,such as ones provided by a teacher or supervisor. A bi-directional RNNmay be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchical RNNthat connects elements in various ways to decompose hierarchicalbehavior, such as into useful subprograms. In embodiments, ahierarchical RNN may be used to manage one or more hierarchicaltemplates for data collection in a transactional environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a stochasticneural network, which may introduce random variations into the network.Such random variations may be viewed as a form of statistical sampling,such as Monte Carlo sampling.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a genetic scalerecurrent neural network. In such embodiments, an RNN (often an LSTM) isused where a series is decomposed into a number of scales where everyscale informs the primary length between two consecutive points. A firstorder scale consists of a normal RNN, a second order consists of allpoints separated by two indices and so on. The Nth order RNN connectsthe first and last node. The outputs from all the various scales may betreated as a committee of members, and the associated scores may be usedgenetically for the next iteration.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a committee ofmachines (CoM), comprising a collection of different neural networksthat together “vote” on a given example. Because neural networks maysuffer from local minima, starting with the same architecture andtraining, but using randomly different initial weights often givesdifferent results. A CoM tends to stabilize the result.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an associativeneural network (ASNN), such as involving an extension of a committee ofmachines that combines multiple feed forward neural networks and ak-nearest neighbor technique. It may use the correlation betweenensemble responses as a measure of distance amid the analyzed cases forthe kNN. This corrects the bias of the neural network ensemble. Anassociative neural network may have a memory that may coincide with atraining set. If new data become available, the network instantlyimproves its predictive ability and provides data approximation(self-learns) without retraining. Another important feature of ASNN isthe possibility to interpret neural network results by analysis ofcorrelations between data cases in the space of models.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an instantaneouslytrained neural network (ITNN), where the weights of the hidden and theoutput layers are mapped directly from training vector data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a spiking neuralnetwork, which may explicitly consider the timing of inputs. The networkinput and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs may process information inthe time domain (e.g., signals that vary over time, such as signalsinvolving dynamic behavior of markets or transactional environments).They are often implemented as recurrent networks.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a dynamic neuralnetwork that addresses nonlinear multivariate behavior and includeslearning of time-dependent behavior, such as transient phenomena anddelay effects. Transients may include behavior of shifting marketvariables, such as prices, available quantities, availablecounterparties, and the like.

In embodiments, cascade correlation may be used as an architecture andsupervised learning algorithm, supplementing adjustment of the weightsin a network of fixed topology. Cascade-correlation may begin with aminimal network, then automatically trains, and adds new hidden unitsone by one, creating a multi-layer structure. Once a new hidden unit hasbeen added to the network, its input-side weights may be frozen. Thisunit then becomes a permanent feature-detector in the network, availablefor producing outputs or for creating other, more complex featuredetectors. The cascade-correlation architecture may learn quickly,determine its own size and topology, and retain the structures it hasbuilt even if the training set changes and requires no back-propagation.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a neuro-fuzzynetwork, such as involving a fuzzy inference system in the body of anartificial neural network. Depending on the type, several layers maysimulate the processes involved in a fuzzy inference, such asfuzzification, inference, aggregation and defuzzification. Embedding afuzzy system in a general structure of a neural net as the benefit ofusing available training methods to find the parameters of a fuzzysystem.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a compositionalpattern-producing network (CPPN), such as a variation of an associativeneural network (ANN) that differs the set of activation functions andhow they are applied. While typical ANNs often contain only sigmoidfunctions (and sometimes Gaussian functions), CPPNs may include bothtypes of functions and many others. Furthermore, CPPNs may be appliedacross the entire space of possible inputs, so that they may represent acomplete image. Since they are compositions of functions, CPPNs ineffect encode images at infinite resolution and may be sampled for aparticular display at whatever resolution is optimal.

This type of network may add new patterns without re-training. Inembodiments, methods and systems described herein that involve an expertsystem or self-organization capability may use a one-shot associativememory network, such as by creating a specific memory structure, whichassigns each new pattern to an orthogonal plane using adjacentlyconnected hierarchical arrays.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchicaltemporal memory (HTM) neural network, such as involving the structuraland algorithmic properties of the neocortex. HTM may use a biomimeticmodel based on memory-prediction theory. HTM may be used to discover andinfer the high-level causes of observed input patterns and sequences.

Holographic Associative Memory

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a holographicassociative memory (HAM) neural network, which may comprise an analog,correlation-based, associative, stimulus-response system. Informationmay be mapped onto the phase orientation of complex numbers. The memoryis effective for associative memory tasks, generalization and patternrecognition with changeable attention.

In embodiments, various embodiments involving network coding may be usedto code transmission data among network nodes in a neural net, such aswhere nodes are located in one or more data collectors or machines in atransactional environment.

Integrated Circuits

In embodiments, one or more of the controllers, circuits, systems, datacollectors, storage systems, network elements, or the like as describedthroughout this disclosure may be embodied in or on an integratedcircuit, such as an analog, digital, or mixed signal circuit, such as amicroprocessor, a programmable logic controller, an application-specificintegrated circuit, a field programmable gate array, or other circuits,such as embodied on one or more chips disposed on one or more circuitboards, such as to provide in hardware (with potentially acceleratedspeed, energy performance, input-output performance, or the like) one ormore of the functions described herein. This may include setting upcircuits with up to billions of logic gates, flip-flops, multiplexers,and other circuits in a small space, facilitating high speed processing,low power dissipation, and reduced manufacturing cost compared withboard-level integration. In embodiments, a digital IC, typically amicroprocessor, digital signal processor, microcontroller, or the likemay use Boolean algebra to process digital signals to embody complexlogic, such as involved in the circuits, controllers, and other systemsdescribed herein. In embodiments, a data collector, an expert system, astorage system, or the like may be embodied as a digital integratedcircuit, such as a logic IC, memory chip, interface IC (e.g., a levelshifter, a serializer, a deserializer, and the like), a power managementIC and/or a programmable device; an analog integrated circuit, such as alinear IC, RF IC, or the like, or a mixed signal IC, such as a dataacquisition IC (including A/D converters, D/A converter, digitalpotentiometers) and/or a clock/timing IC.

With reference to FIG. 32 , the environment includes an intelligentenergy and compute facility (such as a large scale facility hosting manycompute resources and having access to a large energy source, such as ahydropower source), as well as a host intelligent energy and computefacility resource management platform, referred to in some cases forconvenience as the energy and information technology platform (withnetworking, data storage, data processing and other resources asdescribed herein), a set of data sources, a set of expert systems,interfaces to a set of market platforms and external resources, and aset of user (or client) systems and devices.

Intelligent Energy and Compute Facility

A facility may be configured to access an inexpensive (at least duringsome time periods) power source (such as a hydropower dam, a wind farm,a solar array, a nuclear power plant, or a grid), to contain a large setof networked information technology resources, including processingunits, servers, and the like that are capable of flexible utilization(such as by switching inputs, switching configurations, switchingprogramming, and the like), and to provide a range of outputs that canalso be flexibly configured (such as passing through power to a smartgrid, providing computational results (such as for cryptocurrencymining, artificial intelligence, or analytics). A facility may include apower storage system, such as for large scale storage of availablepower.

Intelligent Energy and Compute Facility Resource Management Platform

In operation, a user can access the energy and information technologyplatform to initiate and manage a set of activities that involveoptimizing energy and computing resources among a diverse set ofavailable tasks. Energy resources may include hydropower, nuclear power,wind power, solar power, grid power and the like, as well as energystorage resources, such as batteries, gravity power, and storage usingthermal materials, such as molten salts. Computing resources may includeGPUs, FPGAs, servers, chips, Asics, processors, data storage media,networking resources, and many others. Available tasks may includecryptocurrency hash processing, expert system processing, computervision processing, NLP, path optimization, applications of models suchas for analytics, etc.

In embodiments, the platform may include various subsystems that may beimplemented as micro services, such that other subsystems of the systemaccess the functionality of a subsystem providing a micro service viaapplication programming interface API. In some embodiments, the variousservices that are provided by the subsystems may be deployed in bundlesthat are integrated, such as by a set of APIs. Each of the subsystems isdescribed in greater detail with respect to FIG. 130 .

The External Data Sources can include any system or device that canprovide data to the platform. Examples of data sources can includemarket data sources (e.g., for financial markets, commercial markets(including e-commerce), advertising markets, energy markets,telecommunication markets, and many others). The energy and computingresource platform accesses external data sources via a network (e.g.,the Internet) in any suitable manner (e.g., crawlers,extract-transform-load (ETL) systems, gateways, brokers, applicationprogramming interfaces (APIs), spiders, distributed database queries,and the like).

A facility is a facility that has an energy resource (e.g., a hydropower resource) and a set of compute resource (e.g., a set of flexiblecomputing resources that can be provisioned and managed to performcomputing tasks, such as GPUs, FPGAs and many others, a set of flexiblenetworking resources that can similarly be provisioned and managed, suchas by adjusting network coding protocols and parameters), and the like.

User and client systems and devices can include any system or devicethat may consume one or more computing or energy resource made availableby the energy and computing resource platform. Examples includecryptocurrency systems (e.g., for Bitcoin and other cryptocurrencymining operations), expert and artificial intelligence systems (such asneural networks and other systems, such as for computer vision, naturallanguage processing, path determination and optimization, patternrecognition, deep learning, supervised learning, decision support, andmany others), energy management systems (such as smart grid systems),and many others. User and client systems may include user devices, suchas smartphones, tablet computer devices, laptop computing devices,personal computing devices, smart televisions, gaming consoles, and thelike.

Energy and Computing Resource Platform Components in FIG. 130.

FIG. 130 illustrates an example energy and computing resource platformaccording to some embodiments of the present disclosure. In embodiments,the energy and computing resource platform may include a processingsystem 13002, a storage system 13004, and a communication system 13006.

The processing system 13002 may include one or more processors andmemory. The processors may operate in an individual or distributedmanner. The processors may be in the same physical device or in separatedevices, which may or may not be located in the same facility. Thememory may store computer-executable instructions that are executed bythe one or more processors. In embodiments, the processing system 13002may execute the facility management system 13008, the data acquisitionsystem 13010, the cognitive processes system 13012, the lead generationsystem 13014, the content generation system 13016, and the workflowsystem 13018.

The storage system 13004 may include one or more computer-readablestorage mediums. The computer-readable storage mediums may be located inthe same physical device or in separate devices, which may or may not belocated in the same facility, which may or may not be located in thesame facility. The computer-readable storage mediums may include flashdevices, solid-state memory devices, hard disk drives, and the like. Inembodiments, the storage system 13004 stores one or more of a facilitydata store 13020, a person data store 13022, and an external data store13024.

The communication system 13006 may include one or more transceivers thatare configured to effectuate wireless or wired communication with one ormore external devices, including user devices and/or servers, via anetwork (e.g., the Internet and/or a cellular network). Thecommunication system 13006 may implement any suitable communicationprotocol. For example, the communication system 13006 may implement anIEEE 801.11 wireless communication protocol and/or any suitable cellularcommunication protocol to effectuate wireless communication withexternal devices and external data stores 13024 via a wireless network.

Energy and Computing Resource Management Platform

Discovers, provisions, manages, and optimizes energy and computeresources using artificial intelligence and expert systems withsensitivity to market and other conditions by learning on a set ofoutcomes. Discovers and facilitates cataloging of resources, optionallyby user entry and/or automated detection (including peer detection). Mayimplement a graphical user interface to receive relevant informationregarding the energy and compute resources that are available. This mayinclude a “digital twin” of an energy and compute facility that allowsmodeling, prediction, and the like. May generate a set of data recordthat define the facility or a set of facilities under common ownershipor operation by a host. The data record may have any suitable schema. Insome embodiments (e.g., FIG. 131 ), the facility data records mayinclude a facility identifier (e.g., a unique identifier thatcorresponds to the facility), a facility type (e.g., energy system andcapabilities, compute systems and capabilities, networking systems andcapabilities), facility attributes (e.g., name of the facility, name ofthe facility initiator, description of the facility, keywords of thefacility, goals of the facility, timing elements, schedules, and thelike), participants/potential participants in the facility (e.g.,identifiers of owners, operators, hosts, service providers, consumers,clients, users, workers, and others), and any suitable metadata (e.g.,creation date, launch date, scheduled requirements and the like). Maygenerate content, such as a document, message, alert, report, webpageand/or application page based on the contents of the data record. Forexample, may obtain the data record of the facility and may populate awebpage template with the data contained therein. In addition, there canbe management of existing facilities, updates the data record of afacility, determinations of outcomes (e.g., energy produced, computetasks completed, processing outcomes achieved, financial outcomesachieved, service levels met and many others), and sending ofinformation (e.g., updates, alerts, requests, instructions, and thelike) to individuals and systems.

Data Acquisition Systems can acquire various types of data fromdifferent data sources and organizes that data into one or more datastructures. In embodiments, the data acquisition system receives datafrom users via a user interface (e.g., user types in profileinformation). In embodiments, the data acquisition system can retrievedata from passive electronic sources. In embodiments, the dataacquisition system can implement crawlers to crawl different websites orapplications. In embodiments, the data acquisition system can implementan API to retrieve data from external data sources or user devices(e.g., various contact lists from user's phone or email account). Inembodiments, the data acquisition system can structure the obtained datainto appropriate data structures. In embodiments, the data acquisitionsystem generates and maintains person records based on data collectedregarding individuals. In embodiments, a person datastore stores personrecords. In some of these embodiments, the person datastore may includeone or more databases, indexes, tables, and the like. Each person recordmay correspond to a respective individual and may be organized accordingto any suitable schema.

FIG. 132 illustrates an example schema of a person record. In theexample, each person record may include a unique person identifier(e.g., username or value), and may define all data relating to a person,including a person's name, facilities they are a part of or associatedwith (e.g., a list of facility identifiers), attributes of the person(age, location, job, company, role, skills, competencies, capabilities,education history, job history, and the like), a list of contacts orrelationships of the person (e.g., in a role hierarchy or graph), andany suitable metadata (e.g., date joined, dates actions were taken,dates input was received, and the like).

In embodiments, the data acquisition system generates and maintains oneor more graphs based on the retrieved data. In some embodiments, a graphdatastore may store the one or more graphs. The graph may be specific toa facility or may be a global graph. The graph may be used in manydifferent applications (e.g., identifying a set of roles, such as forauthentication, for approvals, and the like for persons, or identifyingsystem configurations, capabilities, or the like, such as hierarchies ofenergy producing, computing, networking, or other systems, subsystemsand/or resources).

In embodiments, a graph may be stored in a graph database, where data isstored in a collection of nodes and edges. In some embodiments, a graphhas nodes representing entities and edges representing relationships,each node may have a node type (also referred to as an entity type) andan entity value, each edge may have a relationship type and may define arelationship between two entities. For example, a person node mayinclude a person ID that identifies the individual represented by thenode and a company node may include a company identifier that identifiesa company. A “works for” edge that is directed from a person node to acompany node may denote that the person represented by the edge nodeworks for the company represented by the company node. In anotherexample, a person node may include a person ID that identifies theindividual represented by the node and a facility node may include afacility identifier that identifies a facility. A “manages” edge that isdirected from a person node to a facility node may denote that theperson represented by the person node is a manager of the facilityrepresented by the facility node. Furthermore in embodiments, an edge ornode may contain or reference additional data. For example, a “manages”edge may include a function that indicates a specific function within afacility that is managed by a person. The graph(s) can be used in anumber of different applications, which are discussed with respect tothe cognitive processing system.

In embodiments, validated Identity information may be imported from oneor more identity information providers, as well as data from LinkedIn™and other social network sources regarding data acquisition andstructuring data. In embodiments, the data acquisition system mayinclude an identity management system (not shown in Figs) of theplatform may manage identity stitching, identity resolution, identitynormalization, and the like, such as determining where an individualrepresented across different social networking sites and email contactsis in fact the same person. In embodiments, the data acquisition systemmay include a profile aggregation system (not shown in Figs) that findsand aggregates disparate pieces of information to generate acomprehensive profile for a person. The profile aggregation system mayalso deduplicate individuals.

Cognitive Processing Systems

The cognitive processing system 13312 may implement one or more ofmachine learning processes, artificial intelligence processes, analyticsprocesses, natural language processing processes, and natural languagegeneration processes. FIG. 133 illustrates an example cognitiveprocessing system according to some embodiments of the presentdisclosure. In this example, the cognitive processing system may includea machine learning system 13302, an artificial intelligence (AI) system13304, an analytics system 13306, a natural language processing system13308, and a natural language generation system 13310.

Machine Learning System

In embodiments, the machine learning system may train models, such aspredictive models (e.g., various types of neural networks, regressionbased models, and other machine-learned models). In embodiments,training can be supervised, semi-supervised, or unsupervised. Inembodiments, training can be done using training data, which may becollected or generated for training purposes.

A facility output model (or prediction model) may be a model thatreceive facility attributes and outputs one or more predictionsregarding the production or other output of a facility. Examples ofpredictions may be the amount of energy a facility will produce, theamount of processing the facility will undertake, the amount of data anetwork will be able to transfer, the amount of data that can be stored,the price of a component, service or the like (such as supplied to orprovided by a facility), a profit generated by accomplishing a giventasks, the cost entailed in performing an action, and the like. In eachcase, the machine learning system optionally trains a model based ontraining data. In embodiments, the machine learning system may receivevectors containing facility attributes (e.g., facility type, facilitycapability, objectives sought, constraints or rules that apply toutilization of resources or the facility, or the like), personattributes (e.g., role, components managed, and the like), and outcomes(e.g., energy produced, computing tasks completed, and financialresults, among many others). Each vector corresponds to a respectiveoutcome and the attributes of the respective facility and respectiveactions that led to the outcome. The machine learning system takes inthe vectors and generates predictive model based thereon. Inembodiments, the machine learning system may store the predictive modelsin the model datastore.

In embodiments, training can also be done based on feedback received bythe system, which is also referred to as “reinforcement learning.” Inembodiments, the machine learning system may receive a set ofcircumstances that led to a prediction (e.g., attributes of facility,attributes of a model, and the like) and an outcome related to thefacility and may update the model according to the feedback.

In embodiments, training may be provided from a training data set thatis created by observing actions of a set of humans, such as facilitymanagers managing facilities that have various capabilities and that areinvolved in various contexts and situations. This may include use ofrobotic process automation to learn on a training data set ofinteractions of humans with interfaces, such as graphical userinterfaces, of one or more computer programs, such as dashboards,control systems, and other systems that are used to manage an energy andcompute management facility.

Artificial Intelligence (AI) Systems

In embodiments, the AI system leverages the predictive models to makepredictions regarding facilities. Examples of predictions include onesrelated to inputs to a facility (e.g., available energy, cost of energy,cost of compute resources, networking capacity and the like, as well asvarious market information, such as pricing information for end usemarkets), ones related to components or systems of a facility (includingperformance predictions, maintenance predictions, uptime/downtimepredictions, capacity predictions and the like), ones related tofunctions or workflows of the facility (such as ones that involvedconditions or states that may result in following one or more distinctpossible paths within a workflow, a process, or the like), ones relatedto outputs of the facility, and others. In embodiments, the AI systemreceives a facility identifier. In response to the facility identifier,the AI system may retrieve attributes corresponding to the facility. Insome embodiments, the AI system may obtain the facility attributes froma graph. Additionally or alternatively, the AI system may obtain thefacility attributes from a facility record corresponding to the facilityidentifier, and the person attributes from a person record correspondingto the person identifier.

Examples of additional attributes that can be used to make predictionsabout a facility or a related process of system include: relatedfacility information; owner goals (including financial goals); clientgoals; and many more additional or alternative attributes. Inembodiments, the AI system may output scores for each possibleprediction, where each prediction corresponds to a possible outcome. Forexample, in using a prediction model used to determine a likelihood thata hydroelectric source for a facility will produce 5 MW of power, theprediction model can output a score for a “will produce” outcome and ascore for a “will not produce” outcome. The AI system may then selectthe outcome with the highest score as the prediction. Alternatively, theAI system may output the respective scores to a requesting system.

Clustering Systems

In embodiments, a clustering system clusters records or entities basedon attributes contained herein. For example, similar facilities,resources, people, clients, or the like may be clustered. The clusteringsystem may implement any suitable clustering algorithm. For example,when clustering people records to identify a list of customer leadscorresponding to resources that can be sold by a facility, theclustering system may implement k-nearest neighbors clustering, wherebythe clustering system identifies k people records that most closelyrelate to the attributes defined for the facility. In another example,the clustering system may implement k-means clustering, such that theclustering system identifies k different clusters of people records,whereby the clustering system or another system selects items from thecluster.

Analytics System

In embodiments, an analytics system may perform analytics relating tovarious aspects of the energy and computing resource platform. Theanalytics system may analyze certain communications to determine whichconfigurations of a facility produce the greatest yield, what conditionstend to indicate potential faults or problems, and the like.

Lead Generation System

FIG. 134 shows the manner by which the lead generation system generatesa lead list. Lead generation system receives a list of potential leads13402 (such as for consumers of available products or resources). Thelead generation system may provide the list of leads to the clusteringsystem 13404. The clustering system clusters the profile of the leadwith the clusters of facility attributes 13406 to identify one or moreclusters. In embodiments, the clustering system returns a list of leads13410. In other embodiments, the clustering system returns the clusters13408, and the lead generation system selects the list of leads 13410from the cluster to which a prospect belongs.

FIG. 135 illustrates the manner by which the lead generation systemdetermines facility outputs for leads identified in the list of leads.In embodiments, the lead generation system provides a lead identifier ofa respective lead to the AI system (step 13502). The AI system may thenobtain the lead attributes of the lead and facility attributes of thefacility and may feed the respective attributes into a prediction model(step 13504). The prediction model outputs a prediction, which may bescores associated with each possible outcome, or a single predictedoutcome that was selected based on its respective score (e.g., theoutcome having the highest score) (step 13506). The lead generationsystem may iterate in this manner for each lead in the lead list. Forexample, the lead generation system may generate leads that areconsumers of compute capabilities, energy capabilities, predictions andforecasts, optimization results, and others.

In embodiments, the lead generation system categorizes the lead (step13508) and generates a lead list (step 13512) which it provides to thefacility operator or host of the systems, including an indicator of thereason why a lead may be willing to engage the facility, such as, forexample, that the lead is an intensive user of computing resources, suchas to forecast behavior of a complex, multi-variable market, or to minefor cryptocurrency. In embodiments, where more leads are stored and/orcategorized, the lead generation system continues checking the lead list(step 13510).

Content Generation Systems

In embodiments, a content generation system of the platform generatescontent for a contact event, such as an email, text message, or a postto a network, or a machine-to-machine message, such as communicating viaan API or a peer-to-peer system. In embodiments, the content iscustomized using artificial intelligence based on the attributes of thefacility, attributes of a recipient (e.g., based on the profile of aperson, the role of a person, or the like), and/or relating to theproject or activity to which the facility relates. The contentgeneration system may be seeded with a set of templates, which may becustomized, such as by training the content generation system on atraining set of data created by human writers, and which may be furthertrained by feedback based on outcomes tracked by the platform, such asoutcomes indicating success of particular forms of communication ingenerating donations to a facility, as well as other indicators as notedthroughout this disclosure. The content generation system may customizecontent based on attributes of the facility, a project, and/or one ormore people, and the like. For example, a facility manager may receiveshort messages about events related to facility operations, includingcodes, acronyms, and jargon, while an outside consumer of outputs fromthe facility may receive a more formal report relating to the sameevent.

FIG. 136 illustrates a manner by which the content generation system maygenerate personalized content. The content generation system receives arecipient id, a sender id (which may be a person or a system, amongothers), and a facility id (step 13602). The content generation systemmay determine the appropriate template (step 13604) to use based on therelationships among the recipient, sender, and facility and/or based onother considerations (e.g., a recipient who is a busy manager is morelikely to respond to less formal messages or more formal messages). Thecontent generation system may provide the template (or an identifierthereof) to the natural language generation system, along with therecipient id, the sender id, and the facility id. The natural languagegeneration system may obtain facility attributes based on the facilityid, and person attributes corresponding to the recipient or sender basedon their identities (step 13606). The natural language generation systemmay then generate the personalized or customized content (step 13608)based on the selected template, the facility parameters, and/or otherattributes of the various types described herein. The natural languagegeneration system may output the generated content (step 13610) to thecontent generation system.

In embodiments, a person, such as a facility manager, may approve thegenerated content provided by the content generation system and/or makeedits to the generated content, then send the content, such as via emailand/or other channels. In embodiments, the platform tracks the contactevent.

Referring to FIG. 137 , an adaptive intelligence system 13704 mayinclude an artificial intelligence system 13748, a digital twin system13720, and an adaptive device (or edge) intelligence system 13730. Theartificial intelligence system 13748 may define a machine learning model13702 for performing analytics, simulation, decision making, andprediction making related to data processing, data analysis, simulationcreation, and simulation analysis of one or more of the transactionentities. The machine learning model 13702 is an algorithm and/orstatistical model that performs specific tasks without using explicitinstructions, relying instead on patterns and inference. The machinelearning model 13702 builds one or more mathematical models based ontraining data to make predictions and/or decisions without beingexplicitly programmed to perform the specific tasks. The machinelearning model 13702 may receive inputs of sensor data as training data,including event data 13724 and state data 13772 related to one or moreof the transaction entities through data collection systems 13718 andmonitoring systems 13706 and connectivity facilities 13716. The eventdata 13724 and state data 13772 may be stored in a data storage system13710 The sensor data input to the machine learning model 13702 may beused to train the machine learning model 13702 to perform the analytics,simulation, decision making, and prediction making relating to the dataprocessing, data analysis, simulation creation, and simulation analysisof the one or more of the transaction entities. The machine learningmodel 13702 may also use input data from a user or users of theinformation technology system. The machine learning model 13702 mayinclude an artificial neural network, a decision tree, a support vectormachine, a Bayesian network, a genetic algorithm, any other suitableform of machine learning model, or a combination thereof. The machinelearning model 13702 may be configured to learn through supervisedlearning, unsupervised learning, reinforcement learning, self-learning,feature learning, sparse dictionary learning, anomaly detection,association rules, a combination thereof, or any other suitablealgorithm for learning.

The artificial intelligence system 13748 may also define the digitaltwin system 13720 to create a digital replica of one or more of thetransaction entities. The digital replica of the one or more of thetransaction entities may use substantially real-time sensor data toprovide for substantially real-time virtual representation of thetransaction entity and provides for simulation of one or more possiblefuture states of the one or more transaction entities. The digitalreplica exists simultaneously with the one or more transaction entitiesbeing replicated. The digital replica provides one or more simulationsof both physical elements and properties of the one or more transactionentities being replicated and the dynamics thereof, in embodiments,throughout the lifestyle of the one or more transaction entities beingreplicated. The digital replica may provide a hypothetical simulation ofthe one or more transaction entities, for example during a design phasebefore the one or more transaction entities are constructed orfabricated, or during or after construction or fabrication of the one ormore transaction entities by allowing for hypothetical extrapolation ofsensor data to simulate a state of the one or more transaction entities,such as during high stress, after a period of time has passed duringwhich component wear may be an issue, during maximum throughputoperation, after one or more hypothetical or planned improvements havebeen made to the one or more transaction entities, or any other suitablehypothetical situation. In some embodiments, the machine learning model13702 may automatically predict hypothetical situations for simulationwith the digital replica, such as by predicting possible improvements tothe one or more transaction entities, predicting when one or morecomponents of the one or more transaction entities may fail, and/orsuggesting possible improvements to the one or more transactionentities, such as changes to timing settings, arrangement, components,or any other suitable change to the transaction entities. The digitalreplica allows for simulation of the one or more transaction entitiesduring both design and operation phases of the one or more transactionentities, as well as simulation of hypothetical operation conditions andconfigurations of the one or more transaction entities. The digitalreplica allows for invaluable analysis and simulation of the one or moretransaction entities, by facilitating observation and measurement ofnearly any type of metric, including temperature, wear, light,vibration, etc. not only in, on, and around each component of the one ormore transaction entities, but in some embodiments within the one ormore transaction entities. In some embodiments, the machine learningmodel 13702 may process the sensor data including the event data 13724and the state data 13772 to define simulation data for use by thedigital twin system 13720. The machine learning model 13702 may, forexample, receive state data 13772 and event data 13724 related to aparticular transaction entity of the plurality of transaction entitiesand perform a series of operations on the state data 13772 and the eventdata 13724 to format the state data 13772 and the event data 13724 intoa format suitable for use by the digital twin system 13720 in creationof a digital replica of the transaction entity. For example, one or moretransaction entities may include a robot configured to augment productson an adjacent assembly line. The machine learning model 13702 maycollect data from one or more sensors positioned on, near, in, and/oraround the robot. The machine learning model 13702 may performoperations on the sensor data to process the sensor data into simulationdata and output the simulation data to the digital twin system 13720.The digital twin system 13720 simulation may use the simulation data tocreate one or more digital replicas of the robot, the simulationincluding for example metrics including temperature, wear, speed,rotation, and vibration of the robot and components thereof. Thesimulation may be a substantially real-time simulation, allowing for ahuman user of the information technology to view the simulation of therobot, metrics related thereto, and metrics related to componentsthereof, in substantially real time. The simulation may be a predictiveor hypothetical situation, allowing for a human user of the informationtechnology to view a predictive or hypothetical simulation of the robot,metrics related thereto, and metrics related to components thereof.

In some embodiments, the machine learning model 13702 and the digitaltwin system 13720 may process sensor data and create a digital replicaof a set of transaction entities of the plurality of transactionentities to facilitate design, real-time simulation, predictivesimulation, and/or hypothetical simulation of a related group oftransaction entities. The digital replica of the set of transactionentities may use substantially real-time sensor data to provide forsubstantially real-time virtual representation of the set of transactionentities and provide for simulation of one or more possible futurestates of the set of transaction entities. The digital replica existssimultaneously with the set of transaction entities being replicated.The digital replica provides one or more simulations of both physicalelements and properties of the set of transaction entities beingreplicated and the dynamics thereof, in embodiments, throughout thelifestyle of the set of transaction entities being replicated. The oneor more simulations may include a visual simulation, such as awire-frame virtual representation of the one or more transactionentities that may be viewable on a monitor, using an augmented reality(AR) apparatus, or using a virtual reality (VR) apparatus. The visualsimulation may be able to be manipulated by a human user of theinformation technology system, such as zooming or highlightingcomponents of the simulation and/or providing an exploded view of theone or more transaction entities. The digital replica may provide ahypothetical simulation of the set of transaction entities, for exampleduring a design phase before the one or more transaction entities areconstructed or fabricated, or during or after construction orfabrication of the one or more transaction entities by allowing forhypothetical extrapolation of sensor data to simulate a state of the setof transaction entities, such as during high stress, after a period oftime has passed during which component wear may be an issue, duringmaximum throughput operation, after one or more hypothetical or plannedimprovements have been made to the set of transaction entities, or anyother suitable hypothetical situation. In some embodiments, the machinelearning model 13702 may automatically predict hypothetical situationsfor simulation with the digital replica, such as by predicting possibleimprovements to the set of transaction entities, predicting when one ormore components of the set of transaction entities may fail, and/orsuggesting possible improvements to the set of transaction entities,such as changes to timing settings, arrangement, components, or anyother suitable change to the transaction entities. The digital replicaallows for simulation of the set of transaction entities during bothdesign and operation phases of the set of transaction entities, as wellas simulation of hypothetical operation conditions and configurations ofthe set of transaction entities. The digital replica allows forinvaluable analysis and simulation of the one or more transactionentities, by facilitating observation and measurement of nearly any typeof metric, including temperature, wear, light, vibration, etc. not onlyin, on, and around each component of the set of transaction entities,but in some embodiments within the set of transaction entities. In someembodiments, the machine learning model 13702 may process the sensordata including the event data 13724 and the state data 13772 to definesimulation data for use by the digital twin system 13720. The machinelearning model 13702 may, for example, receive state data 13772 andevent data 13724 related to a particular transaction entity of theplurality of transaction entities and perform a series of operations onthe state data 13772 and the event data 13724 to format the state data13772 and the event data 13724 into a format suitable for use by thedigital twin system 13720 in the creation of a digital replica of theset of transaction entities. For example, a set of transaction entitiesmay include a die machine configured to place products on a conveyorbelt, the conveyor belt on which the die machine is configured to placethe products, and a plurality of robots configured to add parts to theproducts as they move along the assembly line. The machine learningmodel 13702 may collect data from one or more sensors positioned on,near, in, and/or around each of the die machines, the conveyor belt, andthe plurality of robots. The machine learning model 13702 may performoperations on the sensor data to process the sensor data into simulationdata and output the simulation data to the digital twin system 13720.The digital twin system 13720 simulation may use the simulation data tocreate one or more digital replicas of the die machine, the conveyorbelt, and the plurality of robots, the simulation including for examplemetrics including temperature, wear, speed, rotation, and vibration ofthe die machine, the conveyor belt, and the plurality of robots andcomponents thereof. The simulation may be a substantially real-timesimulation, allowing for a human user of the information technology toview the simulation of the die machine, the conveyor belt, and theplurality of robots, metrics related thereto, and metrics related tocomponents thereof, in substantially real time. The simulation may be apredictive or hypothetical situation, allowing for a human user of theinformation technology to view a predictive or hypothetical simulationof the die machine, the conveyor belt, and the plurality of robots,metrics related thereto, and metrics related to components thereof.

In some embodiments, the machine learning model 13702 may prioritizecollection of sensor data for use in digital replica simulations of oneor more of the transaction entities. The machine learning model 13702may use sensor data and user inputs to train, thereby learning whichtypes of sensor data are most effective for creation of digitalreplicate simulations of one or more of the transaction entities. Forexample, the machine learning model 13702 may find that a particulartransaction entity has dynamic properties such as component wear andthroughput affected by temperature, humidity, and load. The machinelearning model 13702 may, through machine learning, prioritizecollection of sensor data related to temperature, humidity, and load,and may prioritize processing sensor data of the prioritized type intosimulation data for output to the digital twin system 13720. In someembodiments, the machine learning model 13702 may suggest to a user ofthe information technology system that more and/or different sensors ofthe prioritized type be implemented in the information technology nearand around the transaction entity being simulation such that more and/orbetter data of the prioritized type may be used in simulation of thetransaction entity via the digital replica thereof.

In some embodiments, the machine learning model 13702 may be configuredto learn to determine which types of sensor data are to be processedinto simulation data for transmission to the digital twin system 13720based on one or both of a modeling goal and a quality or type of sensordata. A modeling goal may be an objective set by a user of theinformation technology system or may be predicted or learned by themachine learning model 13702. Examples of modeling goals includecreating a digital replica capable of showing dynamics of throughput onan assembly line, which may include collection, simulation, and modelingof, e.g., thermal, electrical power, component wear, and other metricsof a conveyor belt, an assembly machine, one or more products, and othercomponents of the transaction ecosystem. The machine learning model137102 may be configured to learn to determine which types of sensordata are necessary to be processed into simulation data for transmissionto the digital twin system 13720 to achieve such a model. In someembodiments, the machine learning model 13702 may analyze which types ofsensor data are being collected, the quality and quantity of the sensordata being collected, and what the sensor data being collectedrepresents, and may make decisions, predictions, analyses, and/ordeterminations related to which types of sensor data are and/or are notrelevant to achieving the modeling goal and may make decisions,predictions, analyses, and/or determinations to prioritize, improve,and/or achieve the quality and quantity of sensor data being processedinto simulation data for use by the digital twin system 13720 inachieving the modeling goal.

In some embodiments, a user of the information technology system mayinput a modeling goal into the machine learning model 13702. The machinelearning model 13702 may learn to analyze training data to outputsuggestions to the user of the information technology system regardingwhich types of sensor data are most relevant to achieving the modelinggoal, such as one or more types of sensors positioned in, on, or near atransaction entity or a plurality of transaction entities that isrelevant to the achievement of the modeling goal is and/or are notsufficient for achieving the modeling goal, and how a differentconfiguration of the types of sensors, such as by adding, removing, orrepositioning sensors, may better facilitate achievement of the modelinggoal by the machine learning model 13702 and the digital twin system13720. In some embodiments, the machine learning model 13702 mayautomatically increase or decrease collection rates, processing,storage, sampling rates, bandwidth allocation, bitrates, and otherattributes of sensor data collection to achieve or better achieve themodeling goal. In some embodiments, the machine learning model 13702 maymake suggestions or predictions to a user of the information technologysystem related to increasing or decreasing collection rates, processing,storage, sampling rates, bandwidth allocation, bitrates, and otherattributes of sensor data collection to achieve or better achieve themodeling goal. In some embodiments, the machine learning model 13702 mayuse sensor data, simulation data, previous, current, and/or futuredigital replica simulations of one or more transaction entities of theplurality of transaction entities to automatically create and/or proposemodeling goals. In some embodiments, modeling goals automaticallycreated by the machine learning model 13702 may be automaticallyimplemented by the machine learning model 13702. In some embodiments,modeling goals automatically created by the machine learning model 13702may be proposed to a user of the information technology system, andimplemented only after acceptance and/or partial acceptance by the user,such as after modifications are made to the proposed modeling goal bythe user.

In some embodiments, the user may input the one or more modeling goals,for example, by inputting one or more modeling commands to theinformation technology system. The one or more modeling commands mayinclude, for example, a command for the machine learning model 13702 andthe digital twin system 13720 to create a digital replica simulation ofone transaction entity or a set of transaction entities, may include acommand for the digital replica simulation to be one or more of areal-time simulation, and a hypothetical simulation. The modelingcommand may also include, for example, parameters for what types ofsensor data should be used, sampling rates for the sensor data, andother parameters for the sensor data used in the one or more digitalreplica simulations. In some embodiments, the machine learning model13702 may be configured to predict modeling commands, such as by usingprevious modeling commands as training data. The machine learning model13702 may propose predicted modeling commands to a user of theinformation technology system, for example, to facilitate simulation ofone or more of the transaction entities that may be useful for themanagement of the transaction entities and/or to allow the user toeasily identify potential issues with or possible improvements to thetransaction entities. The system of FIG. 137 may include a transactionsmanagement platform and applications.

In some embodiments, the machine learning model 13702 may be configuredto evaluate a set of hypothetical simulations of one or more of thetransaction entities. The set of hypothetical simulations may be createdby the machine learning model 13702 and the digital twin system 13720 asa result of one or more modeling commands, as a result of one or moremodeling goals, one or more modeling commands, by prediction by themachine learning model 13702, or a combination thereof. The machinelearning model 13702 may evaluate the set of hypothetical simulationsbased on one or more metrics defined by the user, one or more metricsdefined by the machine learning model 13702, or a combination thereof.In some embodiments, the machine learning model 13702 may evaluate eachof the hypothetical simulations of the set of hypothetical simulationsindependently of one another. In some embodiments, the machine learningmodel 13702 may evaluate one or more of the hypothetical simulations ofthe set of hypothetical simulations in relation to one another, forexample by ranking the hypothetical simulations or creating tiers of thehypothetical simulations based on one or more metrics.

In some embodiments, the machine learning model 13702 may include one ormore model interpretability systems to facilitate human understanding ofoutputs of the machine learning model 13702, as well as information andinsight related to cognition and processes of the machine learning model13702, i.e., the one or more model interpretability systems allow forhuman understanding of not only “what” the machine learning model 13702is outputting, but also “why” the machine learning model 13702 isoutputting the outputs thereof, and what process led to the machinelearning models 13702 formulating the outputs. The one or more modelinterpretability systems may also be used by a human user to improve andguide training of the machine learning model 13702, to help debug themachine learning model 13702, to help recognize bias in the machinelearning model 13702. The one or more model interpretability systems mayinclude one or more of linear regression, logistic regression, ageneralized linear model (GLM), a generalized additive model (GAM), adecision tree, a decision rule, RuleFit, Naive Bayes Classifier, aK-nearest neighbors algorithm, a partial dependence plot, individualconditional expectation (ICE), an accumulated local effects (ALE) plot,feature interaction, permutation feature importance, a global surrogatemodel, a local surrogate (LIME) model, scoped rules, i.e., anchors,Shapley values, Shapley additive explanations (SHAP), featurevisualization, network dissection, or any other suitable machinelearning interpretability implementation. In some embodiments, the oneor more model interpretability systems may include a model datasetvisualization system. The model dataset visualization system isconfigured to automatically provide to a human user of the informationtechnology system visual analysis related to distribution of values ofthe sensor data, the simulation data, and data nodes of the machinelearning model 13702.

In some embodiments, the machine learning model 13702 may include and/orimplement an embedded model interpretability system, such as a Bayesiancase model (BCM) or glass box. The Bayesian case model uses Bayesiancase-based reasoning, prototype classification, and clustering tofacilitate human understanding of data such as the sensor data, thesimulation data, and data nodes of the machine learning model 13702. Insome embodiments, the model interpretability system may include and/orimplement a glass box interpretability method, such as a Gaussianprocess, to facilitate human understanding of data such as the sensordata, the simulation data, and data nodes of the machine learning model13702.

In some embodiments, the machine learning model 13702 may include and/orimplement testing with concept activation vectors (TCAV). The TCAVallows the machine learning model 13702 to learn human-interpretableconcepts, such as “running,” “not running,” “powered,” “not powered,”“robot,” “human,” “truck,” or “ship” from examples by a processincluding defining the concept, determining concept activation vectors,and calculating directional derivatives. By learning human-interpretableconcepts, objects, states, etc., TCAV may allow the machine learningmodel 13702 to output useful information related to the transactionentities and data collected therefrom in a format that is readilyunderstood by a human user of the information technology system.

In some embodiments, the machine learning model 13702 may be and/orinclude an artificial neural network, e.g. a connectionist systemconfigured to “learn” to perform tasks by considering examples andwithout being explicitly programmed with task-specific rules. Themachine learning model 13702 may be based on a collection of connectedunits and/or nodes that may act like artificial neurons that may in someways emulate neurons in a biological brain. The units and/or nodes mayeach have one or more connections to other units and/or nodes. The unitsand/or nodes may be configured to transmit information, e.g. one or moresignals, to other units and/or nodes, process signals received fromother units and/or nodes, and forward processed signals to other unitsand/or nodes. One or more of the units and/or nodes and connectionstherebetween may have one or more numerical “weights” assigned. Theassigned weights may be configured to facilitate learning, i.e.,training, of the machine learning model 13702. The weights assignedweights may increase and/or decrease one or more signals between one ormore units and/or nodes, and in some embodiments may have one or morethresholds associated with one or more of the weights. The one or morethresholds may be configured such that a signal is only sent between oneor more units and/or nodes if a signal and/or aggregate signal crossesthe threshold. In some embodiments, the units and/or nodes may beassigned to a plurality of layers, each of the layers having one or bothof inputs and outputs. A first layer may be configured to receivetraining data, transform at least a portion of the training data, andtransmit signals related to the training data and transformation thereofto a second layer. A final layer may be configured to output anestimate, conclusion, product, or other consequence of processing of oneor more inputs by the machine learning model 13702. Each of the layersmay perform one or more types of transformations, and one or moresignals may pass through one or more of the layers one or more times. Insome embodiments, the machine learning model 13702 may employ deeplearning and being at least partially modeled and/or configured as adeep neural network, a deep belief network, a recurrent neural network,and/or a convolutional neural network, such as by being configured toinclude one or more hidden layers.

In some embodiments, the machine learning model 13702 may be and/orinclude a decision tree, e.g. a tree-based predictive model configuredto identify one or more observations and determine one or moreconclusions based on an input. The observations may be modeled as one ormore “branches” of the decision tree, and the conclusions may be modeledas one or more “leaves” of the decision tree. In some embodiments, thedecision tree may be a classification tree. the classification tree mayinclude one or more leaves representing one or more class labels, andone or more branches representing one or more conjunctions of featuresconfigured to lead to the class labels. In some embodiments, thedecision tree may be a regression tree. The regression tree may beconfigured such that one or more target variables may take continuousvalues.

In some embodiments, the machine learning model 13702 may be and/orinclude a support vector machine, e.g. a set of related supervisedlearning methods configured for use in one or both of classification andregression-based modeling of data. The support vector machine may beconfigured to predict whether a new example falls into one or morecategories, the one or more categories being configured during trainingof the support vector machine.

In some embodiments, the machine learning model 13702 may be configuredto perform regression analysis to determine and/or estimate arelationship between one or more inputs and one or more features of theone or more inputs. Regression analysis may include linear regression,wherein the machine learning model 13702 may calculate a single line tobest fit input data according to one or more mathematical criteria.

In embodiments, inputs to the machine learning model 13702 (such as aregression model, Bayesian network, supervised model, or other type ofmodel) may be tested, such as by using a set of testing data that isindependent from the data set used for the creation and/or training ofthe machine learning model, such as to test the impact of various inputsto the accuracy of the model 13702. For example, inputs to theregression model may be removed, including single inputs, pairs ofinputs, triplets, and the like, to determine whether the absence ofinputs creates a material degradation of the success of the model 13702.This may assist with recognition of inputs that are in fact correlated(e.g., are linear combinations of the same underlying data), that areoverlapping, or the like. Comparison of model success may help selectamong alternative input data sets that provide similar information, suchas to identify the inputs (among several similar ones) that generate theleast “noise” in the model, that provide the most impact on modeleffectiveness for the lowest cost, or the like. Thus, input variationand testing of the impact of input variation on model effectiveness maybe used to prune or enhance model performance for any of the machinelearning systems described throughout this disclosure.

In some embodiments, the machine learning model 13702 may be and/orinclude a Bayesian network. The Bayesian network may be a probabilisticgraphical model configured to represent a set of random variables andconditional independence of the set of random variables. The Bayesiannetwork may be configured to represent the random variables andconditional independence via a directed acyclic graph. The Bayesiannetwork may include one or both of a dynamic Bayesian network and aninfluence diagram.

In some embodiments, the machine learning model 13702 may be defined viasupervised learning, i.e., one or more algorithms configured to build amathematical model of a set of training data containing one or moreinputs and desired outputs. The training data may consist of a set oftraining examples, each of the training examples having one or moreinputs and desired outputs, i.e., a supervisory signal. Each of thetraining examples may be represented in the machine learning model 13702by an array and/or a vector, i.e., a feature vector. The training datamay be represented in the machine learning model 13702 by a matrix. Themachine learning model 13702 may learn one or more functions viaiterative optimization of an objective function, thereby learning topredict an output associated with new inputs. Once optimized, theobjective function may provide the machine learning model 13702 with theability to accurately determine an output for inputs other than inputsincluded in the training data. In some embodiments, the machine learningmodel 13702 may be defined via one or more supervised learningalgorithms such as active learning, statistical classification,regression analysis, and similarity learning. Active learning mayinclude interactively querying, by the machine learning model 13702, auser and/or an information source to label new data points with desiredoutputs. Statistical classification may include identifying, by themachine learning model 13702, to which a set of subcategories, i.e.,subpopulations, a new observation belongs based on a training set ofdata containing observations having known categories. Regressionanalysis may include estimating, by the machine learning model 13702relationships between a dependent variable, i.e., an outcome variable,and one or more independent variables, i.e., predictors, covariates,and/or features. Similarity learning may include learning, by themachine learning model 13702, from examples using a similarity function,the similarity function being designed to measure how similar or relatedtwo objects are.

In some embodiments, the machine learning model 13702 may be defined viaunsupervised learning, i.e., one or more algorithms configured to builda mathematical model of a set of data containing only inputs by findingstructure in the data such as grouping or clustering of data points. Insome embodiments, the machine learning model 13702 may learn from testdata, i.e., training data, that has not been labeled, classified, orcategorized. The unsupervised learning algorithm may includeidentifying, by the machine learning model 13702, commonalities in thetraining data and learning by reacting based on the presence or absenceof the identified commonalities in new pieces of data. In someembodiments, the machine learning model 13702 may generate one or moreprobability density functions. In some embodiments, the machine learningmodel 13702 may learn by performing cluster analysis, such as byassigning a set of observations into subsets, i.e., clusters, accordingto one or more predesignated criteria, such as according to a similaritymetric of which internal compactness, separation, estimated density,and/or graph connectivity are factors.

In some embodiments, the machine learning model 13702 may be defined viasemi-supervised learning, i.e., one or more algorithms using trainingdata wherein some training examples may be missing training labels. Thesemi-supervised learning may be weakly supervised learning, wherein thetraining labels may be noisy, limited, and/or imprecise. The noisy,limited, and/or imprecise training labels may be cheaper and/or lesslabor intensive to produce, thus allowing the machine learning model13702 to train on a larger set of training data for less cost and/orlabor.

In some embodiments, the machine learning model 13702 may be defined viareinforcement learning, such as one or more algorithms using dynamicprogramming techniques such that the machine learning model 13702 maytrain by taking actions in an environment in order to maximize acumulative reward. In some embodiments, the training data is representedas a Markov Decision Process.

In some embodiments, the machine learning model 13702 may be defined viaself-learning, wherein the machine learning model 13702 is configured totrain using training data with no external rewards and no externalteaching, such as by employing a Crossbar Adaptive Array (CAA). The CAAmay compute decisions about actions and/or emotions about consequencesituations in a crossbar fashion, thereby driving teaching of themachine learning model 13702 by interactions between cognition andemotion.

In some embodiments, the machine learning model 13702 may be defined viafeature learning, i.e., one or more algorithms designed to discoverincreasingly accurate and/or apt representations of one or more inputsprovided during training, e.g. training data. Feature learning mayinclude training via principal component analysis and/or clusteranalysis. Feature learning algorithms may include attempting, by themachine learning model 13702, to preserve input training data while alsotransforming the input training data such that the transformed inputtraining data is useful. In some embodiments, the machine learning model13702 may be configured to transform the input training data prior toperforming one or more classifications and/or predictions of the inputtraining data. Thus, the machine learning model 13702 may be configuredto reconstruct input training data from one or more unknowndata-generating distributions without necessarily conforming toimplausible configurations of the input training data according to thedistributions. In some embodiments, the feature learning algorithm maybe performed by the machine learning model 13702 in a supervised,unsupervised, or semi-supervised manner.

In some embodiments, the machine learning model 13702 may be defined viaanomaly detection, i.e., by identifying rare and/or outlier instances ofone or more items, events and/or observations. The rare and/or outlierinstances may be identified by the instances differing significantlyfrom patterns and/or properties of a majority of the training data.Unsupervised anomaly detection may include detecting of anomalies, bythe machine learning model 13702, in an unlabeled training data setunder an assumption that a majority of the training data is “normal.”Supervised anomaly detection may include training on a data set whereinat least a portion of the training data has been labeled as “normal”and/or “abnormal.”

In some embodiments, the machine learning model 13702 may be defined viarobot learning. Robot learning may include generation, by the machinelearning model 13702, of one or more curricula, the curricula beingsequences of learning experiences, and cumulatively acquiring new skillsvia exploration guided by the machine learning model 13702 and socialinteraction with humans by the machine learning model 13702. Acquisitionof new skills may be facilitated by one or more guidance mechanisms suchas active learning, maturation, motor synergies, and/or imitation.

In some embodiments, the machine learning model 13702 can be defined viaassociation rule learning. Association rule learning may includediscovering relationships, by the machine learning model 13702, betweenvariables in databases, in order to identify strong rules using somemeasure of “interestingness.” Association rule learning may includeidentifying, learning, and/or evolving rules to store, manipulate and/orapply knowledge. The machine learning model 13702 may be configured tolearn by identifying and/or utilizing a set of relational rules, therelational rules collectively representing knowledge captured by themachine learning model 13702. Association rule learning may include oneor more of learning classifier systems, inductive logic programming, andartificial immune systems. Learning classifier systems are algorithmsthat may combine a discovery component, such as one or more geneticalgorithms, with a learning component, such as one or more algorithmsfor supervised learning, reinforcement learning, or unsupervisedlearning. Inductive logic programming may include rule-learning, by themachine learning model 13702, using logic programming to represent oneor more of input examples, background knowledge, and hypothesisdetermined by the machine learning model 13702 during training. Themachine learning model 13702 may be configured to derive a hypothesizedlogic program entailing all positive examples given an encoding of knownbackground knowledge and a set of examples represented as a logicaldatabase of facts.

Referring to FIG. 138 , a compliance system 13800 that facilitates thelicensing of personality rights using a distributed ledger andcryptocurrency is depicted. As used herein, personality rights may referto an entity's ability to control the use of his, her, or its identityfor commercial purposes. The term entity, as used herein, may refer toan individual or an organization (e.g., a university, a school, a team,a corporation, or the like) that agrees to license its personalityrights, unless context suggests otherwise. This may include an entity'sability to control the use of its name, image, likeness, voice, or thelike. For example, an individual exercising their personality rights forcommercial purposes may include appearing in a commercial, televisionshow, or movie, making a sponsored social media post (e.g., Instagrampost, Facebook post, Twitter tweet, or the like), having their nameappear on clothing (e.g., a jersey, t-shirts, sweatshirts, or the like)or other goods, appearing in a video game, or the like. In embodiments,individuals may refer to student athletes or professional athletes, butmay include other classes of individuals as well. While the currentdescription makes reference to the NCAA, the system may be used tomonitor and facilitate transactions relating to other individuals andorganizations. For example, the system may be used in the context ofprofessional sports, where organizations may use sponsorships and otherlicensing deals to circumvent salary caps or other league rules (e.g.,FIFA fair play rules).

In embodiments, the compliance system 13800 maintains one or moredigital ledgers that record transactions relating to the licensing ofpersonality rights of entities. In embodiments, a digital ledger may bea distributed ledger that is distributed amongst a set of computingdevices 13870, 13880, 13890 (also referred to as nodes) and/or may beencrypted. Put another way, each participating node may store a copy ofthe distributed ledger. An example of the digital ledger is a Blockchainledger. In some embodiments, a distributed ledger is stored across a setof public nodes. In other embodiments, a distributed ledger is storedacross a set of whitelisted participant nodes (e.g., on the servers ofparticipating universities or teams). In some embodiments, the digitalledger is privately maintained by the compliance system 13800. Thelatter configuration provides a more energy efficient means ofmaintaining a digital ledger; while the former configurations (e.g.,distributed ledgers) provide a more secure/verifiable means ofmaintaining a digital ledger.

In embodiments, a distributed ledger may store tokens. The tokens may becryptocurrency tokens that are transferrable to licensors and licensees.In some embodiments, a distributed ledger may store the ownership dataof each token. A token (or a portion thereof) may be owned by thecompliance system, the governing organization (e.g., the NCAA), alicensor, a licensee, a team, an institution, an individual or the like.In embodiments, the distributed ledger may store event records. Eventrecords may store information relating to events associated with theentities involved with the compliance system. For example, an eventrecord may record an agreement entered into by two parties, thecompletion of an obligation by a licensor, the distribution of funds toa licensor from a license, the non-completion of an obligation by alicensor, the distribution of funds to entities associated with thelicensee (e.g., teammates, institution, team, etc.), and the like.

In embodiments, the digital ledger may store smart contracts that governagreements between licensors and licensees. As used herein, a licenseemay be an organization or person that wishes to enter an agreement tolicense a licensor's personality rights. Examples of licensees mayinclude, but are not limited to, a car dealership that wants a starstudent athlete to appear in a print ad, a company that wants thelikeness of a licensor (e.g., an athlete and/or a team) to appear in acommercial, a video game maker that wants to use team names, teamapparel, player names and/or numbers in a video game, a shoe maker thatwants an athlete to endorse a sneaker, a television show producer thatwants an athlete to appear in the television show, or the like. Inembodiments, the compliance system 13800 generates a smart contract thatmemorializes an agreement between the individual and a licensee andfacilitates the transfer of consideration (e.g., money) when the partiesagree that the individual has performed his or her requirements as putforth in the agreement. For example, an athlete may agree to appear in acommercial on behalf of a local car dealership. The smart contract inthis example may include an identifier of the athlete (e.g., anindividual ID and/or an individual account ID), an identifier of theorganization (e.g., an organization ID and/or an organization accountID), the requirements of the individual (e.g., to appear in acommercial, to make a sponsored social media post, to appear at anautograph signing, or the like), and the consideration (e.g., a monetaryamount). In embodiments, the smart contract may include additionalterms. In embodiments, the additional terms may include an allocationrule that defines a manner by which the consideration is allocated tothe athlete and one or more other parties (e.g., agent, manager,university, team, teammates, or the like). For example, in the contextof a student athlete, a smart contract may define a split between thelicensing athlete, the athletic department of the student athlete'suniversity, and the student athlete's teammates. In a specific example,a university may have a policy that requires a player appearing in anyadvertisement to split the funds according to a 60/20/20 split, whereby60% of the funds are allocated to the student athlete appearing in thecommercial, 20% of the funds are allocated to the athletic department,and 20% of the funds are allocated to the student athlete's teammates.When a smart contract verifies that the athlete has performed his or herduties with respect to the smart contract (e.g., appeared for thecommercial), the smart contract can transfer the agreed upon amount froman account of the licensee to an account of the athlete and accounts ofany other entities that may be allocated a percentage of the funds inthe smart contract (e.g., athletic department and teammates).

In embodiments, the compliance system 13800 utilizes cryptocurrency tofacilitate the transfer of funds. In embodiments, the cryptocurrency ismined by participant nodes and/or generated by the compliance system.The cryptocurrency can be an established type of cryptocurrency (e.g.,Bitcoin, Ethereum, Litecoin, or the like) or may be a proprietarycryptocurrency. In some embodiments, the cryptocurrency is a peggedcryptocurrency that is pegged to a particular fiat currency (e.g.,pegged to the US dollar. British Pound, Euro, or the like). For example,a single unit of cryptocurrency (also referred to as a “coin”) may bepegged to a single unit of fiat currency (e.g., a US dollar). Inembodiments, a licensee may exchange fiat currency for a correspondingamount of cryptocurrency. For example, if the cryptocurrency is peggedto the dollar, the licensee may exchange an amount of US dollars for acorresponding amount of cryptocurrency. In embodiments, the compliancesystem 13800 may keep a percentage of the real-world currency as atransaction fee (e.g., 5%). For example, in exchanging $10,000, thecompliance system 13800 may distribute $9,500 dollars' worth ofcryptocurrency to an account of the licensee and may keep the $5,000dollars as a transaction fee. Once the cryptocurrency is deposited in anaccount of a licensee, the licensee may enter into transactions withindividuals.

In embodiments, the compliance system 13800 may allow organizations tocreate smart contract templates that define one or moreconditions/restrictions on the contract. For example, an organizationmay predefine the allocation between the licensee, the organization, andany other individuals (e.g., coaches, teammates, representatives).Additionally or alternatively, the organization may place minimum and/ormaximum amounts of agreements. Additionally or alternatively, theorganization may place restrictions on when an agreement can be enteredinto and/or performed. For example, players may be restricted fromappearing in commercials or advertisements during the season and/orduring exam periods. These details may be stored in an organizationdatastore 13856A Organizations may place other conditions/restrictionsin a smart contract. In these embodiments, an individual and licenseewishing to enter to an agreement must use a smart contract templateprovided by the organization to which the individual belongs. In otherwords, the compliance system 13800 may only allow an individual that hasan active relationship with an organization (e.g., plays on a team of auniversity) to participate in a smart contract if the smart contract isdefined by or otherwise approved by the organization.

In embodiments, the compliance system 13800 manages a clearinghouseprocess that approves potential licensees. Before a licensee canparticipate in agreements facilitated by the compliance system 13800,the licensee can provide information relating to the licensee. This mayinclude a tax ID number, an entity name, incorporation information(e.g., state and type), a list of key personnel (e.g., directors,executives, board members, approved decision makers, and/or the like),and any other suitable information. In embodiments, the potentiallicensee may be required to sign (e.g., eSign or wet ink signature) adocument indicating that the organization will not willingly use thecompliance system 13800 to circumvent any rules, laws, or regulations(e.g., they will not circumvent NCAA regulations). In embodiments, thecompliance system 13800 or another entity (e.g., the NCAA) may verifythe licensee. Once verified, the information is stored in a licenseedatastore 13856B and the licensee may participate in transactions.

In embodiments, the compliance system 13800 may create accounts forlicensors once they have joined an organization (e.g., signed anathletic scholarship with a university). Once a licensor is verified asbeing affiliated with the organization, the compliance system 13800 maycreate an account for the licensor and may create a relationship betweenthe individual and the organization, whereby the licensor may berequired to use smart contracts that are approved or provided by theorganization. Should the licensor join another organization (e.g.,transfers to another school), the compliance system 13800 may sever therelationship with the previous organization and may create a newrelationship with the other organization. Similarly, once a licensor isno longer affiliated with any organization (e.g., the player graduates,enters a professional league, retires, or the like), the compliancesystem 13800 may prevent the licensor from participating in transactionson the compliance system 13800.

In embodiments, the compliance system 13800 may provide a graphical userinterface that allows users to create smart contracts governingpersonality rights licenses. In these embodiments, the compliance systemallows a user (e.g., a licensor) to select a smart contract template. Insome embodiments, the compliance system 13800 may restrict the user toonly select a smart contract template that is associated with aninstitution of the licensor. In embodiments, the graphical userinterface allows a user to define certain terms (e.g., the type or typesof obligations placed on the licensor, an amount of funds to paid, adate by which the obligations of the licensor must be completed by, alocation at which the obligation is completed, and/or other suitableterms). Upon a user providing input for parameterizing a smart contracttemplate, the compliance system 13800 may generate a smart contract byparameterizing one or more variables in the smart contract with theprovided input. Upon parameterizing an instance of a smart contract, thecompliance system 13800 may deploy the smart contract. In someembodiments, the compliance system 13800 may deploy the smart contractby broadcasting the parameterized smart contract to the participantnodes, which in turn may update each respective instance of thedistributed ledger with the new smart contract. In some embodiments, aninstitution of the licensor must approve the parameterized smartcontract before the parameterized smart contract may be deployed to thedistributed ledger.

In embodiments, the compliance system 13800 may provide a graphical userinterface to verify performance of an obligation by a licensor. In someof these embodiments, the compliance system 13800 may include anapplication that is accessed by licensors, that allows a licensor toprove that he or she performed an obligation. In some of theseembodiments, the application may allow a user to record locations thatthe licensor went to (e.g., locations of film or photo shoots), toupload records (e.g., screen shots of social media posts) or to provideother corroborating evidence that the licensor has performed his or herobligations with respect to a licensing transaction. In this way, thelicensor can prove that he or she performed the tasks required by thelicensing deal. In some embodiments, the application may interact with awearable device or may capture other digital exhaust, such as socialmedia posts of the user (e.g., licensor) to collect evidence thatsupports or disproves a licensor's claim that he or she performed theobligations under the transaction agreement. In embodiments, thecorroborating evidence collected by the application may be recorded bythe application and stored on the distributed ledger as a licensordatastore 13856C.

In embodiments, the compliance system 13800 (or a smart contract issuedin connection with the compliance system 13800) may completetransactions pertaining to a smart contract governing the licensing ofthe personality rights of a licensor upon verification that licensor hasperformed his or her obligations defined in the agreement. As mentioned,the licensor may use an application to provide evidence of satisfactionof the obligations of the agreement. Additionally or alternatively, thelicensee may provide verification that the licensor has performed his orher obligations (e.g., using an application). In embodiments, the smartcontract governing the agreement may receive verification that thelicensor has performed his or her obligations defined by the agreement.In response the smart contract may release (or initiate the release of)the cryptocurrency amount defined in the smart contract. Thecryptocurrency amount may be distributed to the accounts of the licensorand any other parties defined in the agreement (e.g., teammates of thelicensor, the program of the licensor, the regulating body, or thelike).

In embodiments, the compliance system 13800 is configured to performanalytics and provide reports to a regulatory body and/or other entities(e.g., the other organizations). In these embodiments, the analytics maybe used to identify individuals that are potentially circumventing therules and regulations of the regulatory body. Furthermore, in someembodiments, transaction records may be maintained on a distributedledger, whereby different organizations may be able to view agreementsentered into by individuals affiliated with other organizations suchthat added levels of transparency and oversight may disincentivizeindividuals, organizations, and/or licensees from circumventing rulesand regulations.

In embodiments, the compliance system 13800 may train and/or leveragemachine-learned models to identify potential instances of circumventionof rules or regulations. In these embodiments, the compliance system13800 may train machine-learned models using outcome data. Examples ofoutcome data may include data relating to a set of transactions where anorganization (e.g., a team or university), licensee (e.g., a company),and/or licensor (e.g., an athlete) were determined to be circumventingrules or regulations and data relating to a set of transactions where anorganization, licensee, and/or licensor were found to be in compliancewith the rules and regulations. Examples of machine-learned modelsinclude neural networks, regression-based models, decisions trees,random forests, Hidden Markov Models, Bayesian Models, and the like. Inembodiments, the compliance system 13800 may leverage a machine-learnedmodel by obtaining a set of records relating to transactions a licensee,a licensor, and/or an organization (e.g., a team or university) from thedistributed ledger. The compliance system may extract relevant features,such as the amount paid to a particular licensor by a licensee, amountspaid to other licensors on other teams, affiliations of the licensor,amounts paid to a licensor by other licensees, and the like, and mayfeed the features to the machine-learned model. The machine-learnedmodel may issue a score that indicates a likelihood that the transactionwas legitimate (or illegitimate) based on the extracted features. Inembodiments, the compliance system 13800 may provide notifications torelevant parties (e.g., regulators) when the output of a machine-learnedmodel indicates that a transaction was likely illegitimate.

FIG. 139 illustrates an example system 13900 configured forelectronically facilitating licensing of one or more personality rightsof a licensor, in accordance with some embodiments of the presentdisclosure. In some embodiments, the system 13900 may include one ormore computing platforms 13902. Computing platform(s) 13902 may beconfigured to communicate with one or more remote platforms 13904according to a client/server architecture, a peer-to-peer architecture,and/or other architectures. Remote platform(s) 13904 may be configuredto communicate with other remote platforms via computing platform(s)13902 and/or according to a client/server architecture, a peer-to-peerarchitecture, and/or other architectures. Users may access system 13900via remote platform(s) 13904.

In embodiments, computing platform(s) 13902 may be configured bymachine-readable instructions 13906. Machine-readable instructions 13906may include one or more instruction modules. The instruction modules mayinclude computer program modules. The instruction modules may includeone or more of an access module 13108, a fund management module 13112, aledger management module 13116, a verification module 13118, ananalytics module 13120, and/or other instruction modules.

In embodiments, the access module 13108 may be configured to receive anaccess request from a licensee to obtain approval to license personalityrights from a set of available licensors. In embodiments, the accessmodule 13108 may be configured to selectively grant access to thelicensee based on the access request. For example, the access module13108 may receive a name of a potential licensee (e.g., corporate name),a list of principals (e.g., executives and/or owners) of the potentiallicensee, a location of the licensee, affiliations of the licensee andthe principals thereof, and the like. In embodiments, the access module13108 may provide this information to a human that grants access and/ormay feed this information into an artificial intelligence system thatvets potential licensees. In embodiments, the access module 13108 isconfigured to selectively grant access to a licensor by verifying thatthe licensee is permitted to engage with a set of licensors includingthe licensor based on the set of affiliations. Selectively grantingaccess to the licensor may include, in response to verifying that thelicensee is permitted to engage with the set of licensors, granting thelicensee approval to engage with the set of licensees. The set ofaffiliations of the licensee may include organizations to which thelicensee or a principal associated with the licensee donates to or owns.

In embodiments, the fund management module 13112 may be configured toreceive confirmation of a deposit of an amount of funds from thelicensee. In some embodiments, the fund management module 13112 may beconfigured to issue an amount of cryptocurrency corresponding to theamount of funds deposited by the licensee to an account of the licensee.In embodiments, the fund management module 13112 may be configured toescrow the consideration amount of cryptocurrency from the account ofthe licensee until the funds are released by a smart contract.

In embodiments, the ledger management module 13116 may be configured toreceive a smart contract request to create a smart contract governingthe licensing of the one or more personality rights of the licensor bythe licensee. In embodiments, the ledger management module 13116 may beconfigured to generate the smart contract based on the smart contractrequest. The smart contract may be generated using a smart contracttemplate provided by an interested third party (e.g., a university, agoverning body, or the like) and by one or more parameters provided by auser (e.g., the licensor, the team of the licensor, an institution,and/or licensee) By way of non-limiting example, the interested thirdparty may be one of a university, a sports team, or a collegiateathletics governance organization. The smart contract request mayindicate one or more terms including a consideration amount ofcryptocurrency to be paid to the licensor in exchange for one or moreobligations on the licensor. In embodiments, the ledger managementmodule 13116 may be configured to deploy the smart contract to adistributed ledger. The distributed ledger may be auditable by a set ofthird parties, including the interested third party. The distributedledger may be a public ledger. The distributed ledger may be a privateledger that is only hosted on computing devices associated withinterested third parties. In embodiments, the distributed ledger may bea blockchain.

In embodiments, the verification module 13118 may be configured toverify that the licensor has performed the one or more obligation. Insome embodiments, verifying that a licensor has performed the one ormore obligations may include receiving location data from a wearabledevice associated with the licensor and verifying that the licensor hasperformed the one or more obligations based on the location data,whereby the location may be used to show that the licensor was at aparticular location at a particular time (e.g., a photoshoot or afilming). In embodiments, verifying that the licensor may have performedthe one or more obligations includes receiving social media data from asocial media website and verifying that the licensor has performed theone or more obligations based on the social media data, whereby thesocial media data may be used to show that the licensor has made arequired social media posting. In embodiments, verifying that thelicensor may have performed the one or more obligations includesreceiving media content from an external data source and verifying thatthe licensor has performed the one or more obligations based on themedia content, whereby a licensor and/or licensee may upload the mediacontent to prove that the licensor has appeared in the media content. Byway of non-limiting example, the media content may be one of a video, aphotograph, or an audio recording. In embodiments, the verificationmodule 13118 may generate and output an event record to theparticipating nodes upon verifying that a licensor has performed itsobligations. In embodiments, the verification module 13118 may generateand output an event record to the participating nodes that indicatesthat the compliance system 13100 has received corroborating evidence(e.g., social media data, location data, and/or media contents) thatshow that the licensor has performed his or her obligations. Inembodiments, the verification module 13118 may be configured to outputan event record indicating completion of a licensing transaction definedby the smart contract to the distributed ledger.

In embodiments, the verification module 13118 may be configured toverify, by the smart contract, that the licensor has performed the oneor more obligations. In embodiments, the verification module 13118and/or a smart contract may be configured to, in response to receivingverification that the licensor has performed the one or moreobligations, release at least a portion of the consideration amount ofcryptocurrency into a licensor account of the licensor. Releasing the atleast a portion of the consideration amount of cryptocurrency into alicensee account of the licensee may include identifying an allocationsmart contract associated with the licensee and distributing theconsideration amount of the cryptocurrency in accordance with theallocation rules. By way of non-limiting example, the additionalentities may include one or more of teammates of the licensor, coachesof the licensor, a team of the licensor, a university of the licensee,and a governing body (e.g., the NCAA).

In embodiments, an analytics module 13120 may be configured to obtain aset of records indicating completion of a set of respective transactionsfrom the distributed ledger. The set of records may include the recordindicating the completion of the transaction defined by the smartcontract. In embodiments, the analytics module 13120 may be configuredto determine whether an organization associated with the licensor islikely in violation of one or more regulations based on the set ofrecords and a fraud detection model. The fraud detection model may betrained using training data that indicates permissible transactions andfraudulent transactions.

In some implementations, the allocation smart contract may defineallocation rules governing a manner by which funds resulting fromlicensing the one or more personality rights are to be distributedamongst the licensor and one or more additional entities.

In some implementations, by way of non-limiting example, the regulationsmay be provided by one of NCAA, FIFA, NBA, MLB, NFL, MLS, NHL, and thelike.

In some implementations, computing platform(s) 13902, remote platform(s)13904, and/or external resources 13934 may be operatively linked via oneor more electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which computing platform(s)13902, remote platform(s) 13904, and/or external resources 13934 may beoperatively linked via some other communication media.

A given remote platform 13904 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given remote platform 13904 to interface with compliance system13100 and/or external resources 13934, and/or provide otherfunctionality attributed herein to remote platform(s). 13904. By way ofnon-limiting example, a given remote platform 13904 and/or a givencomputing platform 13902 may include one or more of a server, a desktopcomputer, a laptop computer, a handheld computer, a tablet computingplatform, a Netbook, a Smartphone, a gaming console, and/or othercomputing platforms.

External resources 13934 may include sources of information outside ofcompliance system 13100, external entities participating with compliancesystem 13100, and/or other resources. In some implementations, some orall of the functionality attributed herein to external resources 13934may be provided by resources included in compliance system 13100.

Computing platform(s) 202 may include electronic storage 13936, one ormore processors 13938, and/or other components. Computing platform(s)1202 may include communication lines, or ports to enable the exchange ofinformation with a network and/or other computing platforms.Illustration of computing platform(s) 13902 in FIG. 139 is not intendedto be limiting. Computing platform(s) 13902 may include a plurality ofhardware, software, and/or firmware components operating together toprovide the functionality attributed herein to computing platform(s)13902. For example, computing platform(s) 13902 may be implemented by acloud of computing platforms operating together as computing platform(s)13902.

Electronic storage 13936 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 13936 may include one or both of system storage thatis provided integrally (i.e., substantially non-removable) withcomputing platform(s) 13902 and/or removable storage that is removablyconnectable to computing platform(s) 13902 via, for example, a port(e.g., a USB port, a firewire port, etc.) or a drive (e.g., a diskdrive, etc.). Electronic storage 13936 may include one or more ofoptically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. Electronic storage13936 may include one or more virtual storage resources (e.g., cloudstorage, a virtual private network, and/or other virtual storageresources). Electronic storage 13936 may store software algorithms,information determined by processor(s) 13938, information received fromcomputing platform(s) 13902, information received from remoteplatform(s) 13904, and/or other information that enables computingplatform(s) 13902 to function as described herein.

Processor(s) 13938 may be configured to provide information processingcapabilities in computing platform(s) 13902. As such, processor(s) 13938may include one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information. Althoughprocessor(s) 13938 is shown in FIG. 139 as a single entity, this is forillustrative purposes only. In some implementations, processor(s) 13938may include a plurality of processing units. These processing units maybe physically located within the same device, or processor(s) 13938 mayrepresent processing functionality of a plurality of devices operatingin coordination. Processor(s) 13938 may be configured to execute modules13108, 13112, 13116, 13118, 13120, and/or other modules. Processor(s)13938 may be configured to execute modules 13108, 13112, 13116, 13118,13120, and/or other modules by software; hardware; firmware; somecombination of software, hardware, and/or firmware; and/or othermechanisms for configuring processing capabilities on processor(s)13938. As used herein, the term “module” may refer to any component orset of components that perform the functionality attributed to themodule. This may include one or more physical processors duringexecution of processor readable instructions, the processor readableinstructions, circuitry, hardware, storage media, or any othercomponents.

It should be appreciated that although modules 13108, 13112, 13116,13118, and 13120 are illustrated in FIG. 139 as being implemented withina single processing unit, in implementations in which processor(s) 13938includes multiple processing units, one or more of modules 13108, 13112,13116, 13118, and 13120 may be implemented remotely from the othermodules. The description of the functionality provided by the differentmodules 13108, 13112, 13116, 13118, and 13120 described below is forillustrative purposes, and is not intended to be limiting, as any ofmodules 13108, 13112, 13116, 13118, and/or 13120 may provide more orless functionality than is described. For example, one or more ofmodules 13108, 13112, 13116, 13118, and/or 13120 may be eliminated, andsome or all of its functionality may be provided by other ones ofmodules 13108, 13112, 13116, 13118, and/or 13120. As another example,processor(s) 13938 may be configured to execute one or more additionalmodules that may perform some or all of the functionality attributedbelow to one of modules 13108, 13112, 13116, 13118, and/or 13120.

FIGS. 140 and/or 141 illustrates an example method 14000 forelectronically facilitating licensing of one or more personality rightsof a licensor, in accordance with some embodiments of the presentdisclosure. The operations of method 14000 presented below are intendedto be illustrative. In some embodiments, method 14000 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of method 14000 are illustrated inFIGS. 140 and/or 141 and described below is not intended to be limiting.

In some implementations, method 14000 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 14000 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 14000.

FIG. 140 illustrates method 14000, in accordance with one or moreimplementations of the present disclosure.

At 14002, the method includes receiving an access request from alicensee to obtain approval to license personality rights from a set ofavailable licensors. Operation 14002 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to access module13108, in accordance with one or more implementations.

At 14004, the method includes selectively granting access to thelicensee based on the access request. Operation 14004 may be performedby one or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to accessmodule 13108, in accordance with one or more implementations.

At 14006, the method includes receiving confirmation of a deposit of anamount of funds from the licensee. Operation 14006 may be performed byone or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to fundmanagement module 13112, in accordance with one or more implementations.

At 14008, the method includes issuing an amount of cryptocurrencycorresponding to the amount of funds deposited by the licensee to anaccount of the licensee. Operation 14008 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to fund managementmodule 13112, in accordance with one or more implementations.

FIG. 141 illustrates method 14100, in accordance with one or moreimplementations of the present disclosure.

At 14122, the method includes receiving a smart contract request tocreate a smart contract governing the licensing of the one or morepersonality rights of the licensor by the licensee. The smart contractrequest may indicate one or more terms including a consideration amountof cryptocurrency to be paid to the licensor in exchange for one or moreobligations on the licensor. Operation 14122 may be performed by one ormore hardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to the ledgermanagement module 13116, in accordance with one or more implementations.

At 14124, the method includes generating the smart contract based on thesmart contract request. Operation 14124 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to ledger managementmodule 13116, in accordance with one or more implementations.

At 14126, the method includes escrowing the consideration amount ofcryptocurrency from the account of the licensee. Operation 14126 may beperformed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to fund management module 13112, in accordance with one or moreimplementations.

At 14128, the method includes deploying the smart contract to adistributed ledger. Operation 14128 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to ledger managementmodule 13116, in accordance with one or more implementations.

At 14130, the method includes verifying, by the smart contract, that thelicensor has performed the one or more obligations. Operation 14130 maybe performed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to verification module 13118, in accordance with one or moreimplementations.

At 14132, the method includes in response to receiving verification thatthe licensor has performed the one or more obligations, releasing atleast a portion of the consideration amount of cryptocurrency into alicensor account of the licensor. Operation 14132 may be performed byone or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to theverification module 13118, in accordance with one or moreimplementations.

At 14134, the method includes outputting a record indicating acompletion of a licensing transaction defined by the smart contract tothe distributed ledger. Operation 14134 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to the verificationmodule 13118 and/or the ledger management module 13116, in accordancewith one or more implementations.

FIG. 142 illustrates method 14200, in accordance with one or moreimplementations.

At 14202, the method includes obtaining a set of records indicatingcompletion of a set of respective transactions from the distributedledger. The set of records may include the record indicating thecompletion of the transaction defined by the smart contract. Operation14202 may be performed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to the analytics module 13120, in accordance with one or moreimplementations.

At 14204, the method includes determining whether an organizationassociated with the licensor is likely in violation of one or moreregulations based on the set of records and a fraud detection model.Operation 14204 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to the analytics module 13120, in accordance withone or more implementations.

Referring to FIG. 143 , a computer-implemented method 14300 forselecting an AI solution for use in a robotic or automated process isdepicted. The computer-implemented method may include receiving one ormore functional media 14302. The functional media may includeinformation indicative of brain activity of a worker engaged in a taskto be automated. The functional media may be functional imaging, such anMRI, an FMRI, and the like from which an area of neocortex activity maybe identified. The functional media may be an image, a video stream, anaudio stream, and the like, from which a type of brain activity may beinferred. The functional media may be acquired while the worker isperforming the work or while performing a simulation of the work, forexample in an augmented reality, a virtual reality environment, or on amodel of the equipment and/or environment. After being received, thefunctional media(s) are analyzed 14304 to identify an activity level inat least one brain region 14306. Based on the activity level, a brainregion parameter and/or an activity parameter are identified 14308. Thebrain region parameter may represent a specific region of the neocortexsuch as frontal, parietal, occipital, and temporal lobes of theneocortex, including primary visual cortex and the primary auditorycortex, or subdivisions of the neocortex, including ventrolateralprefrontal cortex (Broca's area), and orbitofrontal cortex. The activityparameter may represent functional areas of the brain, such as visualprocessing, inductive reasoning, audio processing, olfactory processing,muscle control, and the like. An activity parameter may berepresentative of a type of activity in which the worker is engaged suchas visual processing (looking) audio processing (listening), olfactoryprocessing (smelling), motion activity, listening to the sound of theequipment, watching another negotiator, and the like. An activity levelmay be representative of a strength or level of activity, such as anextent of the brain region involved, a signal strength, whether a brainregion is engaged or unengaged, and the like.

Based on one or more of the brain region parameter, the activityparameter, or the activity level, an action parameter may be identified14310. An action parameter may provide additional information regardingthe activity parameter. For example, activity parameter is indicative ofmotion, an action parameter may describe a range of motion, a speed ofmotion, a repetition of motion, a use of muscle memory, a smoothness ofmotion, a flow of motion, a timing of motion, and the like. Based on oneor more of the brain region parameter, the activity parameter, or theactivity level, a component to be incorporated in the final AI solutionmay be selected 14312. The component may include one or more of a model,an expert system, a neural network, and the like. After the componentfor the AI solution has been selected, configuration parameters may bedetermined 14314. The configuration parameters may be based, in part, onthe type of component selected, the brain region parameter, the activityparameter, the activity level, or the action parameter. Configuring andconfiguration parameters may include selecting an input for a machinelearning process, identifying an output to be provided by the machinelearning process, identifying an input for an operational solutionprocess 14316, identifying an output an operational solution process,tuning a learning parameter, identifying a change rates, identifying aweighting factor, identifying a parameter for inclusion, identifying aparameter for exclusion of a parameter, setting a threshold for inputdata, setting an output threshold for the operational robotic process,or setting a parameter threshold. Additionally, analysis of thefunctional media 14304 may include identifying a second brain regionparameter or a second activity parameter 14318. The component of the AIsolution may be revised 14320 based on the second brain region parameteror the second activity parameter. A second component of the AI solutionmay be selected 14322 based on the second brain region parameter or thesecond activity parameter. The final AI solution may be assembled fromthe component 14324 or the second component 14326. In embodiments, thefinal AI solution may be assembled from the component and the secondcomponents, optionally along with any standard or mandatory componentsthat enable operation.

Referring to FIG. 144 , a computer-implemented method 14400 forselecting an AI solution for use in a robotic or automated process isdepicted. The method may include receiving a user-related input 14402comprising a timestamp and analyzing the user-related input 14404. Theuser-related input may include an audio feed, a motion sensor, a videofeed, a heartbeat monitor, an eye tracker, a biosensor (e.g., galvanicskin response), and the like. The analysis may enable the identificationof a series of user actions and associated activity parameters 14406. Acomponent for an AI solution may be selected based on a user action ofthe series of user actions 14408. The analysis may enable theidentification of a second user action of the series of user actions14410. Based on the second user action, the selected component for theAI solution may be revised 14412. A second component for the AI solutionmay be selected 14414 based on the second user action. An actionparameter may be identified 14416 based on the user action and/or theassociated activity parameters. For example, if the user action ismotion, an action parameter may include a range of motion, a speed ofmotion, a repetition of motion, a use of muscle memory, a smoothness ofmotion, a flow of motion, a timing of motion, and the like. The selectedcomponent of the AI solution may be configured 14418 based on the actionparameter. In embodiments, at least one device input performed by theuser may be received (14420). The device input may be synchronized withthe user actions based on the timestamp and a correlation between thedevice input and the user action determined 14419. The component may berevised 14423 based on the correlation. The selection of the componentof the AI solution may be partially based on the correlation between thedevice input and the user-related input 14421. The AI solution may beassembled 14422 from the component. The AI solution may be assembledfrom the second component 14424. In embodiments, the AI may be assembledfrom both the component and the second component, optionally along withany standard or mandatory components that enable operation.

Referring to FIG. 145 , an illustrative and non-limiting example of anassembled AI solution 14502 is shown. The assembled AI solution 14502may include the selected component 14504 and a second selected component14506, as well as other components 14508. Configuration data 14514 forthe first selected component and configuration data 14512 for the secondselected component may be provided. Runtime input data 14510 may bespecified as part of the component configuration process. Components maybe structured to run serially (such as the selected component 14504 andthe second selected component 14506 which received input from theselected component 14504) or in parallel (such as the second component14506 and the other component(s) 14508). Some of the components mayprovide input for other components (such as the selected component 14504providing input to the second selected component 14506). Multiplecomponents may provide various portions of the overall AI solutionoutput 14518 (such as the second selected component 14506 and the othercomponents 14508). This depiction is not meant to be limiting and thefinal solution may include a varying number of components, configurationdata and input, as well as other components (e.g., sensors, voicemodulators, and the like) and may be interconnected in a variety ofconfigurations.

Referring to FIGS. 146-147 , a computer-implemented method for selectingan AI solution for use in a robotic or automated process is depicted.The method may include receiving temporal biometric measurement data14602 of a worker performing a task and receiving spatial-temporalenvironmental data 14604 experienced by the worker performing the task.Using the received data, a spatial-temporal activity pattern may beidentified 14606. Based on the spatial-temporal activity pattern, anactive area of the worker's neocortex may be identified 14608. A type ofreasoning used when performing the task may be identified 14610 based onthe active area of the neocortex and/or the biometric measurement data,or the spatial-temporal environmental data. A component may be selected14612 for use in the AI solution to replicate the type of reasoning. Thecomponent of the AI solution may be configured 14614 based on thespatial-temporal environmental input. A determination may be made as towhether a serial or parallel AI solution is optimal 14616. A set ofconfiguration inputs to the component may be identified 14618 and anordered set of inputs to the component of the AI solution may beidentified 14620. Training the machine may include providing varioussubsets of the spatial-temporal environmental input to determineappropriate input weightings and identify efficiencies from combinationsof spatial-temporal environmental input 14622. Desirable or undesirablecombinations of the spatial-temporal environmental data may also beidentified 14624. Based on the identified required input, inputenvironmental data may be processed to reduce input noise 14626 (e.g.improve signal to noise for a signal of interest), filtered to providethe appropriate input signals to the component, and the like.

Continuing with reference to FIG. 147 , a second temporal biometricmeasurement data of the same worker performing the task may be received14702 and a plurality of performed tasks identified from the biometricmeasurements 14704. A performance parameter may be extracted from thebiometric measurements 14706 (e.g. worker heartrate, galvanic skinresponse, and the like). In some embodiments, the component may beconfigured based on the performance parameter 14707. In someembodiments, the second temporal biometric measurements may be providedto the configuration module as a training set 14709. Results datarelated to the task may be received 14708 and the second temporalbiometric measurement data may be correlated with the received resultsdata 14710. In some embodiments, the component may be selected based, atleast in part, on the correlation 14711. A series of time intervalsbetween each of the plurality of performed tasks may be identified 14712and the component of the AI solution configured based on at least one ofthe time intervals 14714. For example, if the worker inspects an objectfor a long period of time before moving on to the next action, this mayindicate complex visual processing as well as mental processing and mayindicate that the corresponding component for the task be configured forin-depth, fine detail processing and the like.

Referring to FIG. 148 , an AI solution selection and configurationsystem 14802 is depicted. An example selection and configuration system14802 may include a media input module 14804 structured to receiveuser-related functional media 14814. The user-related functional media14814 may include images of a person engaged in a task to be automated,audio recordings, video feeds, biometric data (e.g., heartbeat data,galvanic skin response data, and the like), motion data, and the like. Amedia analysis module 14806 may analyze the received media and identifyan action parameter. The action parameter may be representative of atype of activity in which the person appears to be engaged such aswatching, listening, moving, thinking, and the like. In someembodiments, the functional media is indicative of a type of brainactivity of a human engaged in the task to be automated and the mediaanalysis module 148206 identifies an activity level in at least onebrain region and provide a brain region parameter corresponding with theactivity level in the identified brain region. The media analysis modulemay also identify an activity parameter indicative of a level ofengagement such as engaged, unengaged, level of activity, type ofactivity, and the like. A solution selection module 14808 may bestructured to select at least one component of the AI solution for usein the automated process based, at least in part, on the actionparameter, the brain region parameter, or the activity parameter. Thebrain region parameter or the action parameter may suggest a type ofcomponent to select and the activity parameter may suggest a level ofprocessing required for that component. For example, an action parameterof watching would suggest selecting a component suited to visualprocessing. If the activity parameter was representative of olfactoryprocession, the input specification module may identify at least onechemical sensor as an input. If the activity parameter is representativeof visual processing the input specification module 13116 may identifyat least one visual sensor as a robotic input. In some embodiments, thevisual sensor may be selected to be sensitive to a portion of thevisible spectrum with wavelengths between about 380 to 700 nanometers.If the activity parameter is representative of auditory processing, theinput specification module 13116 may identify at least one microphone asa robotic input. If the activity parameter was representative of a veryhigh level of concentration, the solution selection module 14808 maysuggest a level of processing that will be required, where theprocessing might occur, and the like. A component configuration module14810 may configure the component 14812. Configuring the component mayinclude: selecting an input for a machine learning process for theselected component, identifying an output to be provided by the machinelearning process, identifying an input for an operational solutionprocess, identifying an output an operational solution process, tuning alearning parameter, identifying a change rates, identifying a weightingfactor, identifying a parameter for inclusion, identifying a parameterfor exclusion of a parameter, setting a threshold for input data,setting an output threshold for the operational robotic process, settinga parameter threshold, and the like. A solution assembly module 14818may assemble the final AI solution based on one or more selectedcomponents, configuration components, and required runtime. An inputspecification module 14816 may suggest input sources based on theselected component, the action parameter, brain region parameter,activity parameter, or the like.

Referring to FIG. 149 , an AI solution selection and configurationsystem 14902 is depicted. An example selection system 14902 may includean image input module 14904 structured to receive functional images14914 of the brain such as, such as functional Mill or other magneticimaging, electroencephalogram (EEG), or other imaging, such as byidentifying broad brain activity (e.g., wave bands of activity, such asdelta, theta, alpha and gamma waves), by identifying a set of brainregions that are activated and/or inactive while the worker isperforming one of the tasks to be automated. The image input module14904 may provide a subset of the functional images 14914 to the imageanalysis module 14906. In some embodiments, the image input module 14904may perform some preprocessing for the subset of functional images14914, such as noise reduction, histogram adjustment, filtering, and thelike, prior to providing the subset of functional images 14914 to theimage analysis module 14906. The image analysis module 14906, mayidentify an activity level in at least one brain region and provide abrain region parameter based on the subset of functional images. Thebrain region parameter may represent a specific region of the neocortexsuch as frontal, parietal, occipital, and temporal lobes of theneocortex, including primary visual cortex and the primary auditorycortex, or subdivisions of the neocortex, including ventrolateralprefrontal cortex (Broca's area), and orbitofrontal cortex. The brainregion parameter may represent functional areas of the brain, such asvisual processing, inductive reasoning, audio processing, olfactoryprocessing, muscle control, and the like. A solution selection module14908 may select a component for use in an AI solution based on thebrain region parameter, and provide input into a component configurationmodule (such as selecting an input for a machine learning process,identifying an output to be provided by the machine learning process,identifying an input for an operational solution process, identifying anoutput an operational solution process, tuning a learning parameter,identifying a change rates, identifying a weighting factor, identifyinga parameter for inclusion, identifying a parameter for exclusion of aparameter, setting a threshold for input data, setting an outputthreshold for the operational robotic process, and setting a parameterthreshold, and the like. The component configuration module 14910, mayuse the input to configure the component 14912. The solution selectionmodule 14908 may also supply data to the input specification module14916. A solution assembly module 14918 may combine the component, andother components, to create the AI solution. The AI solution may be setup to receive inputs as specified by the input specification module14916. Although one iteration of selecting a component is shown in thisfigure, it is envisioned, that multiple components may be selected,configured, and assembled as part of the AI solution

Referring to FIGS. 150-151 , an AI solution selection and configurationsystem 15002 is depicted. An example AI solution selection andconfiguration system 15002 may include an input module 15004 structuredto receive a variety of user-related input such as videos, audiorecording, heartbeat monitors, galvanic skin response data, motion data,and the like. There may be temporal data associated with theuser-related input. The input module 15004 may provide a subset of theuser-related input data 15014 to the input analysis module 15006. Theanalysis module 15006 may include a temporal analysis module 15018 toidentify timing of user-related actions. The temporal analysis module15018 may enable identification of timing of user actions. In someembodiments the input module 15004 may perform some preprocessing forthe subset of the user-related input data 15014, such as noisereduction, correlation between types of input data, and the like, priorto providing the subset of user-related input data 15014 to the inputanalysis module 15006. The input analysis module 15006, may identify atype of brain activity being engaged in (e.g. visual processing,auditory processing, olfactory processing, motion control, and the like)and a level of intensity of activity based on data such as heartbeatdata, galvanic skin response data and the like. A component selectionmodule 15008 may select a component for use in an AI solution based onthe type of brain activity and provide input into a componentconfiguration module 15010 which may include an ML input selectionmodule 15102 for selecting an input for a machine learning process, anMP output identification module 15104 for identifying an output to beprovided by the machine learning process, a runtime input selectionmodule 15106 for identifying an input for an operational solutionprocess, a runtime output identification module 15108 for identifying anoutput of the component, a settings module 15110 for identifying achange rate, identifying a weighting factor, setting a threshold forinput data, setting an output threshold for the operational roboticprocess, and the like, a parameter settings module 15112 for tuning alearning parameter, identifying a parameter for inclusion, identifying aparameter for exclusion, setting a parameter threshold, and the like.The component configuration module 15010 may configure the selectedcomponent 15012. The component selection module 15008 may also supplydata to the input specification module 15016. An AI solution assemblymodule 15020 may combine the configured component with other components,along with any standard or mandatory components, as necessary, to createthe AI solution. The AI solution may be set up to receive inputs asspecified by the input specification module 15016. Although oneiteration of selecting a component is shown in this figure, it isenvisioned, that multiple components may be selected, configured, andassembled as part of the AI solution.

In embodiments, referring to FIG. 152 , an AI solution selection andconfiguration system 15202 is depicted. An example AI solution selectionand configuration system 15202 may include a data input module 15204 toreceive an input stream including temporal user-related data 15214 whichmay include video streams, audio streams, equipment interactions (e.g.mouse clicks, mouse motion, physical input to a machine) user biometricssuch as heartbeat, galvanic skin response, eye tracking, and the like.The data input module 15204 may also receive temporal environmentalinput data 15220 representative of environmental input the user isreceiving such as a visual environment, an auditory environment,olfactory environment, equipment displays, a device user interface, andthe like. The data input module 15204 may also receive temporal resultsinput data 15203. The data input module 15204 may provide a subset ofthe received data 15214, 15220, 15203 to an input analysis module 15216.The data input module 15204 may process the received data 15214, 1522015203 to reduce noise, compress the data, correlate some of the data,and the like. The analysis module 15216 may identify a plurality of useractions to provide to the component selection module 15208. The imageanalysis module 15216 may include a temporal analysis module 15218 toidentify timing of user actions. The temporal analysis module 15218 mayallow for the correlation between temporal user-related data 15214,environmental data 15220, and results data 15203. Based on the useractions, the component selection module 15208 may select a componentthat would simulate one or more mental processes of the user needed toperform at least one of the plurality of user actions. Factors inidentifying the selected component may include the level ofcomputational intensity needed, time sensitivity, and the like. This maydictate a type of component, a location of component (on-board, in thecloud, edge-computing, and the like. The input analysis module 15216 mayalso provide information regarding the user's actions and environmentaldata to the component configuration module 15210. This data may be usedby the component configuration module as input to a machine learningalgorithm, in conjunction with the results data to identify which inputsare beneficial and which are detrimental to enabling the component toreach desired results, and identify appropriate weighting of inputs,parameter settings, and the like. The component configuration module15210 configures the component 15212 which is provided to the overall AIsolution 15224 together with configuration information.

As described elsewhere herein, this disclosure concerns systems andmethods for the discovery of opportunities for increased automation andintelligence, including solutions to domain-specific problems. Further,this disclosure also concerns selection and configuration of anartificial intelligence solution (e.g. neural networks, machine learningsystems, expert systems, etc.) once opportunities are discovered.

Referring now to FIG. 153 , a controller 15308 includes an opportunitymining module 153, an artificial intelligence configuration module15304, and an artificial intelligence search engine 15310, optionallyhaving a collaborative filter 15328 and a clustering engine 15330. Theopportunity mining module 153 receives input 15302, such as attributeinput regarding an attribute of a task, a domain, or a domain-relatedproblem.

The input 15302 may be processed by the opportunity mining module 153 todetermine whether an artificial intelligence system can be applied tothe task or the domain. For example, the attribute input 15302 mayinclude an attribute of a task, domain, or problem, such as anegotiating task, a drafting task, a data entry task, an email responsetask, a data analysis task, a document review task, an equipmentoperation task, a forecasting task, an NLP task, an image recognitiontask, a pattern recognition task, a motion detection task, a routeoptimization task, and the like. The opportunity mining module 153 maydetermine if one or more attributes of the task are similar to othertasks that have been automated or to which an intelligence has beenapplied, or based on the attribute of the task, if the task ispotentially automatable or suitable to have an intelligence applied toit regardless of whether it has been done previously. For example,attributes of a drafting task may include articulating a first idea,articulating a second idea, articulating a plurality of ideas, combiningthe plurality of ideas in a pairwise fashion, and combining the ideas ina triplicate fashion. Articulating ideas may not be suitable forautomation, but the task of combining ideas pairwise or in triplicateform may be suitable for automation or to have an intelligence appliedto the task.

If a determination is made that an artificial intelligence system can beapplied to the task or the domain, the output 15312 regarding thatdetermination may be used to trigger an artificial intelligence searchengine 15310 to perform a search of an artificial intelligence store157. The artificial intelligence store 157 may include a plurality ofdomain-specific and general artificial intelligence models 15318, andcomponents of domain-specific and general artificial intelligence models15318. The artificial intelligence store 157 may be organized by acategory. The category may be at least one of an artificial intelligencemodel component type, a domain, an input type, a processing type, anoutput type, a computational requirement, a computational capability, acost, a training status, or an energy usage. The artificial intelligencestore may include at least one e-commerce feature. The at least onee-commerce feature may include at least one of a rating, a review, alink to relevant content, a mechanism for provisioning, a mechanism forlicensing, a mechanism for delivery, or a mechanism for payment. Models15318 may be pre-trained, or may be available for training. Componentsof domain-specific and general artificial intelligence models 15318 mayinclude artificial intelligence building blocks, such as a componentthat detects and translates between languages, or a component thatdelivers highly personalized customer recommendations. One or moremodels 15318 and/or components of a model 15318 may be identified in asearch of the artificial intelligence store 157. Components of a model15318 may be identified either as a stand-alone element to be used inthe assembly of a custom AI model 15318 or as a component of a complete,optionally pre-trained, model 15318.

The artificial intelligence store 157 may include metadata 15324 orother descriptive material indicating a suitability of an artificialintelligence system for at least one of solving a particular type ofproblem or operating on domain-specific inputs, data, or other entities.The metadata 15324, or other descriptive material, category, ore-commerce feature may be searched using the attribute input 15302and/or other selection criteria 15314. For example, attributes of a taskinvolving 2D object classification may be searched in the artificialintelligence store 157 and its metadata 15324 to reveal that anartificial intelligence model 15318 suitable for a task involving 2Dobject classification may be a convolutional neural network. Continuingwith the example, there may be model diversity even within the class ofconvolutional neural networks (CNN) in the artificial intelligence store157, such as a CNN calibrated to a certain type of 2D object recognition(e.g., straight edges) and another CNN calibrated to another kind of 2Dobject recognition (e.g., combo of curved and straight edges). In thisexample, if the further edge vs. curved attribute of the type of 2Dobject is searched, the artificial intelligence store 157 would presentthe CNN best suited to the 2D object to be classified.

In embodiments, in addition to the input 15302, at least one selectioncriteria 15314 may be used by the artificial intelligence search engine15310 to search the artificial intelligence store 157 for artificialintelligence models 15318 and/or components thereof. Selection criteriaused in the recommendation of an artificial intelligence model 15318 ormodel component may include at least one of if the model is pre-trainedor not, an availability of the at least one artificial intelligencemodel 15318 or component thereof to execute in a user environment, anavailability of the at least one artificial intelligence model 15318 orcomponent thereof to a user, a governance principle, a governancepolicy, a computational factor, a network factor, a data availability, atask-specific factor, a performance factor, a quality of service factor,a model deployment consideration, a security consideration, or a humaninterface, which may be elsewhere described herein. For example, agovernance principle, such as a requirement for an anti-bias review ofpedestrian accident-avoidance systems, may be used to search anartificial intelligence store 157 for artificial intelligence models toapply to an autonomous driving task. In another example, a selectioncriteria for an artificial intelligence solution to be used with airtraffic control system may be a requirement for having been trained onadversarial attacks and deceptive input. In yet another example, aselection criteria for an artificial intelligence solution to be usedwith an equities trading task may be the requirement for humanoversight, and particularly, human-based final decisions.

The artificial intelligence search engine 15310 may rank one or moreresults of the search according to a strength or a weakness of the atleast one artificial intelligence model 15318 or model componentrelative to the at least one selection criteria 15314. The ranked searchresults may be presented to a user for evaluation and consideration, andultimately, selection. In embodiments, the artificial intelligencesearch engine 15310 may further include a collaborative filter 15328that receives an indication of an element of the at least one artificialintelligence model 15318 or model component from a user that is used tofilter the search results. In embodiments, the artificial intelligencesearch engine 15310 may further include a clustering engine 15330structured to cluster search results comprising the at least oneartificial intelligence model 15318 or model component. The clusteringengine 15330 may be at least one of a similarity matrix or a k-meansclustering. The clustering engine 15330 may associate at least one ofsimilar developers, similar domain-specific problems, or similarartificial intelligence solutions in the search results.

Once an artificial intelligence model 15318 or components thereof areidentified by the artificial intelligence search engine 15310, either bysearching with the input 15302 alone or with both the input 15302 and aselection criteria 15314, an artificial intelligence configurationmodule 15304 may configure one or more data inputs 15320 to use with theat least one artificial intelligence model 15318 or model component. Theartificial intelligence configuration module 15304 may, in certainembodiments, be operative in discovering and selecting what inputs 15320may enable effective and efficient use of artificial intelligence for agiven problem. In embodiments, the artificial intelligence configurationmodule 15304 may further configure the at least one artificialintelligence model 15318 or model component(s) in accordance with atleast one configuration criteria 15322. In embodiments, individual datainputs and model components may be configured via one or moreconfiguration criteria, while in other embodiments, a singleconfiguration criteria governs configuration of data input, AI componentassembly, and the like.

In embodiments, the at least one configuration criteria 15322 mayinclude at least one of an availability of the at least one artificialintelligence model 15318 or model component to execute in a userenvironment, an availability of the at least one artificial intelligencemodel 15318 or model component to a user, a governance principle, agovernance policy, a computational factor, a network factor, a dataavailability, a task-specific factor, a performance factor, a quality ofservice factor, a model deployment consideration, a securityconsideration, or a human interface. In embodiments, the at least oneconfiguration criteria may include at least one of identifying a desiredoutput, identifying training data, identifying parameters for exclusionor inclusion in training or operation of the model, an input datathreshold, an output data threshold, a selection of a neural networktype, a selection of an input model type, a setting of initial modelweights, a setting of model size, a selection of computationaldeployment environment, a selection of input data sources for training,a selection of input data sources for operation, a selection of feedbackfunction/outcome measures, a selection of data integration language(s)for inputs and outputs, a configuration of APIs for model training, aconfiguration of APIs 13114 for model inputs, a configuration of APIs13114 for outputs, a configuration of access controls, a configurationof security parameters, a configuration of network protocols, aconfiguration of storage parameters, a configuration of economicfactors, a configuration of data flows, a configuration of highavailability, one or more fault tolerance environments, a price-baseddata acquisition strategy, a heuristic method, a decision to make adecision model, or a coordination of massively parallel decision makingenvironments. In embodiments, the at least one configuration criteriamay include parameters for assembly of an AI solution from a pluralityof identified model components, optionally along with other standard ormandatory model components. For example, the model components may beconfigured to run in parallel, to run serially, or in a combination ofserial and parallel.

For example, the artificial intelligence configuration module 15304 mayconfigure an artificial intelligence model 15318 to weight one datainput 15320 more heavily than another. For example, in the rain, anautonomous driving solution may weight input from a traction controlsystem and a forward radar system more heavily than sensors targeted toincreasing fuel efficiency, such as sensors measuring road slope andvehicle speed. After the rain, the weighting may be reversed.

In another example, the artificial intelligence configuration module15304 may configure an artificial intelligence model 15318 to operatewithin certain thresholds of data input 15320. For example, anartificial intelligence model 15318 may be used in a combinatorialdrafting task. When only two articulated ideas are provided to the model15318, the model 15318 may not be triggered to operate. However, oncethe model 15318 receives a third articulated idea, its combinatorialprocessing of articulated ideas may commence.

The artificial intelligence configuration module 15304 may configurewhich sensors to use as data input 15320, how frequently to sample data,how frequently to transmit output, the weighting of various data inputs15320, thresholds to apply to data from data inputs 15320, whether anoutput of one component of the model 15318 is used as input to anothercomponent of the model 15318, an order of operation of the components ofthe model 15318, a positioning of a model component within a workflow ofa model, and the like.

The artificial intelligence configuration module 15304 may configure anartificial intelligence model 15318 from one or more model componentsidentified by the artificial intelligence search engine 15310. Forexample, if the search result consisted solely of model components, theAI configuration module 15304 may configure where to place theidentified 127 components in relation to one another, such as in aworkflow or data flow, as well as in relation to other components thatmay be required for the model 15318 to function.

In embodiments, an artificial intelligence store 157 may include a setof interfaces to artificial intelligence systems, such as enabling thedownload of relevant artificial intelligence applications, establishmentof links or other connections to artificial intelligence systems (suchas links to cloud-deployed artificial intelligence systems via APIs,ports, connectors, or other interfaces) and the like.

Referring now to FIG. 154 , a method of artificial intelligence modelidentification and selection may include receiving input regarding anattribute of a task or a domain 15402, and processing the input todetermine whether an artificial intelligence system can be applied tothe task or the domain 15404, performing a search of an artificialintelligence store of a plurality of domain-specific and generalartificial intelligence models and model components using the inputand/or at least one selection criteria to identify at least oneartificial intelligence model or model component to apply to the task orthe domain 15408, and configuring one or more data inputs to use withthe at least one artificial intelligence model 15410 or model component.The artificial intelligence store may include metadata or otherdescriptive material indicating a suitability of an artificialintelligence system for at least one of solving a particular type ofproblem or operating on domain-specific inputs, data, or other entities.

The method may further include ranking one or more results of the searchaccording to a strength or a weakness of the at least one artificialintelligence model relative to the at least one selection criteria15412. The method may further include configuring the at least oneartificial intelligence model or model component in accordance with atleast one configuration criteria 15414. The method may further includecollaborative filtering search results comprising the at least oneartificial intelligence model using an element of the at least oneartificial intelligence model selected or model component by a user15416. The method may further include clustering search resultscomprising the at least one artificial intelligence model or modelcomponent with a clustering engine 15418.

FIG. 155 illustrates an example environment of a digital twin system15500. In embodiments, the digital twin system 15500 generates a set ofdigital twins of a set of industrial environments 15520 and/orindustrial entities within the set of industrial environments. Inembodiments, the digital twin system 15500 maintains a set of states ofthe respective industrial environments 15520, such as using sensor dataobtained from respective sensor systems 15530 that monitor theindustrial environments 15520. In embodiments, the digital twin system15500 may include a digital twin management system 15502, a digital twinI/O system 15504, a digital twin simulation system 15506, a digital twindynamic model system 15508, a cognitive intelligence system 15510,and/or an environment control module 15512. In embodiments, the digitaltwin system 15500 may provide a real time sensor API that provides a setof capabilities for enabling a set of interfaces for the sensors of therespective sensor systems 15530. In embodiments, the digital twin system15500 may include and/or employ other suitable APIs, brokers,connectors, bridges, gateways, hubs, ports, routers, switches, dataintegration systems, peer-to-peer systems, and the like to facilitatethe transferring of data to and from the digital twin system 15500. Inthese embodiments, these connective components may allow an IoT sensoror an intermediary device (e.g., a relay, an edge device, a switch, orthe like) within a sensor system 15530 to communicate data to thedigital twin system 15500 and/or to receive data (e.g., configurationdata, control data, or the like) from the digital twin system 15500 oranother external system. In embodiments, the digital twin system 15500may further include a digital twin datastore 15516 that stores digitaltwins 15518 of various industrial environments 15520 and the objects15522, devices 15524, sensors 15526, and/or humans 15528 in theenvironment 15520.

A digital twin may refer to a digital representation of one or moreindustrial entities, such as an industrial environment 15520, a physicalobject 15522, a device 15524, a sensor 15526, a human 15528, or anycombination thereof. Examples of industrial environments 15520 include,but are not limited to, a factory, a power plant, a food productionfacility (which may include an inspection facility), a commercialkitchen, an indoor growing facility, a natural resources excavation site(e.g., a mine, an oil field, etc.), and the like. Depending on the typeof environment, the types of objects, devices, and sensors that arefound in the environments will differ. Non-limiting examples of physicalobjects 15522 include raw materials, manufactured products, excavatedmaterials, containers (e.g., boxes, dumpsters, cooling towers, vats,pallets, barrels, palates, bins, and the like), furniture (e.g., tables,counters, workstations, shelving, etc.), and the like. Non-limitingexamples of devices 15524 include robots, computers, vehicles (e.g.,cars, trucks, tankers, trains, forklifts, cranes, etc.),machinery/equipment (e.g., tractors, tillers, drills, presses, assemblylines, conveyor belts, etc.), and the like. The sensors 15526 may be anysensor devices and/or sensor aggregation devices that are found in asensor system 15530 within an environment. Non-limiting examples ofsensors 15526 that may be implemented in a sensor system 15530 mayinclude temperature sensors 15532, humidity sensors 15534, vibrationsensors 15536, LIDAR sensors 15538, motion sensors 15540, chemicalsensors 15542, audio sensors 15544, pressure sensors 15546, weightsensors 15548, radiation sensors 15550, video sensors 15552, wearabledevices 15554, relays 15556, edge devices 15558, crosspoint switches15560, and/or any other suitable sensors. Examples of different types ofphysical objects 15522, devices 15524, sensors 15526, and environments15520 are referenced throughout the disclosure.

In some embodiments, on-device sensor fusion and data storage forindustrial IoT devices is supported, including on-device sensor fusionand data storage for an industrial IoT device, where data from multiplesensors is multiplexed at the device for storage of a fused data stream.For example, pressure and temperature data may be multiplexed into adata stream that combines pressure and temperature in a time series,such as in a byte-like structure (where time, pressure, and temperatureare bytes in a data structure, so that pressure and temperature remainlinked in time, without requiring separate processing of the streams byoutside systems), or by adding, dividing, multiplying, subtracting, orthe like, such that the fused data can be stored on the device. Any ofthe sensor data types described throughout this disclosure, includingvibration data, can be fused in this manner, and stored in a local datapool, in storage, or on an IoT device, such as a data collector, acomponent of a machine, or the like.

In some embodiments, a set of digital twins may represent an entireorganization, such as energy production organizations, oil and gasorganizations, renewable energy production organizations, aerospacemanufacturers, vehicle manufacturers, heavy equipment manufacturers,mining organizations, drilling organizations, offshore platformorganizations, and the like. In these examples, the digital twins mayinclude digital twins of one or more industrial facilities of theorganization.

In embodiments, the digital twin management system 15502 generatesdigital twins. A digital twin may be comprised of (e.g., via reference)other digital twins. In this way, a discrete digital twin may becomprised of a set of other discrete digital twins. For example, adigital twin of a machine may include digital twins of sensors on themachine, digital twins of components that make up the machine, digitaltwins of other devices that are incorporated in or integrated with themachine (such as systems that provide inputs to the machine or takeoutputs from it), and/or digital twins of products or other items thatare made by the machine. Taking this example one step further, a digitaltwin of an industrial facility (e.g., a factory) may include a digitaltwin representing the layout of the industrial facility, including thearrangement of physical assets and systems in or around the facility, aswell as digital assets of the assets within the facility (e.g., thedigital twin of the machine), as well as digital twins of storage areasin the facility, digital twins of humans collecting vibrationmeasurements from machines throughout the facility, and the like. Inthis second example, the digital twin of the industrial facility mayreference the embedded digital twins, which may then reference otherdigital twins embedded within those digital twins.

In some embodiments, a digital twin may represent abstract entities,such as workflows and/or processes, including inputs, outputs, sequencesof steps, decision points, processing loops, and the like that make upsuch workflows and processes. For example, a digital twin may be adigital representation of a manufacturing process, a logistics workflow,an agricultural process, a mineral extraction process, or the like. Inthese embodiments, the digital twin may include references to theindustrial entities that are included in the workflow or process. Thedigital twin of the manufacturing process may reflect the various stagesof the process. In some of these embodiments, the digital twin system15500 receives real-time data from the industrial facility (e.g., from asensor system 15530 of the environment 15520) in which the manufacturingprocess takes place and reflects a current (or substantially current)state of the process in real-time.

In embodiments, the digital representation may include a set of datastructures (e.g., classes) that collectively define a set of propertiesof a represented physical object 15522, device 15524, sensor 15526, orenvironment 15520 and/or possible behaviors thereof. For example, theset of properties of a physical object 15522 may include a type of thephysical object, the dimensions of the object, the mass of the object,the density of the object, the material(s) of the object, the physicalproperties of the material(s), the surface of the physical object, thestatus of the physical object, a location of the physical object,identifiers of other digital twins contained within the object, and/orother suitable properties. Examples of behavior of a physical object mayinclude a state of the physical object (e.g., a solid, liquid, or gas),a melting point of the physical object, a density of the physical objectwhen in a liquid state, a viscosity of the physical object when in aliquid state, a freezing point of the physical object, a density of thephysical object when in a solid state, a hardness of the physical objectwhen in a solid state, the malleability of the physical object, thebuoyancy of the physical object, the conductivity of the physicalobject, a burning point of the physical object, the manner by whichhumidity affects the physical object, the manner by which water or otherliquids affect the physical object, a terminal velocity of the physicalobject, and the like. In another example, the set of properties of adevice may include a type of the device, the dimensions of the device,the mass of the device, the density of the density of the device, thematerial(s) of the device, the physical properties of the material(s),the surface of the device, the output of the device, the status of thedevice, a location of the device, a trajectory of the device, vibrationcharacteristics of the device, identifiers of other digital twins thatthe device is connected to and/or contains, and the like. Examples ofthe behaviors of a device may include a maximum acceleration of adevice, a maximum speed of a device, ranges of motion of a device, aheating profile of a device, a cooling profile of a device, processesthat are performed by the device, operations that are performed by thedevice, and the like. Example properties of an environment may includethe dimensions of the environment, the boundaries of the environment,the temperature of the environment, the humidity of the environment, theairflow of the environment, the physical objects in the environment,currents of the environment (if a body of water), and the like. Examplesof behaviors of an environment may include scientific laws that governthe environment, processes that are performed in the environment, rulesor regulations that must be adhered to in the environment, and the like.

In embodiments, the properties of a digital twin may be adjusted. Forexample, the temperature of a digital twin, a humidity of a digitaltwin, the shape of a digital twin, the material of a digital twin, thedimensions of a digital twin, or any other suitable parameters may beadjusted. As the properties of the digital twin are adjusted, otherproperties may be affected as well. For example, if the temperature ofan environment 15520 is increased, the pressure within the environmentmay increase as well, such as a pressure of a gas in accordance with theideal gas law. In another example, if a digital twin of a subzeroenvironment is increased to above freezing temperatures, the propertiesof an embedded twin of water in a solid state (i.e., ice) may changeinto a liquid state over time.

Digital twins may be represented in a number of different forms. Inembodiments, a digital twin may be a visual digital twin that isrendered by a computing device, such that a human user can view digitalrepresentations of an environment 15520 and/or the physical objects15522, devices 15524, and/or the sensors 15526 within an environment. Inembodiments, the digital twin may be rendered and output to a displaydevice. In some of these embodiments, the digital twin may be renderedin a graphical user interface, such that a user may interact with thedigital twin. For example, a user may “drill down” on a particularelement (e.g., a physical object or device) to view additionalinformation regarding the element (e.g., a state of a physical object ordevice, properties of the physical object or device, or the like). Insome embodiments, the digital twin may be rendered and output in avirtual reality display. For example, a user may view a 3D rendering ofan environment (e.g., using monitor or a virtual reality headset). Whiledoing so, the user may view/inspect digital twins of physical assets ordevices in the environment.

In some embodiments, a data structure of the visual digital twins (i.e.,digital twins that are configured to be displayed in a 2D or 3D manner)may include surfaces (e.g., splines, meshes, polygons meshes, or thelike). In some embodiments, the surfaces may include texture data,shading information, and/or reflection data. In this way, a surface maybe displayed in a more realistic manner. In some embodiments, suchsurfaces may be rendered by a visualization engine (not shown) when thedigital twin is within a field of view and/or when existing in a largerdigital twin (e.g., a digital twin of an industrial environment). Inthese embodiments, the digital twin system 15500 may render the surfacesof digital objects, whereby a rendered digital twin may be depicted as aset of adjoined surfaces.

In embodiments, a user may provide input that controls one or moreproperties of a digital twin via a graphical user interface. Forexample, a user may provide input that changes a property of a digitaltwin. In response, the digital twin system 15500 can calculate theeffects of the changed property and may update the digital twin and anyother digital twins affected by the change of the property.

In embodiments, a user may view processes being performed with respectto one or more digital twins (e.g., manufacturing of a product,extracting minerals from a mine or well, a livestock inspection line,and the like). In these embodiments, a user may view the entire processor specific steps within a process.

In some embodiments, a digital twin (and any digital twins embeddedtherein) may be represented in a non-visual representation (or “datarepresentation”). In these embodiments, a digital twin and any embeddeddigital twins exist in a binary representation but the relationshipsbetween the digital twins are maintained. For example, in embodiments,each digital twin and/or the components thereof may be represented by aset of physical dimensions that define a shape of the digital twin (orcomponent thereof). Furthermore, the data structure embodying thedigital twin may include a location of the digital twin. In someembodiments, the location of the digital twin may be provided in a setof coordinates. For example, a digital twin of an industrial environmentmay be defined with respect to a coordinate space (e.g., a Cartesiancoordinate space, a polar coordinate space, or the like). Inembodiments, embedded digital twins may be represented as a set of oneor more ordered triples (e.g., [x coordinate, y coordinate, zcoordinates] or other vector-based representations). In some of theseembodiments, each ordered triple may represent a location of a specificpoint (e.g., center point, top point, bottom point, or the like) on theindustrial entity (e.g., object, device, or sensor) in relation to theenvironment in which the industrial entity resides. In some embodiments,a data structure of a digital twin may include a vector that indicates amotion of the digital twin with respect to the environment. For example,fluids (e.g., liquids or gasses) or solids may be represented by avector that indicates a velocity (e.g., direction and magnitude ofspeed) of the entity represented by the digital twin. In embodiments, avector within a twin may represent a microscopic subcomponent, such as aparticle within a fluid, and a digital twin may represent physicalproperties, such as displacement, velocity, acceleration, momentum,kinetic energy, vibrational characteristics, thermal properties,electromagnetic properties, and the like.

In some embodiments, a set of two or more digital twins may berepresented by a graph database that includes nodes and edges thatconnect the nodes. In some implementations, an edge may represent aspatial relationship (e.g., “abuts,” “rests upon,” “contains”, and thelike). In these embodiments, each node in the graph database representsa digital twin of an entity (e.g., an industrial entity) and may includethe data structure defining the digital twin. In these embodiments, eachedge in the graph database may represent a relationship between twoentities represented by connected nodes. In some implementations, anedge may represent a spatial relationship (e.g., “abuts,” “rests upon,”“interlocks with”, “bears”, “contains”, and the like). In embodiments,various types of data may be stored in a node or an edge. Inembodiments, a node may store property data, state data, and/or metadatarelating to a facility, system, subsystem, and/or component. Types ofproperty data and state data will differ based on the entity representedby a node. For example, a node representing a robot may include propertydata that indicates a material of the robot, the dimensions of the robot(or components thereof), a mass of the robot, and the like. In thisexample, the state data of the robot may include a current pose of therobot, a location of the robot, and the like. In embodiments, an edgemay store relationship data and metadata data relating to a relationshipbetween two nodes. Examples of relationship data may include the natureof the relationship, whether the relationship is permanent (e.g., afixed component would have a permanent relationship with the structureto which it is attached or resting on), and the like. In embodiments, anedge may include metadata concerning the relationship between twoentities. For example, if a product was produced on an assembly line,one relationship that may be documented between a digital twin of theproduct and the assembly line may be “created by.” In these embodiments,an example edge representing the “created by” relationship may include atimestamp indicating a date and time that the product was created. Inanother example, a sensor may take measurements relating to a state of adevice, whereby one relationship between the sensor and the device mayinclude “measured” and may define a measurement type that is measured bythe sensor. In this example, the metadata stored in an edge may includea list of N measurements taken and a timestamp of each respectivemeasurement. In this way, temporal data relating to the nature of therelationship between two entities may be maintained, thereby allowingfor an analytics engine, machine-learning engine, and/or visualizationengine to leverage such temporal relationship data, such as by aligningdisparate data sets with a series of points in time, such as tofacilitate cause-and-effect analysis used for prediction systems.

In some embodiments, a graph database may be implemented in ahierarchical manner, such that the graph database relates a set offacilities, systems, and components. For example, a digital twin of amanufacturing environment may include a node representing themanufacturing environment. The graph database may further include nodesrepresenting various systems within the manufacturing environment, suchas nodes representing an HVAC system, a lighting system, a manufacturingsystem, and the like, all of which may connect to the node representingthe manufacturing system. In this example, each of the systems mayfurther connect to various subsystems and/or components of the system.For example, within the HVAC system, the HVAC system may connect to asubsystem node representing a cooling system of the facility, a secondsubsystem node representing a heating system of the facility, a thirdsubsystem node representing the fan system of the facility, and one ormore nodes representing a thermostat of the facility (or multiplethermostats). Carrying this example further, the subsystem nodes and/orcomponent nodes may connect to lower level nodes, which may includesubsystem nodes and/or component nodes. For example, the subsystem noderepresenting the cooling subsystem may be connected to a component noderepresenting an air conditioner unit. Similarly, a component noderepresenting a thermostat device may connect to one or more componentnodes representing various sensors (e.g., temperature sensors, humiditysensors, and the like).

In embodiments, where a graph database is implemented, a graph databasemay relate to a single environment or may represent a larger enterprise.In the latter scenario, a company may have various manufacturing anddistribution facilities. In these embodiments, an enterprise noderepresenting the enterprise may connect to environment nodes of eachrespective facility. In this way, the digital twin system 15500 maymaintain digital twins for multiple industrial facilities of anenterprise.

In embodiments, the digital twin system 15500 may use a graph databaseto generate a digital twin that may be rendered and displayed and/or maybe represented in a data representation. In the former scenario, thedigital twin system 15500 may receive a request to render a digitaltwin, whereby the request includes one or more parameters that areindicative of a view that will be depicted. For example, the one or moreparameters may indicate an industrial environment to be depicted and thetype of rendering (e.g., “real-world view” that depicts the environmentas a human would see it, an “infrared view” that depicts objects as afunction of their respective temperature, an “airflow view” that depictsthe airflow in a digital twin, or the like). In response, the digitaltwin system 15500 may traverse a graph database and may determine aconfiguration of the environment to be depicted based on the nodes inthe graph database that are related (either directly or through a lowerlevel node) to the environment node of the environment and the edgesthat define the relationships between the related nodes. Upondetermining a configuration, the digital twin system 15500 may identifythe surfaces that are to be depicted and may render those surfaces. Thedigital twin system 15500 may then render the requested digital twin byconnecting the surfaces in accordance with the configuration. Therendered digital twin may then be output to a viewing device (e.g., VRheadset, monitor, or the like). In some scenarios, the digital twinsystem 15500 may receive real-time sensor data from a sensor system15530 of an environment 15520 and may update the visual digital twinbased on the sensor data. For example, the digital twin system 1550 mayreceive sensor data (e.g., vibration data from a vibration sensor 15536)relating to a motor and its set of bearings. Based on the sensor data,the digital twin system 15500 may update the visual digital twin toindicate the approximate vibrational characteristics of the set ofbearings within a digital twin of the motor.

In scenarios where the digital twin system 15500 is providing datarepresentations of digital twins (e.g., for dynamic modeling,simulations, machine learning), the digital twin system 15500 maytraverse a graph database and may determine a configuration of theenvironment to be depicted based on the nodes in the graph database thatare related (either directly or through a lower level node) to theenvironment node of the environment and the edges that define therelationships between the related nodes. In some scenarios, the digitaltwin system 15500 may receive real-time sensor data from a sensor system15530 of an environment 15520 and may apply one or more dynamic modelsto the digital twin based on the sensor data. In other scenarios, a datarepresentation of a digital twin may be used to perform simulations, asis discussed in greater detail throughout the specification.

In some embodiments, the digital twin system 15500 may execute a digitalghost that is executed with respect to a digital twin of an industrialenvironment. In these embodiments, the digital ghost may monitor one ormore sensors of a sensor system 15530 of an industrial environment todetect anomalies that may indicate a malicious virus or other securityissues.

As discussed, the digital twin system 15500 may include a digital twinmanagement system 15502, a digital twin I/O system 15504, a digital twinsimulation system 15506, a digital twin dynamic model system 15508, acognitive intelligence system 15510, and/or an environment controlsystem 15512.

In embodiments, the digital twin management system 15502 creates newdigital twins, maintains/updates existing digital twins, and/or rendersdigital twins. The digital twin management system 15502 may receive userinput, uploaded data, and/or sensor data to create and maintain existingdigital twins. Upon creating a new digital twin, the digital twinmanagement system 15502 may store the digital twin in the digital twindatastore 15516. Creating, updating, and rendering digital twins arediscussed in greater detail throughout the disclosure.

In embodiments, the digital twin I/O system 15504 receives input fromvarious sources and outputs data to various recipients. In embodiments,the digital twin I/O system receives sensor data from one or more sensorsystems 15530. In these embodiments, each sensor system 15530 mayinclude one or more IoT sensors that output respective sensor data. Eachsensor may be assigned an IP address or may have another suitableidentifier. Each sensor may output sensor packets that include anidentifier of the sensor and the sensor data. In some embodiments, thesensor packets may further include a timestamp indicating a time atwhich the sensor data was collected. In some embodiments, the digitaltwin I/O system 15504 may interface with a sensor system 15530 via thereal-time sensor API 15514. In these embodiments, one or more devices(e.g., sensors, aggregators, edge devices) in the sensor system 15530may transmit the sensor packets containing sensor data to the digitaltwin I/O system 15504 via the API. The digital twin I/O system maydetermine the sensor system 15530 that transmitted the sensor packetsand the contents thereof, and may provide the sensor data and any otherrelevant data (e.g., time stamp, environment identifier/sensor systemidentifier, and the like) to the digital twin management system 15502.

In embodiments, the digital twin I/O system 15504 may receive importeddata from one or more sources. For example, the digital twin system15500 may provide a portal for users to create and manage their digitaltwins. In these embodiments, a user may upload one or more files (e.g.,image files, LIDAR scans, blueprints, and the like) in connection with anew digital twin that is being created. In response, the digital twinI/O system 15504 may provide the imported data to the digital twinmanagement system 15502. The digital twin I/O system 15504 may receiveother suitable types of data without departing from the scope of thedisclosure.

In some embodiments, the digital twin simulation system 15506 isconfigured to execute simulations using the digital twin. For example,the digital twin simulation system 15506 may iteratively adjust one ormore parameters of a digital twin and/or one or more embedded digitaltwins. In embodiments, the digital twin simulation system 15506, foreach set of parameters, executes a simulation based on the set ofparameters and may collect the simulation outcome data resulting fromthe simulation. Put another way, the digital twin simulation system15506 may collect the properties of the digital twin and the digitaltwins within or containing the digital twin used during the simulationas well as any outcomes stemming from the simulation. For example, inrunning a simulation on a digital twin of an indoor agriculturalfacility, the digital twin simulation system 15506 can vary thetemperature, humidity, airflow, carbon dioxide and/or other relevantparameters and can execute simulations that output outcomes resultingfrom different combinations of the parameters. In another example, thedigital twin simulation system 15506 may simulate the operation of aspecific machine within an industrial facility that produces an outputgiven a set of inputs. In some embodiments, the inputs may be varied todetermine an effect of the inputs on the machine and the output thereof.In another example, the digital twin simulation system 15506 maysimulate the vibration of a machine and/or machine components. In thisexample, the digital twin of the machine may include a set of operatingparameters, interfaces, and capabilities of the machine. In someembodiments, the operating parameters may be varied to evaluate theeffectiveness of the machine. The digital twin simulation system 15506is discussed in further detail throughout the disclosure.

In embodiments, the digital twin dynamic model system 15508 isconfigured to model one or more behaviors with respect to a digital twinof an environment. In embodiments, the digital twin dynamic model system15508 may receive a request to model a certain type of behaviorregarding an environment or a process and may model that behavior usinga dynamic model, the digital twin of the environment or process, andsensor data collected from one or more sensors that are monitoring theenvironment or process. For example, an operator of a machine havingbearings may wish to model the vibration of the machine and bearings todetermine whether the machine and/or bearings can withstand an increasein output. In this example, the digital twin dynamic model system 15508may execute a dynamic model that is configured to determine whether anincrease in output would result in adverse consequences (e.g., failures,downtime, or the like). The digital twin dynamic model system 15508 isdiscussed in further detail throughout the disclosure.

In embodiments, the cognitive processes system 15510 performs machinelearning and artificial intelligence related tasks on behalf of thedigital twin system. In embodiments, the cognitive processes system15510 may train any suitable type of model, including but not limited tovarious types of neural networks, regression models, random forests,decision trees, Hidden Markov models, Bayesian models, and the like. Inembodiments, the cognitive processes system 15510 trains machine learnedmodels using the output of simulations executed by the digital twinsimulation system 15506. In some of these embodiments, the outcomes ofthe simulations may be used to supplement training data collected fromreal-world environments and/or processes. In embodiments, the cognitiveprocesses system 15510 leverages machine learned models to makepredictions, identifications, classifications and provide decisionsupport relating to the real-world environments and/or processesrepresented by respective digital twins.

For example, a machine-learned prediction model may be used to predictthe cause of irregular vibrational patterns (e.g., a suboptimal,critical, or alarm vibration fault state) for a bearing of an engine inan industrial facility. In this example, the cognitive processes system15510 may receive vibration sensor data from one or more vibrationsensors disposed on or near the engine and may receive maintenance datafrom the industrial facility and may generate a feature vector based onthe vibration sensor data and the maintenance data. The cognitiveprocesses system 15510 may input the feature vector into amachine-learned model trained specifically for the engine (e.g., using acombination of simulation data and real-world data of causes ofirregular vibration patterns) to predict the cause of the irregularvibration patterns. In this example, the causes of the irregularvibrational patterns could be a loose bearing, a lack of bearinglubrication, a bearing that is out of alignment, a worn bearing, thephase of the bearing may be aligned with the phase of the engine, loosehousing, loose bolt, and the like.

In another example, a machine-learned model may be used to providedecision support to bring a bearing of an engine in an industrialfacility operating at a suboptimal vibration fault level state to anormal operation vibration fault level state. In this example, thecognitive processes system 15510 may receive vibration sensor data fromone or more vibration sensors disposed on or near the engine and mayreceive maintenance data from the industrial facility and may generate afeature vector based on the vibration sensor data and the maintenancedata. The cognitive processes system 15510 may input the feature vectorinto a machine-learned model trained specifically for the engine (e.g.,using a combination of simulation data and real-world data of solutionsto irregular vibration patterns) to provide decision support inachieving a normal operation fault level state of the bearing. In thisexample, the decision support could be a recommendation to tighten thebearing, lubricate the bearing, re-align the bearing, order a newbearing, order a new part, collect additional vibration measurements,change operating speed of the engine, tighten housings, tighten bolts,and the like.

In another example, a machine-learned model may be used to providedecision support relating to vibration measurement collection by aworker. In this example, the cognitive processes system 15510 mayreceive vibration measurement history data from the industrial facilityand may generate a feature vector based on the vibration measurementhistory data. The cognitive processes system 15510 may input the featurevector into a machine-learned model trained specifically for the engine(e.g., using a combination of simulation data and real-world vibrationmeasurement history data) to provide decision support in selectingvibration measurement locations.

In yet another example, a machine-learned model may be used to identifyvibration signatures associated with machine and/or machine componentproblems. In this example, the cognitive processes system 15510 mayreceive vibration measurement history data from the industrial facilityand may generate a feature vector based on the vibration measurementhistory data. The cognitive processes system 15510 may input the featurevector into a machine-learned model trained specifically for the engine(e.g., using a combination of simulation data and real-world vibrationmeasurement history data) to identify vibration signatures associatedwith a machine and/or machine component. The foregoing examples arenon-limiting examples and the cognitive processes system 15510 may beused for any other suitable AI/machine-learning related tasks that areperformed with respect to industrial facilities.

In embodiments, the environment control system 15512 controls one ormore aspects of industrial facilities. In some of these embodiments, theenvironment control system 15512 may control one or more devices withinan industrial environment. For example, the environment control system15512 may control one or more machines within an environment, robotswithin an environment, an HVAC system of the environment, an alarmsystem of the environment, an assembly line in an environment, or thelike. In embodiments, the environment control system 15512 may leveragethe digital twin simulation system 15506, the digital twin dynamic modelsystem 15508, and/or the cognitive processes system 15510 to determineone or more control instructions. In embodiments, the environmentcontrol system 15512 may implement a rules-based and/or amachine-learning approach to determine the control instructions. Inresponse to determining a control instruction, the environment controlsystem 15512 may output the control instruction to the intended devicewithin a specific environment via the digital twin I/O system 15504.

FIG. 156 illustrates an example digital twin management system 15502according to some embodiments of the present disclosure. In embodiments,the digital twin management system 15502 may include, but is not limitedto, a digital twin creation module 15564, a digital twin update module15566, and a digital twin visualization module 15568.

In embodiments, the digital twin creation module 15564 may create a setof new digital twins of a set of environments using input from users,imported data (e.g., blueprints, specifications, and the like), imagescans of the environment, 3D data from a LIDAR device and/or SLAMsensor, and other suitable data sources. For example, a user (e.g., auser affiliated with an organization/customer account) may, via a clientapplication 15570, provide input to create a new digital twin of anenvironment. In doing so, the user may upload 2D or 3D image scans ofthe environment and/or a blueprint of the environment. The user may alsoupload 3D data, such as taken by a camera, a LIDAR device, an IRscanner, a set of SLAM sensors, a radar device, an EMF scanner, or thelike. In response to the provided data, the digital twin creation module15564 may create a 3D representation of the environment, which mayinclude any objects that were captured in the image data/detected in the3D data. In embodiments, the cognitive processes system 15572 mayanalyze input data (e.g., blueprints, image scans, 3D data) to classifyrooms, pathways, equipment, and the like to assist in the generation ofthe 3D representation. In some embodiments, the digital twin creationmodule 15564 may map the digital twin to a 3D coordinate space (e.g., aCartesian space having x, y, and z axes).

In some embodiments, the digital twin creation module 15564 may outputthe 3D representation of the environment to a graphical user interface(GUI). In some of these embodiments, a user may identify certain areasand/or objects and may provide input relating to the identified areasand/or objects. For example, a user may label specific rooms, equipment,machines, and the like. Additionally or alternatively, the user mayprovide data relating to the identified objects and/or areas. Forexample, in identifying a piece of equipment, the user may provide amake/model number of the equipment. In some embodiments, the digitaltwin creation module 15564 may obtain information from a manufacturer ofa device, a piece of equipment, or machinery. This information mayinclude one or more properties and/or behaviors of the device,equipment, or machinery. In some embodiments, the user may, via the GUI,identify locations of sensors throughout the environment. For eachsensor, the user may provide a type of sensor and related data (e.g.,make, model, IP address, and the like). The digital twin creation module15564 may record the locations (e.g., the x, y, z coordinates of thesensors) in the digital twin of the environment. In embodiments, thedigital twin system 15500 may employ one or more systems that automatethe population of digital twins. For example, the digital twin system15500 may employ a machine vision-based classifier that classifies makesand models of devices, equipment, or sensors. Additionally oralternatively, the digital twin system 15500 may iteratively pingdifferent types of known sensors to identify the presence of specifictypes of sensors that are in an environment. Each time a sensor respondsto a ping, the digital twin system 15500 may extrapolate the make andmodel of the sensor.

In some embodiments, the manufacturer may provide or make availabledigital twins of their products (e.g., sensors, devices, machinery,equipment, raw materials, and the like). In these embodiments, thedigital twin creation module 15564 may import the digital twins of oneor more products that are identified in the environment and may embedthose digital twins in the digital twin of the environment. Inembodiments, embedding a digital twin within another digital twin mayinclude creating a relationship between the embedded digital twin withthe other digital twin. In these embodiments, the manufacturer of thedigital twin may define the behaviors and/or properties of therespective products. For example, a digital twin of a machine may definethe manner by which the machine operates, the inputs/outputs of themachine, and the like. In this way, the digital twin of the machine mayreflect the operation of the machine given a set of inputs.

In embodiments, a user may define one or more processes that occur in anenvironment. In these embodiments, the user may define the steps in theprocess, the machines/devices that perform each step in the process, theinputs to the process, and the outputs of the process.

In embodiments, the digital twin creation module 15564 may create agraph database that defines the relationships between a set of digitaltwins. In these embodiments, the digital twin creation module 15564 maycreate nodes for the environment, systems and subsystems of theenvironment, devices in the environment, sensors in the environment,workers that work in the environment, processes that are performed inthe environment, and the like. In embodiments, the digital twin creationmodule 15564 may write the graph database representing a set of digitaltwins to the digital twin datastore 15516.

In embodiments, the digital twin creation module 15564 may, for eachnode, include any data relating to the entity in the node representingthe entity. For example, in defining a node representing an environment,the digital twin creation module 15564 may include the dimensions,boundaries, layout, pathways, and other relevant spatial data in thenode. Furthermore, the digital twin creation module 15564 may define acoordinate space with respect to the environment. In the case that thedigital twin may be rendered, the digital twin creation module 15564 mayinclude a reference in the node to any shapes, meshes, splines,surfaces, and the like that may be used to render the environment. Inrepresenting a system, subsystem, device, or sensor, the digital twincreation module 15564 may create a node for the respective entity andmay include any relevant data. For example, the digital twin creationmodule 15564 may create a node representing a machine in theenvironment. In this example, the digital twin creation module 15564 mayinclude the dimensions, behaviors, properties, location, and/or anyother suitable data relating to the machine in the node representing themachine. The digital twin creation module 15564 may connect nodes ofrelated entities with an edge, thereby creating a relationship betweenthe entities. In doing so, the created relationship between the entitiesmay define the type of relationship characterized by the edge. Inrepresenting a process, the digital twin creation module 15564 maycreate a node for the entire process or may create a node for each stepin the process. In some of these embodiments, the digital twin creationmodule 15564 may relate the process nodes to the nodes that representthe machinery/devices that perform the steps in the process. Inembodiments, where an edge connects the process step nodes to themachinery/device that performs the process step, the edge or one of thenodes may contain information that indicates the input to the step, theoutput of the step, the amount of time the step takes, the nature ofprocessing of inputs to produce outputs, a set of states or modes theprocess can undergo, and the like.

In embodiments, the digital twin update module 15566 updates sets ofdigital twins based on a current status of one or more industrialentities. In some embodiments, the digital twin update module 15566receives sensor data from a sensor system 15530 of an industrialenvironment and updates the status of the digital twin of the industrialenvironment and/or digital twins of any affected systems, subsystems,devices, workers, processes, or the like. As discussed, the digital twinI/O system 15504 may receive the sensor data in one or more sensorpackets. The digital twin I/O system 15504 may provide the sensor datato the digital twin update module 15566 and may identify the environmentfrom which the sensor packets were received and the sensor that providedthe sensor packet. In response to the sensor data, the digital twinupdate module 15566 may update a state of one or more digital twinsbased on the sensor data. In some of these embodiments, the digital twinupdate module 15566 may update a record (e.g., a node in a graphdatabase) corresponding to the sensor that provided the sensor data toreflect the current sensor data. In some scenarios, the digital twinupdate module 15566 may identify certain areas within the environmentthat are monitored by the sensor and may update a record (e.g., a nodein a graph database) to reflect the current sensor data. For example,the digital twin update module 15566 may receive sensor data reflectingdifferent vibrational characteristics of a machine and/or machinecomponents. In this example, the digital twin update module 15566 mayupdate the records representing the vibration sensors that provided thevibration sensor data and/or the records representing the machine and/orthe machine components to reflect the vibration sensor data. In anotherexample, in some scenarios, workers in an industrial environment (e.g.,manufacturing facility, industrial storage facility, a mine, a drillingoperation, or the like) may be required to wear wearable devices (e.g.,smart watches, smart helmets, smart shoes, or the like). In theseembodiments, the wearable devices may collect sensor data relating tothe worker (e.g., location, movement, heartrate, respiration rate, bodytemperature, or the like) and/or the environment surrounding the workerand may communicate the collected sensor data to the digital twin system15500 (e.g., via the real-time sensor API 15514) either directly or viaan aggregation device of the sensor system. In response to receiving thesensor data from the wearable device of a worker, the digital twinupdate module 15566 may update a digital twin of a worker to reflect,for example, a location of the worker, a trajectory of the worker, ahealth status of the worker, or the like. In some of these embodiments,the digital twin update module 15566 may update a node representing aworker and/or an edge that connects the node representing theenvironment with the collected sensor data to reflect the current statusof the worker.

In some embodiments, the digital twin update module 15566 may providethe sensor data from one or more sensors to the digital twin dynamicmodel system 15508, which may model a behavior of the environment and/orone or more industrial entities to extrapolate additional state data.

In embodiments, the digital twin visualization module 15568 receivesrequests to view a visual digital twin or a portion thereof. Inembodiments, the request may indicate the digital twin to be viewed(e.g., an environment identifier). In response, the digital twinvisualization module 15568 may determine the requested digital twin andany other digital twins implicated by the request. For example, inrequesting to view a digital twin of an environment, the digital twinvisualization module 15568 may further identify the digital twins of anyindustrial entities within the environment. In embodiments, the digitaltwin visualization module 15568 may identify the spatial relationshipsbetween the industrial entities and the environment based on, forexample, the relationships defined in a graph database. In theseembodiments, the digital twin visualization module 15568 can determinethe relative location of embedded digital twins within the containingdigital twin, relative locations of adjoining digital twins, and/or thetransience of the relationship (e.g., is an object fixed to a point ordoes the object move). The digital twin visualization module 15568 mayrender the requested digital twins and any other implicated digital twinbased on the identified relationships. In some embodiments, the digitaltwin visualization module 15568 may, for each digital twin, determinethe surfaces of the digital twin. In some embodiments, the surfaces of adigital may be defined or referenced in a record corresponding to thedigital twin, which may be provided by a user, determined from importedimages, or defined by a manufacturer of an industrial entity. In thescenario that an object can take different poses or shapes (e.g., anindustrial robot), the digital twin visualization module 15568 maydetermine a pose or shape of the object for the digital twin. Thedigital twin visualization module 15568 may embed the digital twins intothe requested digital twin and may output the requested digital twin toa client application.

In some of these embodiments, the request to view a digital twin mayfurther indicate the type of view. As discussed, in some embodiments,digital twins may be depicted in a number of different view types. Forexample, an environment or device may be viewed in a “real-world” viewthat depicts the environment or device as they typically appear, in a“heat” view that depicts the environment or device in a manner that isindicative of a temperature of the environment or device, in a“vibration” view that depicts the machines and/or machine components inan industrial environment in a manner that is indicative of vibrationalcharacteristics of the machines and/or machine components, in a“filtered” view that only displays certain types of objects within anenvironment or components of a device (such as objects that requireattention resulting from, for example, recognition of a fault condition,an alert, an updated report, or other factor), an augmented view thatoverlays data on the digital twin, and/or any other suitable view types.In embodiments, digital twins may be depicted in a number of differentrole-based view types. For example, a manufacturing facility device maybe viewed in an “operator” view that depicts the facility in a mannerthat is suitable for a facility operator, a “C-Suite” view that depictsthe facility in a manner that is suitable for executive-level managers,a “marketing” view that depicts the facility in a manner that issuitable for workers in sales and/or marketing roles, a “board” viewthat depicts the facility in a manner that is suitable for members of acorporate board, a “regulatory” view that depicts the facility in amanner that is suitable for regulatory managers, and a “human resources”view that depicts the facility in a manner that is suitable for humanresources personnel. In response to a request that indicates a viewtype, the digital twin visualization module 15568 may retrieve the datafor each digital twin that corresponds to the view type. For example, ifa user has requested a vibration view of a factory floor, the digitaltwin visualization module 15568 may retrieve vibration data for thefactory floor (which may include vibration measurements taken fromdifferent machines and/or machine components and/or vibrationmeasurements that were extrapolated by the digital twin dynamic modelsystem 15508 and/or simulated vibration data from digital twinsimulation system 15506) as well as available vibration data for anyindustrial entities appearing on the factory floor. In this example, thedigital twin visualization module 15568 may determine colorscorresponding to each machine component on a factory floor thatrepresent a vibration fault level state (e.g., red for alarm, orange forcritical, yellow for suboptimal, and green for normal operation). Thedigital twin visualization module 15568 may then render the digitaltwins of the machine components within the environment based on thedetermined colors. Additionally or alternatively, the digital twinvisualization module 15568 may render the digital twins of the machinecomponents within the environment with indicators having the determinedcolors. For instance, if the vibration fault level state of an inboundbearing of a motor is suboptimal and the outbound bearing of the motoris critical, the digital twin visualization module 15568 may render thedigital twin of the inbound bearing having an indicator in a shade ofyellow (e.g., suboptimal) and the outbound bearing having an indicatorin a shade of orange (e.g., critical). It is noted that in someembodiments, the digital twin system 15500 may include an analyticssystem (not shown) that determine the manner by which the digital twinvisualization system 15500 presents information to a human user. Forexample, the analytics system may track outcomes relating to humaninteractions with real-world environments or objects in response toinformation presented in a visual digital twin. In some embodiments, theanalytics system may apply cognitive models to determine the mosteffective manner to display visualized information (e.g., what colors touse to denote an alarm condition, what kind of movements or animationsbring attention to an alarm condition, or the like) or audio information(what sounds to use to denote an alarm condition) based on the outcomedata. In some embodiments, the analytics system may apply cognitivemodels to determine the most suitable manner to display visualizedinformation based on the role of the user. In embodiments, thevisualization may include display of information related to thevisualized digital twins, including graphical information, graphicalinformation depicting vibration characteristics, graphical informationdepicting harmonic peaks, graphical information depicting peaks,vibration severity units data, vibration fault level state data,recommendations from cognitive intelligence system 15510, predictionsfrom cognitive intelligence system 15510, probability of failure data,maintenance history data, time to failure data, cost of downtime data,probability of downtime data, cost of repair data, cost of machinereplace data, probability of shutdown data, manufacturing KPIs, and thelike.

In another example, a user may request a filtered view of a digital twinof a process, whereby the digital twin of the process only showscomponents (e.g., machine or equipment) that are involved in theprocess. In this example, the digital twin visualization module 15568may retrieve a digital twin of the process, as well as any relateddigital twins (e.g., a digital twin of the environment and digital twinsof any machinery or devices that impact the process). The digital twinvisualization module 15568 may then render each of the digital twins(e.g., the environment and the relevant industrial entities) and thenmay perform the process on the rendered digital twins. It is noted thatas a process may be performed over a period of time and may includemoving items and/or parts, the digital twin visualization module 15568may generate a series of sequential frames that demonstrate the process.In this scenario, the movements of the machines and/or devicesimplicated by the process may be determined according to the behaviorsdefined in the respective digital twins of the machines and/or devices.

As discussed, the digital twin visualization module 15568 may output therequested digital twin to a client application 15570. In someembodiments, the client application 15570 is a virtual realityapplication, whereby the requested digital twin is displayed on avirtual reality headset. In some embodiments, the client application15570 is an augmented reality application, whereby the requested digitaltwin is depicted in an AR-enabled device. In these embodiments, therequested digital twin may be filtered such that visual elements and/ortext are overlaid on the display of the AR-enabled device.

It is noted that while a graph database is discussed, the digital twinsystem 15500 may employ other suitable data structures to storeinformation relating to a set of digital twins. In these embodiments,the data structures, and any related storage system, may be implementedsuch that the data structures provide for some degree of feedback loopsand/or recursion when representing iteration of flows.

FIG. 157 illustrates an example of a digital twin I/O system 15504 thatinterfaces with the environment 15520, the digital twin system 15500,and/or components thereof to provide bi-directional transfer of databetween coupled components according to some embodiments of the presentdisclosure.

In embodiments, the transferred data includes signals (e.g., requestsignals, command signals, response signals, etc.) between connectedcomponents, which may include software components, hardware components,physical devices, virtualized devices, simulated devices, combinationsthereof, and the like. The signals may define material properties (e.g.,physical quantities of temperature, pressure, humidity, density,viscosity, etc.), measured values (e.g., contemporaneous or storedvalues acquired by the device or system), device properties (e.g.,device ID or properties of the device's design specifications,materials, measurement capabilities, dimensions, absolute position,relative position, combinations thereof, and the like), set points(e.g., targets for material properties, device properties, systemproperties, combinations thereof, and the like), and/or critical points(e.g., threshold values such as minimum or maximum values for materialproperties, device properties, system properties, etc.). The signals maybe received from systems or devices that acquire (e.g., directly measureor generate) or otherwise obtain (e.g., receive, calculate, look-up,filter, etc.) the data, and may be communicated to or from the digitaltwin I/O system 15504 at predetermined times or in response to a request(e.g., polling) from the digital twin I/O system 15504. Thecommunications may occur through direct or indirect connections (e.g.,via intermediate modules within a circuit and/or intermediate devicesbetween the connected components). The values may correspond toreal-world elements 157302 r (e.g., an input or output for a tangiblevibration sensor) or virtual elements 157302 v (e.g., an input or outputfor a digital twin 157302 d and/or a simulated element 157302 s thatprovide vibration data).

In embodiments, the real-world elements 157302 r may be elements withinthe industrial environment 15520. The real-world elements 157302 r mayinclude, for example, non-networked objects 15522, the devices 15524(smart or non-smart), sensors 15526, and humans 15528. The real-worldelements 151302 r may be process or non-process equipment within theindustrial environments 15520. For example, process equipment mayinclude motors, pumps, mills, fans, painters, welders, smelters, etc.,and non-process equipment may include personal protective equipment,safety equipment, emergency stations or devices (e.g., safety showers,eyewash stations, fire extinguishers, sprinkler systems, etc.),warehouse features (e.g., walls, floor layout, etc.), obstacles (e.g.,persons or other items within the environment 15520, etc.), etc.

In embodiments, the virtual elements 157302 v may be digitalrepresentations of or that correspond to contemporaneously existingreal-world elements 157302 r. Additionally or alternatively, the virtualelements 157302 v may be digital representations of or that correspondto real-world elements 157302 r that may be available for later additionand implementation into the environment 15520. The virtual elements mayinclude, for example, simulated elements 175302 s and/or digital twins157302 d. In embodiments, the simulated elements 157302 s may be digitalrepresentations of real-world elements 157302 s that are not presentwithin the industrial environment 15520. The simulated elements 157302 smay mimic desired physical properties which may be later integratedwithin the environment 15520 as real-world elements 157302 r (e.g., a“black box” that mimics the dimensions of a real-world elements 157302r). The simulated elements 157302 s may include digital twins ofexisting objects (e.g., a single simulated element 151302 s may includeone or more digital twins 151302 d for existing sensors). Informationrelated to the simulated elements 157302 s may be obtained, for example,by evaluating behavior of corresponding real-world elements 157302 rusing mathematical models or algorithms, from libraries that defineinformation and behavior of the simulated elements 131302 s (e.g.,physics libraries, chemistry libraries, or the like).

In embodiments, the digital twin 157302 d may be a digitalrepresentation of one or more real-world elements 157302 r. The digitaltwins 157302 d are configured to mimic, copy, and/or model behaviors andresponses of the real-world elements 157302 r in response to inputs,outputs, and/or conditions of the surrounding or ambient environment.Data related to physical properties and responses of the real-worldelements 157302 r may be obtained, for example, via user input, sensorinput, and/or physical modeling (e.g., thermodynamic models,electrodynamic models, mechanodynamic models, etc.). Information for thedigital twin 157302 d may correspond to and be obtained from the one ormore real-world elements 157302 r corresponding to the digital twin157302 d. For example, in some embodiments, the digital twin 131302 dmay correspond to one real-world element 157302 r that is a fixeddigital vibration sensor 15536 on a machine component, and vibrationdata for the digital twin 131302 d may be obtained by polling orfetching vibration data measured by the fixed digital vibration sensoron the machine component. In a further example, the digital twin 157302d may correspond to a plurality of real-world elements 157302 r suchthat each of the elements can be a fixed digital vibration sensor on amachine component, and vibration data for the digital twin 157302 d maybe obtained by polling or fetching vibration data measured by each ofthe fixed digital vibration sensors on the plurality of real-worldelements 157302 r. Additionally or alternatively, vibration data of afirst digital twin 157302 d may be obtained by fetching vibration dataof a second digital twin 157302 d that is embedded within the firstdigital twin 157302 d, and vibration data for the first digital twin157302 d may include or be derived from vibration data for the seconddigital twin 157302 d. For example, the first digital twin may be adigital twin 157302 d of an environment 15520 (alternatively referred toas an “environmental digital twin”) and the second digital twin 157302 dmay be a digital twin 157302 d corresponding to a vibration sensordisposed within the environment 15520 such that the vibration data forthe first digital twin 157302 d is obtained from or calculated based ondata including the vibration data for the second digital twin 157302 d.

In embodiments, the digital twin system 15500 monitors properties of thereal-world elements 157302 r using the sensors 15526 within a respectiveenvironment 15520 that is or may be represented by a digital twin 157302d and/or outputs of models for one or more simulated elements 157302 s.In embodiments, the digital twin system 15500 may minimize networkcongestion while maintaining effective monitoring of processes byextending polling intervals and/or minimizing data transfer for sensorscorresponding that correspond to affected real-world elements 157302 rand performing simulations (e.g., via the digital-twin simulation system15506) during the extended interval using data that was obtained fromother sources (e.g., sensors that are physically proximate to or have aneffect on the affected real-world elements 157302 r). Additionally oralternatively, error checking may be performed by comparing thecollected sensor data with data obtained from the digital-twinsimulation system 15506. For example, consistent deviations orfluctuations between sensor data obtained from the real-world element157302 r and the simulated element 157302 s may indicate malfunction ofthe respective sensor or another fault condition.

In embodiments, the digital twin system 15500 may optimize features ofthe environment through use of one or more simulated elements 157302 s.For example, the digital twin system 15500 may evaluate effects of thesimulated elements 157302 s within a digital twin of an environment toquickly and efficiently determine costs and/or benefits flowing frominclusion, exclusion, or substitution of real-world elements 157302 rwithin the environment 15520. The costs and benefits may include, forexample, increased machinery costs (e.g., capital investment andmaintenance), increased efficiency (e.g., process optimization to reducewaste or increase throughput), decreased or altered footprint within theenvironment 15520, extension or optimization of useful lifespans,minimization of component faults, minimization of component downtime,etc.

In embodiments, the digital twin I/O system 15504 may include one ormore software modules that are executed by one or more controllers ofone or more devices (e.g., server devices, user devices, and/ordistributed devices) to affect the described functions. The digital twinI/O system 15504 may include, for example, an input module 157304, anoutput module 157306, and an adapter module 157308.

In embodiments, the input module 157304 may obtain or import data fromdata sources in communication with the digital twin I/O system 15504,such as the sensor system 15530 and the digital twin simulation system15506. The data may be immediately used by or stored within the digitaltwin system 15500. The imported data may be ingested from data streams,data batches, in response to a triggering event, combinations thereof,and the like. The input module 157304 may receive data in a format thatis suitable to transfer, read, and/or write information within thedigital twin system 15500.

In embodiments, the output module 157306 may output or export data toother system components (e.g., the digital twin datastore 15516, thedigital twin simulation system 15506, the cognitive intelligence system15510, etc.), devices 15524, and/or the client application 15570. Thedata may be output in data streams, data batches, in response to atriggering event (e.g., a request), combinations thereof, and the like.The output module 157306 may output data in a format that is suitable tobe used or stored by the target element (e.g., one protocol for outputto the client application and another protocol for the digital twindatastore 15516).

In embodiments, the adapter module 157308 may process and/or convertdata between the input module 157304 and the output module 157306. Inembodiments, the adapter module 157308 may convert and/or route dataautomatically (e.g., based on data type) or in response to a receivedrequest (e.g., in response to information within the data).

In embodiments, the digital twin system 15500 may represent a set ofindustrial workpiece elements in a digital twin, and the digital twinsimulation system 15506 simulates a set of physical interactions of aworker with the workpiece elements.

In embodiments, the digital twin simulation system 15506 may determineprocess outcomes for the simulated physical interactions accounting forsimulated human factors. For example, variations in workpiece throughputmay be modeled by the digital twin system 15500 including, for example,worker response times to events, worker fatigue, discontinuity withinworker actions (e.g., natural variations in human-movement speed,differing positioning times, etc.), effects of discontinuities ondownstream processes, and the like. In embodiments, individualizedworker interactions may be modeled using historical data that iscollected, acquired, and/or stored by the digital twin system 15500. Thesimulation may begin based on estimated amounts (e.g., worker age,industry averages, workplace expectations, etc.). The simulation mayalso individualize data for each worker (e.g., comparing estimatedamounts to collected worker-specific outcomes).

In embodiments, information relating to workers (e.g., fatigue rates,efficiency rates, and the like) may be determined by analyzingperformance of specific workers over time and modeling said performance.

In embodiments, the digital twin system 15500 includes a plurality ofproximity sensors within the sensor system 15530. The proximity sensorsare or may be configured to detect elements of the environment 15520that are within a predetermined area. For example, proximity sensors mayinclude electromagnetic sensors, light sensors, and/or acoustic sensors.

The electromagnetic sensors are or may be configured to sense objects orinteractions via one or more electromagnetic fields (e.g., emittedelectromagnetic radiation or received electromagnetic radiation). Inembodiments, the electromagnetic sensors include inductive sensors(e.g., radio-frequency identification sensors), capacitive sensors(e.g., contact, and contactless capacitive sensors), combinationsthereof, and the like.

The light sensors are or may be configured to sense objects orinteractions via electromagnetic radiation in, for example, thefar-infrared, near-infrared, optical, and/or ultraviolet spectra. Inembodiments, the light sensors may include image sensors (e.g.,charge-coupled devices and CMOS active-pixel sensors), photoelectricsensors (e.g., through-beam sensors, retroreflective sensors, anddiffuse sensors), combinations thereof, and the like. Further, the lightsensors may be implemented as part of a system or subsystem, such as alight detection and ranging (“LIDAR”) sensor.

The acoustic sensors are or may be configured to sense objects orinteractions via sound waves that are emitted and/or received by theacoustic sensors. In embodiments, the acoustic sensors may includeinfrasonic, sonic, and/or ultrasonic sensors. Further, the acousticsensors may be grouped as part of a system or subsystem, such as a soundnavigation and ranging (“SONAR”) sensor.

In embodiments, the digital twin system 15500 stores and collects datafrom a set of proximity sensors within the environment 15520 or portionsthereof. The collected data may be stored, for example, in the digitaltwin datastore 15516 for use by components the digital twin system 15500and/or visualization by a user. Such use and/or visualization may occurcontemporaneously with or after collection of the data (e.g., duringlater analysis and/or optimization of processes).

In embodiments, data collection may occur in response to a triggeringcondition. These triggering conditions may include, for example,expiration of a static or a dynamic predetermined interval, obtaining avalue short of or in excess of a static or dynamic value, receiving anautomatically generated request or instruction from the digital twinsystem 15500 or components thereof, interaction of an element with therespective sensor or sensors (e.g., in response to a worker or machinebreaking a beam or coming within a predetermined distance from theproximity sensor), interaction of a user with a digital twin (e.g.,selection of an environmental digital twin, a sensor array digital twin,or a sensor digital twin), combinations thereof, and the like.

In some embodiments, the digital twin system 15500 collects and/orstores RFID data in response to interaction of a worker with areal-world element 157302 r. For example, in response to a workerinteraction with a real-world environment, the digital twin will collectand/or store RFID data from RFID sensors within or associated with thecorresponding environment 15520. Additionally or alternatively, workerinteraction with a sensor-array digital twin will collect and/or storeRFID data from RFID sensors within or associated with the correspondingsensor array. Similarly, worker interaction with a sensor digital twinwill collect and/or store RFID data from the corresponding sensor. TheRFID data may include suitable data attainable by RFID sensors such asproximate RFID tags, RFID tag position, authorized RFID tags,unauthorized RFID tags, unrecognized RFID tags, RFID type (e.g., activeor passive), error codes, combinations thereof, and the like.

In embodiments, the digital twin system 15500 may further embed outputsfrom one or more devices within a corresponding digital twin. Inembodiments, the digital twin system 15500 embeds output from a set ofindividual-associated devices into an industrial digital twin. Forexample, the digital twin I/O system 15504 may receive informationoutput from one or more wearable devices 15554 or mobile devices (notshown) associated with an individual within an industrial environment.The wearable devices may include image capture devices (e.g., bodycameras or augmented-reality headwear), navigation devices (e.g., GPSdevices, inertial guidance systems), motion trackers, acoustic capturedevices (e.g., microphones), radiation detectors, combinations thereof,and the like.

In embodiments, upon receiving the output information, the digital twinI/O system 15504 routes the information to the digital twin creationmodule 15564 to check and/or update the environment digital twin and/orassociated digital twins within the environment (e.g., a digital twin ofa worker, machine, or robot position at a given time). Further, thedigital twin system 15500 may use the embedded output to determinecharacteristics of the environment 15520.

In embodiments, the digital twin system 15500 embeds output from a LIDARpoint cloud system into an industrial digital twin. For example, thedigital twin I/O system 15504 may receive information output from one ormore Lidar devices 15538 within an industrial environment. The Lidardevices 15538 is configured to provide a plurality of points havingassociated position data (e.g., coordinates in absolute or relative x,y, and z values). Each of the plurality of points may include furtherLIDAR attributes, such as intensity, return number, total returns, lasercolor data, return color data, scan angle, scan direction, etc. TheLidar devices 15538 may provide a point cloud that includes theplurality of points to the digital twin system 15500 via, for example,the digital twin I/O system 15504. Additionally or alternatively, thedigital twin system 15500 may receive a stream of points and assemblethe stream into a point cloud, or may receive a point cloud and assemblethe received point cloud with existing point cloud data, map data, orthree dimensional (3D)-model data.

In embodiments, upon receiving the output information, the digital twinI/O system 15504 routes the point cloud information to the digital twincreation module 15564 to check and/or update the environment digitaltwin and/or associated digital twins within the environment (e.g., adigital twin of a worker, machine, or robot position at a given time).In some embodiments, the digital twin system 15500 is further configuredto determine closed-shape objects within the received LIDAR data. Forexample, the digital twin system 15500 may group a plurality of pointswithin the point cloud as an object and, if necessary, estimateobstructed faces of objects (e.g., a face of the object contacting oradjacent a floor or a face of the object contacting or adjacent anotherobject such as another piece of equipment). The system may use suchclosed-shape objects to narrow search space for digital twins andthereby increase efficiency of matching algorithms (e.g., ashape-matching algorithm).

In embodiments, the digital twin system 15500 embeds output from asimultaneous location and mapping (“SLAM”) system in an environmentaldigital twin. For example, the digital twin I/O system 15504 may receiveinformation output from the SLAM system, such as Slam sensor 15562, andembed the received information within an environment digital twincorresponding to the location determined by the SLAM system. Inembodiments, upon receiving the output information from the SLAM system,the digital twin I/O system 15504 routes the information to the digitaltwin creation module 15564 to check and/or update the environmentdigital twin and/or associated digital twins within the environment(e.g., a digital twin of a workpiece, furniture, movable object, orautonomous object). Such updating provides digital twins ofnon-connected elements (e.g., furnishings or persons) automatically andwithout need of user interaction with the digital twin system 15500.

In embodiments, the digital twin system 15500 can leverage known digitaltwins to reduce computational requirements for the Slam sensor 15562 byusing suboptimal map-building algorithms. For example, the suboptimalmap-building algorithms may allow for a higher uncertainty toleranceusing simple bounded-region representations and identifying possibledigital twins. Additionally or alternatively, the digital twin system15500 may use a bounded-region representation to limit the number ofdigital twins, analyze the group of potential twins for distinguishingfeatures, then perform higher precision analysis for the distinguishingfeatures to identify and/or eliminate categories of, groups of, orindividual digital twins and, in the event that no matching digital twinis found, perform a precision scan of only the remaining areas to bescanned.

In embodiments, the digital twin system 15500 may further reduce computerequired to build a location map by leveraging data captured from othersensors within the environment (e.g., captured images or video, radioimages, etc.) to perform an initial map-building process (e.g., a simplebounded-region map or other suitable photogrammetry methods), associatedigital twins of known environmental objects with features of the simplebounded-region map to refine the simple bounded-region map, and performmore precise scans of the remaining simple bounded regions to furtherrefine the map. In some embodiments, the digital twin system 15500 maydetect objects within received mapping information and, for eachdetected object, determine whether the detected object corresponds to anexisting digital twin of a real-world-element. In response todetermining that the detected object does not correspond to an existingreal-world-element digital twin, the digital twin system 15500 may use,for example, the digital twin creation module 15564 to generate a newdigital twin corresponding to the detected object (e.g., adetected-object digital twin) and add the detected-object digital twinto the real-world-element digital twins within the digital twindatastore. Additionally or alternatively, in response to determiningthat the detected object corresponds to an existing real-world-elementdigital twin, the digital twin system 15500 may update thereal-world-element digital twin to include new information detected bythe simultaneous location and mapping sensor, if any.

In embodiments, the digital twin system 15500 represents locations ofautonomously or remotely moveable elements and attributes thereof withinan industrial digital twin. Such movable elements may include, forexample, workers, persons, vehicles, autonomous vehicles, robots, etc.The locations of the moveable elements may be updated in response to atriggering condition. Such triggering conditions may include, forexample, expiration of a static or a dynamic predetermined interval,receiving an automatically generated request or instruction from thedigital twin system 15500 or components thereof, interaction of anelement with a respective sensor or sensors (e.g., in response to aworker or machine breaking a beam or coming within a predetermineddistance from a proximity sensor), interaction of a user with a digitaltwin (e.g., selection of an environmental digital twin, a sensor arraydigital twin, or a sensor digital twin), combinations thereof, and thelike.

In embodiments, the time intervals may be based on probability of therespective movable element having moved within a time period. Forexample, the time interval for updating a worker location may berelatively shorter for workers expected to move frequently (e.g., aworker tasked with lifting and carrying objects within and through theenvironment 15520) and relatively longer for workers expected to moveinfrequently (e.g., a worker tasked with monitoring a process stream).Additionally or alternatively, the time interval may be dynamicallyadjusted based on applicable conditions, such as increasing the timeinterval when no movable elements are detected, decreasing the timeinterval as or when the number of moveable elements within anenvironment increases (e.g., increasing number of workers and workerinteractions), increasing the time interval during periods of reducedenvironmental activity (e.g., breaks such as lunch), decreasing the timeinterval during periods of abnormal environmental activity (e.g., tours,inspections, or maintenance), decreasing the time interval whenunexpected or uncharacteristic movement is detected (e.g., frequentmovement by a typically sedentary element or coordinated movement, forexample, of workers approaching an exit or moving cooperatively to carrya large object), combinations thereof, and the like. Further, the timeinterval may also include additional, semi-random acquisitions. Forexample, occasional mid-interval locations may be acquired by thedigital twin system 15500 to reinforce or evaluate the efficacy of theparticular time interval.

In embodiments, the digital twin system 15500 may analyze data receivedfrom the digital twin I/O system 15504 to refine, remove, or addconditions. For example, the digital twin system 15500 may optimize datacollection times for movable elements that are updated more frequentlythan needed (e.g., multiple consecutive received positions beingidentical or within a predetermined margin of error).

In embodiments, the digital twin system 15500 may receive, identify,and/or store a set of states 15840 a-n related to the environment 15520.The states 15840 a-n may be, for example, data structures that include aplurality of attributes 158404 a-n and a set of identifying criteria158406 a-n to uniquely identify each respective state 15840 a-n. Inembodiments, the states 15840 a-n may correspond to states where it isdesirable for the digital twin system 15500 to set or alter conditionsof real-world elements 157302 r and/or the environment 15520 (e.g.,increase/decrease monitoring intervals, alter operating conditions,etc.).

In embodiments, the set of states 15840 a-n may further include, forexample, minimum monitored attributes for each state 15840 a-n, the setof identifying criteria 158406 a-n for each state 15840 a-n, and/oractions available to be taken or recommended to be taken in response toeach state 15840 a-n. Such information may be stored by, for example,the digital twin datastore 15516 or another datastore. The states 15840a-n or portions thereof may be provided to, determined by, or altered bythe digital twin system 15500. Further, the set of states 15840 a-n mayinclude data from disparate sources. For example, details to identifyand/or respond to occurrence of a first state may be provided to thedigital twin system 15500 via user input, details to identify and/orrespond to occurrence of a second state may be provided to the digitaltwin system 15500 via an external system, details to identify and/orrespond to occurrence of a third state may be determined by the digitaltwin system 15500 (e.g., via simulations or analysis of process data),and details to identify and/or respond to occurrence of a fourth statemay be stored by the digital twin system 15500 and altered as desired(e.g., in response to simulated occurrence of the state or analysis ofdata collected during an occurrence of and response to the state).

In embodiments, the plurality of attributes 158404 a-n includes at leastthe attributes 158404 a-n needed to identify the respective state 15840a-n. The plurality of attributes 158404 a-n may further includeadditional attributes that are or may be monitored in determining therespective state 15840 a-n, but are not needed to identify therespective state 15840 a-n. For example, the plurality of attributes158404 a-n for a first state may include relevant information such asrotational speed, fuel level, energy input, linear speed, acceleration,temperature, strain, torque, volume, weight, etc.

The set of identifying criteria 158406 a-n may include information foreach of the set of attributes 158404 a-n to uniquely identify therespective state. The identifying criteria 158406 a-n may include, forexample, rules, thresholds, limits, ranges, logical values, conditions,comparisons, combinations thereof, and the like.

The change in operating conditions or monitoring may be any suitablechange. For example, after identifying occurrence of a respective state158406 a-n, the digital twin system 15500 may increase or decreasemonitoring intervals for a device (e.g., decreasing monitoring intervalsin response to a measured parameter differing from nominal operation)without altering operation of the device. Additionally or alternatively,the digital twin system 15500 may alter operation of the device (e.g.,reduce speed or power input) without altering monitoring of the device.In further embodiments, the digital twin system 15500 may alteroperation of the device (e.g., reduce speed or power input) and altermonitoring intervals for the device (e.g., decreasing monitoringintervals).

FIG. 158 illustrates an example set of identified states 15840 a-nrelated to industrial environments that the digital twin system 15500may identify and/or store for access by intelligent systems (e.g., thecognitive intelligence system 15510) or users of the digital twin system15500, according to some embodiments of the present disclosure. Thestates 15840 a-n may include operational states (e.g., suboptimal,normal, optimal, critical, or alarm operation of one or morecomponents), excess or shortage states (e.g., supply-side, oroutput-side quantities), combinations thereof, and the like.

In embodiments, the digital twin system 15500 may monitor attributes158404 a-n of real-world elements 157302 r and/or digital twins 157302 dto determine the respective state 15840 a-n. The attributes 158404 a-nmay be, for example, operating conditions, set points, critical points,status indicators, other sensed information, combinations thereof, andthe like. For example, the attributes 158404 a-n may include power input158404 a, operational speed 158404 b, critical speed 158404 c, andoperational temperature 158404 d of the monitored elements. While theillustrated example illustrates uniform monitored attributes, themonitored attributes may differ by target device (e.g., the digital twinsystem 15500 would not monitor rotational speed for an object with norotatable components).

Each of the states 15840 a-n includes a set of identifying criteria158406 a-n meeting particular criteria that are unique among the groupof monitored states 13240 a-n. The digital twin system 15500 mayidentify the overspeed state 15540 a, for example, in response to themonitored attributes 158404 a-n meeting a first set of identifyingcriteria 158406 a (e.g., operational speed 158404 b being higher thanthe critical speed 158404 c, while the operational temperature 158404 dis nominal).

In response to determining that one or more states 15840 a-n exists orhas occurred, the digital twin system 15500 may update triggeringconditions for one or more monitoring protocols, issue an alert ornotification, or trigger actions of subcomponents of the digital twinsystem 15500. For example, subcomponents of the digital twin system15500 may take actions to mitigate and/or evaluate impacts of thedetected states 15540 a-n. When attempting to take actions to mitigateimpacts of the detected states 15540 a-n on real-world elements 157302r, the digital twin system 15500 may determine whether instructionsexist (e.g., are stored in the digital twin datastore 15516) or shouldbe developed (e.g., developed via simulation and cognitive intelligenceor via user or worker input). Further, the digital twin system 15500 mayevaluate impacts of the detected states 15540 a-n, for example,concurrently with the mitigation actions or in response to determiningthat the digital twin system 15500 has no stored mitigation instructionsfor the detected states 15540 a-n.

In embodiments, the digital twin system 15500 employs the digital twinsimulation system 15506 to simulate one or more impacts, such asimmediate, upstream, downstream, and/or continuing effects, ofrecognized states. The digital twin simulation system 15506 may collectand/or be provided with values relevant to the evaluated states 15540a-n. In simulating the impact of the one or more states 15540 a-n, thedigital twin simulation system 15506 may recursively evaluateperformance characteristics of affected digital twins 157302 d untilconvergence is achieved. The digital twin simulation system 15506 maywork, for example, in tandem with the cognitive intelligence system15510 to determine response actions to alleviate, mitigate, inhibit,and/or prevent occurrence of the one or more states 15540 a-n. Forexample, the digital twin simulation system 15506 may recursivelysimulate impacts of the one or more states 15540 a-n until achieving adesired fit (e.g., convergence is achieved), provide the simulatedvalues to the cognitive intelligence system 15510 for evaluation anddetermination of potential actions, receive the potential actions,evaluate impacts of each of the potential actions for a respectivedesired fit (e.g., cost functions for minimizing production disturbance,preserving critical components, minimizing maintenance and/or downtime,optimizing system, worker, user, or personal safety, etc.).

In embodiments, the digital twin simulation system 15506 and thecognitive intelligence system 15510 may repeatedly share and update thesimulated values and response actions for each desired outcome untildesired conditions are met (e.g., convergence for each evaluated costfunction for each evaluated action). The digital twin system 15500 maystore the results in the digital twin datastore 15516 for use inresponse to determining that one or more states 15540 a-n has occurred.Additionally, simulations and evaluations by the digital twin simulationsystem 15506 and/or the cognitive intelligence system 15510 may occur inresponse to occurrence or detection of the event.

In embodiments, simulations and evaluations are triggered only whenassociated actions are not present within the digital twin system 15500.In further embodiments, simulations and evaluations are performedconcurrently with use of stored actions to evaluate the efficacy oreffectiveness of the actions in real time and/or evaluate whetherfurther actions should be employed or whether unrecognized states mayhave occurred. In embodiments, the cognitive intelligence system 15510may also be provided with notifications of instances of undesiredactions with or without data on the undesired aspects or results of suchactions to optimize later evaluations.

In embodiments, the digital twin system 15500 evaluates and/orrepresents the impact of machine downtime within a digital twin of amanufacturing facility. For example, the digital twin system 15500 mayemploy the digital twin simulation system 15506 to simulate theimmediate, upstream, downstream, and/or continuing effects of a machinedowntime state 15540 b. The digital twin simulation system 15506 maycollect or be provided with performance-related values such as optimal,suboptimal, and minimum performance requirements for elements (e.g.,real-world elements 157302 r and/or nested digital twins 157302 d)within the affected digital twins 157302 d, and/or characteristicsthereof that are available to the affected digital twins 157302 d,nested digital twins 157302 d, redundant systems within the affecteddigital twins 157302 d, combinations thereof, and the like.

In embodiments, the digital twin system 15500 is configured to: simulateone or more operating parameters for the real-world elements in responseto the industrial environment being supplied with given characteristicsusing the real-world-element digital twins; calculate a mitigatingaction to be taken by one or more of the real-world elements in responseto being supplied with the contemporaneous characteristics; and actuate,in response to detecting the contemporaneous characteristics, themitigating action. The calculation may be performed in response todetecting contemporaneous characteristics or operating parametersfalling outside of respective design parameters or may be determined viaa simulation prior to detection of such characteristics.

Additionally or alternatively, the digital twin system 15500 may providealerts to one or more users or system elements in response to detectingstates.

In embodiments (FIG. 157 ), the digital twin I/O system 15504 includes apathing module 157310. The pathing module 157310 may ingest navigationaldata from the elements 157302, provide and/or request navigational datato components of the digital twin system 15500 (e.g., the digital twinsimulation system 15506, the digital twin behavior system, and/or thecognitive intelligence system 15510), and/or output navigational data toelements 157302 (e.g., to the wearable devices 15554). The navigationaldata may be collected or estimated using, for example, historical data,guidance data provided to the elements 157302, combinations thereof, andthe like.

For example, the navigational data may be collected or estimated usinghistorical data stored by the digital twin system 15500. The historicaldata may include or be processed to provide information such asacquisition time, associated elements 157302, polling intervals, taskperformed, laden or unladen conditions, whether prior guidance data wasprovided and/or followed, conditions of the environment 15520, otherelements 157302 within the environment 15520, combinations thereof, andthe like. The estimated data may be determined using one or moresuitable pathing algorithms. For example, the estimated data may becalculated using suitable order-picking algorithms, suitable path-searchalgorithms, combinations thereof, and the like. The order-pickingalgorithm may be, for example, a largest gap algorithm, an s-shapealgorithm, an aisle-by-aisle algorithm, a combined algorithm,combinations thereof, and the like. The path-search algorithms may be,for example, Dijkstra's algorithm, the A* algorithm, hierarchicalpath-finding algorithms, incremental path-finding algorithms, any anglepath-finding algorithms, flow field algorithms, combinations thereof,and the like.

Additionally or alternatively, the navigational data may be collected orestimated using guidance data of the worker. The guidance data mayinclude, for example, a calculated route provided to a device of theworker (e.g., a mobile device or the wearable device 15554). In anotherexample, the guidance data may include a calculated route provided to adevice of the worker that instructs the worker to collect vibrationmeasurements from one or more locations on one or more machines alongthe route. The collected and/or estimated navigational data may beprovided to a user of the digital twin system 15500 for visualization,used by other components of the digital twin system 15500 for analysis,optimization, and/or alteration, provided to one or more elements157302, combinations thereof, and the like.

In embodiments, the digital twin system 15500 ingests navigational datafor a set of workers for representation in a digital twin. Additionallyor alternatively, the digital twin system 15500 ingests navigationaldata for a set of mobile equipment assets of an industrial environmentinto a digital twin.

In embodiments, the digital twin system 15500 ingests a system formodeling traffic of mobile elements in an industrial digital twin. Forexample, the digital twin system 15500 may model traffic patterns forworkers or persons within the environment 15520, mobile equipmentassets, combinations thereof, and the like. The traffic patterns may beestimated based on modeling traffic patterns from and historical dataand contemporaneous ingested data. Further, the traffic patterns may becontinuously or intermittently updated depending on conditions withinthe environment 15520 (e.g., a plurality of autonomous mobile equipmentassets may provide information to the digital twin system 15500 at aslower update interval than the environment 15520 including both workersand mobile equipment assets).

The digital twin system 15500 may alter traffic patterns (e.g., byproviding updated navigational data to one or more of the mobileelements) to achieve one or more predetermined criteria. Thepredetermined criteria may include, for example, increasing processefficiency, decreasing interactions between laden workers and mobileequipment assets, minimizing worker path length, routing mobileequipment around paths or potential paths of persons, combinationsthereof, and the like.

In embodiments, the digital twin system 15500 may provide traffic dataand/or navigational information to mobile elements in an industrialdigital twin. The navigational information may be provided asinstructions or rule sets, displayed path data, or selective actuationof devices. For example, the digital twin system 15500 may provide a setof instructions to a robot to direct the robot to and/or along a desiredroute for collecting vibration data from one or more specified locationson one or more specified machines along the route using a vibrationsensor. The robot may communicate updates to the system includingobstructions, reroutes, unexpected interactions with other assets withinthe environment 15520, etc.

In some embodiments, an ant-based system 15574 enables industrialentities, including robots, to lay a trail with one or more messages forother industrial entities, including themselves, to follow in laterjourneys. In embodiments, the messages include information related tovibration measurement collection. In embodiments, the messages includeinformation related to vibration sensor measurement locations. In someembodiments, the trails may be configured to fade over time. In someembodiments, the ant-based trails may be experienced via an augmentedreality system. In some embodiments, the ant-based trails may beexperienced via a virtual reality system. In some embodiments, theant-based trails may be experienced via a mixed reality system. In someembodiments, ant-based tagging of areas can trigger a pain-responseand/or accumulate into a warning signal. In embodiments, the ant-basedtrails may be configured to generate an information filtering response.In some embodiments, the ant-based trails may be configured to generatean information filtering response wherein the information filteringresponse is a heightened sense of visual awareness. In some embodiments,the ant-based trails may be configured to generate an informationfiltering response wherein the information filtering response is aheightened sense of acoustic awareness. In some embodiments, themessages include vectorized data.

In embodiments, the digital twin system 15500 includes designspecification information for representing a real-world element 157302 rusing a digital twin 157302 d. The digital may correspond to an existingreal-world element 157302 r or a potential real-world element 157302 r.The design specification information may be received from one or moresources. For example, the design specification information may includedesign parameters set by user input, determined by the digital twinsystem 15500 (e.g., the via digital twin simulation system 15506),optimized by users or the digital twin simulation system 15506,combinations thereof, and the like. The digital twin simulation system15506 may represent the design specification information for thecomponent to users, for example, via a display device or a wearabledevice. The design specification information may be displayedschematically (e.g., as part of a process diagram or table ofinformation) or as part of an augmented reality or virtual realitydisplay. The design specification information may be displayed, forexample, in response to a user interaction with the digital twin system15500 (e.g., via user selection of the element or user selection togenerally include design specification information within displays).Additionally or alternatively, the design specification information maybe displayed automatically, for example, upon the element coming withinview of an augmented reality or virtual reality device. In embodiments,the displayed design specification information may further includeindicia of information source (e.g., different displayed colors indicateuser input versus digital twin system 15500 determination), indicia ofmismatches (e.g., between design specification information andoperational information), combinations thereof, and the like.

In embodiments, the digital twin system 15500 embeds a set of controlinstructions for a wearable device within an industrial digital twin,such that the control instructions are provided to the wearable deviceto induce an experience for a wearer of the wearable device uponinteraction with an element of the industrial digital twin. The inducedexperience may be, for example, an augmented reality experience or avirtual reality experience. The wearable device, such as a headset, maybe configured to output video, audio, and/or haptic feedback to thewearer to induce the experience. For example, the wearable device mayinclude a display device and the experience may include display ofinformation related to the respective digital twin. The informationdisplayed may include maintenance data associated with the digital twin,vibration data associated with the digital twin, vibration measurementlocation data associated with the digital twin, financial dataassociated with the digital twin, such as a profit or loss associatedwith operation of the digital twin, manufacturing KPIs associated withthe digital twin, information related to an occluded element (e.g., asub-assembly) that is at least partially occluded by a foregroundelement (e.g., a housing), a virtual model of the occluded elementoverlaid on the occluded element and visible with the foregroundelement, operating parameters for the occluded element, a comparison toa design parameter corresponding to the operating parameter displayed,combinations thereof, and the like. Comparisons may include, forexample, altering display of the operating parameter to change a color,size, and/or display period for the operating parameter.

In some embodiments, the displayed information may include indicia forremovable elements that are or may be configured to provide access tothe occluded element with each indicium being displayed proximate to oroverlying the respective removable element. Further, the indicia may besequentially displayed such that a first indicium corresponding to afirst removable element (e.g., a housing) is displayed, and a secondindicium corresponding to a second removable element (e.g., an accesspanel within the housing) is displayed in response to the workerremoving the first removable element. In some embodiments, the inducedexperience allows the wearer to see one or more locations on a machinefor optimal vibration measurement collection. In an example, the digitaltwin system 15500 may provide an augmented reality view that includeshighlighted vibration measurement collection locations on a machineand/or instructions related to collecting vibration measurements.Furthering the example, the digital twin system 15500 may provide anaugmented reality view that includes instructions related to timing ofvibration measurement collection. Information utilized in displaying thehighlighted placement locations may be obtained using information storedby the digital twin system 15500. In some embodiments, mobile elementsmay be tracked by the digital twin system 15500 (e.g., via observationalelements within the environment 15520 and/or via pathing informationcommunicated to the digital twin system 15500) and continually displayedby the wearable device within the occluded view of the worker. Thisoptimizes movement of elements within the environment 15520, increasesworker safety, and minimizes down time of elements resulting fromdamage.

In some embodiments, the digital twin system 15500 may provide anaugmented reality view that displays mismatches between designparameters or expected parameters of real-world elements 157302 r to thewearer. The displayed information may correspond to real-world elements157302 r that are not within the view of the wearer (e.g., elementswithin another room or obscured by machinery). This allows the worker toquickly and accurately troubleshoot mismatches to determine one or moresources for the mismatch. The cause of the mismatch may then bedetermined, for example, by the digital twin system 15500 and correctiveactions ordered. In example embodiments, a wearer may be able to viewmalfunctioning subcomponents of machines without removing occludingelements (e.g., housings or shields). Additionally or alternatively, thewearer may be provided with instructions to repair the device, forexample, including display of the removal process (e.g., location offasteners to be removed), assemblies or subassemblies that should betransported to other areas for repair (e.g., dust-sensitive components),assemblies or subassemblies that need lubrication, and locations ofobjects for reassembly (e.g., storing location that the wearer hasplaced removed objects and directing the wearer or another wearer to thestored locations to expedite reassembly and minimize further disassemblyor missing parts in the reassembled element). This can expedite repairwork, minimize process impact, allow workers to disassemble andreassemble equipment (e.g., by coordinating disassembly without directcommunication between the workers), increase equipment longevity andreliability (e.g., by assuring that all components are properly replacedprior to placing back in service), combinations thereof, and the like.

In some embodiments, the induced experience includes a virtual realityview or an augmented reality view that allows the wearer to seeinformation related to existing or planned elements. The information maybe unrelated to physical performance of the element (e.g., financialperformance such as asset value, energy cost, input material cost,output material value, legal compliance, and corporate operations). Oneor more wearers may perform a virtual walkthrough or an augmentedwalkthrough of the industrial environment 15520.

In examples, the wearable device displays compliance information thatexpedites inspections or performance of work.

In further examples, the wearable device displays financial informationthat is used to identify targets for alteration or optimization. Forexample, a manager or officer may inspect the environment 15520 forcompliance with updated regulations, including information regardingcompliance with former regulations, “grandfathered,” and/or exceptedelements. This can be used to reduce unnecessary downtime (e.g.,scheduling upgrades for the least impactful times, such as duringplanned maintenance cycles), prevent unnecessary upgrades (e.g.,replacing grandfathered or excepted equipment), and reduce capitalinvestment.

Referring back to FIG. 155 , in embodiments, the digital twin system15500 may include, integrate, integrate with, manage, handle, link to,take input from, provide output to, control, coordinate with, orotherwise interact with a digital twin dynamic model system 15508. Thedigital twin dynamic model system 15508 can update the properties of aset of digital twins of a set of industrial entities and/orenvironments, including properties of physical industrial assets,workers, processes, manufacturing facilities, warehouses, and the like(or any of the other types of entities or environments described in thisdisclosure or in the documents incorporated by reference herein) in sucha manner that the digital twins may represent those industrial entitiesand environments, and properties or attributes thereof, in real-time orvery near real-time. In some embodiments, the digital twin dynamic modelsystem 15508 may obtain sensor data received from a sensor system 15530and may determine one or more properties of an industrial environment oran industrial entity within an environment based on the sensor data andbased on one or more dynamic models.

In embodiments, the digital twin dynamic model system 15508 mayupdate/assign values of various properties in a digital twin and/or oneor more embedded digital twins, including, but not limited to, vibrationvalues, vibration fault level states, probability of failure values,probability of downtime values, cost of downtime values, probability ofshutdown values, financial values, KPI values, temperature values,humidity values, heat flow values, fluid flow values, radiation values,substance concentration values, velocity values, acceleration values,location values, pressure values, stress values, strain values, lightintensity values, sound level values, volume values, shapecharacteristics, material characteristics, and dimensions.

In embodiments, a digital twin may be comprised of (e.g., via reference)of other embedded digital twins. For example, a digital twin of amanufacturing facility may include an embedded digital twin of a machineand one or more embedded digital twins of one or more respective motorsenclosed within the machine. A digital twin may be embedded, forexample, in the memory of an industrial machine that has an onboard ITsystem (e.g., the memory of an Onboard Diagnostic System, control system(e.g., SCADA system) or the like). Other non-limiting examples of wherea digital twin may be embedded include the following: on a wearabledevice of a worker; in memory on a local network asset, such as aswitch, router, access point, or the like; in a cloud computing resourcethat is provisioned for an environment or entity; and on an asset tag orother memory structure that is dedicated to an entity.

In one example, the digital twin dynamic model system 15508 can updatevibration characteristics throughout an industrial environment digitaltwin based on captured vibration sensor data measured at one or morelocations in the industrial environment and one or more dynamic modelsthat model vibration within the industrial environment digital twin. Theindustrial digital twin may, before updating, already containinformation about properties of the industrial entities and/orenvironment that can be used to feed a dynamic model, such asinformation about materials, shapes/volumes (e.g., of conduits),positions, connections/interfaces, and the like, such that vibrationcharacteristics can be represented for the entities and/or environmentin the digital twin. Alternatively, the dynamic models may be configuredusing such information.

In embodiments, the digital twin dynamic model system 15508 can updatethe properties of a digital twin and/or one or more embedded digitaltwins on behalf of a client application 15570. In embodiments, a clientapplication 15570 may be an application relating to an industrialcomponent or environment (e.g., monitoring an industrial facility or acomponent within, simulating an industrial environment, or the like). Inembodiments, the client application 15570 may be used in connection withboth fixed and mobile data collection systems. In embodiments, theclient application 15570 may be used in connection with IndustrialInternet of Things sensor system 15530.

In embodiments, the digital twin dynamic model system 15508 leveragesdigital twin dynamic models 155100 to model the behavior of anindustrial entity and/or environment. Dynamic models 155100 may enabledigital twins to represent physical reality, including the interactionsof industrial entities, by using a limited number of measurements toenrich the digital representation of an industrial entity and/orenvironment, such as based on scientific principles. In embodiments, thedynamic models 155100 are formulaic or mathematical models. Inembodiments, the dynamic models 155100 adhere to scientific laws, lawsof nature, and formulas (e.g., Newton's laws of motion, second law ofthermodynamics, Bernoulli's principle, ideal gas law, Dalton's law ofpartial pressures, Hooke's law of elasticity, Fourier's law of heatconduction, Archimedes' principle of buoyancy, and the like). Inembodiments, the dynamic models are machine-learned models.

In embodiments, the digital twin system 15500 may have a digital twindynamic model datastore 155102 for storing dynamic models 155100 thatmay be represented in digital twins. In embodiments, digital twindynamic model datastore can be searchable and/or discoverable. Inembodiments, digital twin dynamic model datastore can contain metadatathat allows a user to understand what characteristics a given dynamicmodel can handle, what inputs are required, what outputs are provided,and the like. In some embodiments, digital twin dynamic model datastore155102 can be hierarchical (such as where a model can be deepened ormade more simple based on the extent of available data and/or inputs,the granularity of the inputs, and/or situational factors (such as wheresomething becomes of high interest and a higher fidelity model isaccessed for a period of time).

In embodiments, a digital twin or digital representation of anindustrial entity or facility may include a set of data structures thatcollectively define a set of properties of a represented physicalindustrial asset, device, worker, process, facility, and/or environment,and/or possible behaviors thereof. In embodiments, the digital twindynamic model system 15508 may leverage the dynamic models 155100 toinform the set of data structures that collectively define a digitaltwin with real-time data values. The digital twin dynamic models 155100may receive one or more sensor measurements, Industrial Internet ofThings device data, and/or other suitable data as inputs and calculateone or more outputs based on the received data and one or more dynamicmodels 155100. The digital twin dynamic model system 15508 then uses theone or more outputs to update the digital twin data structures.

In one example, the set of properties of a digital twin of an industrialasset that may be updated by the digital twin dynamic model system 15508using dynamic models 155100 may include the vibration characteristics ofthe asset, temperature(s) of the asset, the state of the asset (e.g., asolid, liquid, or gas), the location of the asset, the displacement ofthe asset, the velocity of the asset, the acceleration of the asset,probability of downtime values associated with the asset, cost ofdowntime values associated with the asset, probability of shutdownvalues associated with the asset, manufacturing KPIs associated with theasset, financial information associated with the asset, heat flowcharacteristics associated with the asset, fluid flow rates associatedwith the asset (e.g., fluid flow rates of a fluid flowing through apipe), identifiers of other digital twins embedded within the digitaltwin of the asset and/or identifiers of digital twins embedding thedigital twin of the asset, and/or other suitable properties. Dynamicmodels 155100 associated with a digital twin of an asset can beconfigured to calculate, interpolate, extrapolate, and/or output valuesfor such asset digital twin properties based on input data collectedfrom sensors and/or devices disposed in the industrial setting and/orother suitable data and subsequently populate the asset digital twinwith the calculated values.

In some embodiments, the set of properties of a digital twin of anindustrial device that may be updated by the digital twin dynamic modelsystem 15508 using dynamic models 155100 may include the status of thedevice, a location of the device, the temperature(s) of a device, atrajectory of the device, identifiers of other digital twins that thedigital twin of the device is embedded within, embeds, is linked to,includes, integrates with, takes input from, provides output to, and/orinteracts with and the like. Dynamic models 155100 associated with adigital twin of a device can be configured to calculate or output valuesfor these device digital twin properties based on input data andsubsequently update the device digital twin with the calculated values.

In some embodiments, the set of properties of a digital twin of anindustrial worker that may be updated by the digital twin dynamic modelsystem 15508 using dynamic models 155100 may include the status of theworker, the location of the worker, a stress measure for the worker, atask being performed by the worker, a performance measure for theworker, and the like. Dynamic models associated with a digital twin ofan industrial worker can be configured to calculate or output values forsuch properties based on input data, which then may be used to populateindustrial worker digital twin. In embodiments, industrial workerdynamic models (e.g., psychometric models) can be configured to predictreactions to stimuli, such as cues that are given to workers to directthem to undertake tasks and/or alerts or warnings that are intended toinduce safe behavior. In embodiments, industrial worker dynamic modelsmay be workflow models (Gantt charts and the like), failure mode effectsanalysis models (FMEA), biophysical models (such as to model workerfatigue, energy utilization, hunger), and the like.

Example properties of a digital twin of an industrial environment thatmay be updated by the digital twin dynamic model system 15508 usingdynamic models 155100 may include the dimensions of the industrialenvironment, the temperature(s) of the industrial environment, thehumidity value(s) of the industrial environment, the fluid flowcharacteristics in the industrial environment, the heat flowcharacteristics of the industrial environment, the lightingcharacteristics of the industrial environment, the acousticcharacteristics of the industrial environment the physical objects inthe environment, processes occurring in the industrial environment,currents of the industrial environment (if a body of water), and thelike. Dynamic models associated with a digital twin of an industrialenvironment can be configured to calculate or output these propertiesbased on input data collected from sensors and/or devices disposed inthe industrial environment and/or other suitable data and subsequentlypopulate the industrial environment digital twin with the calculatedvalues.

In embodiments, dynamic models 155100 may adhere to physical limitationsthat define boundary conditions, constants, or variables for digitaltwin modeling. For example, the physical characterization of a digitaltwin of an industrial entity or industrial environment may include agravity constant (e.g., 9.8 m/s2), friction coefficients of surfaces,thermal coefficients of materials, maximum temperatures of assets,maximum flow capacities, and the like. Additionally or alternatively,the dynamic models may adhere to the laws of nature. For example,dynamic models may adhere to the laws of thermodynamics, laws of motion,laws of fluid dynamics, laws of buoyancy, laws of heat transfer, laws orradiation, laws of quantum dynamics, and the like. In some embodiments,dynamic models may adhere to biological aging theories or mechanicalaging principles. Thus, when the digital twin dynamic model system 15508facilitates a real-time digital representation, the digitalrepresentation may conform to dynamic models, such that the digitalrepresentations mimic real world conditions. In some embodiments, theoutput(s) from a dynamic model can be presented to a human user and/orcompared against real-world data to ensure convergence of the dynamicmodels with the real world. Furthermore, as dynamic models are basedpartly on assumptions, the properties of a digital twin may be improvedand/or corrected when a real-world behavior differs from that of thedigital twin. In embodiments, additional data collection and/orinstrumentation can be recommended based on the recognition that aninput is missing from a desired dynamic model, that a model in operationis not working as expected (perhaps due to missing and/or faulty sensorinformation), that a different result is needed (such as due tosituational factors that make something of high interest), and the like.

Dynamic models may be obtained from a number of different sources. Insome embodiments, a user can upload a model created by the user or athird party. Additionally or alternatively, the models may be created onthe digital twin system using a graphical user interface. The dynamicmodels may include bespoke models that are configured for a particularenvironment and/or set of industrial entities and/or agnostic modelsthat are applicable to similar types of digital twins. The dynamicmodels may be machine-learned models.

FIG. 159 illustrates example embodiments of a method for updating a setof properties of a digital twin and/or one or more embedded digitaltwins on behalf of client applications 15570. In embodiments, digitaltwin dynamic model system 15508 leverages one or more dynamic models155100 to update a set of properties of a digital twin and/or one ormore embedded digital twins on behalf of client application 15570 basedon the impact of collected sensor data from sensor system 15530, datacollected from Internet of Things connected devices 15524, and/or othersuitable data in the set of dynamic models 155100 that are used toenable the industrial digital twins. In embodiments, the digital twindynamic model system 15508 may be instructed to run specific dynamicmodels using one or more digital twins that represent physicalindustrial assets, devices, workers, processes, and/or industrialenvironments that are managed, maintained, and/or monitored by theclient applications 15570.

In embodiments, the digital twin dynamic model system 15508 may obtaindata from other types of external data sources that are not necessarilyindustrial-related data sources, but may provide data that can be usedas input data for the dynamic models. For example, weather data, newsevents, social media data, and the like may be collected, crawled,subscribed to, and the like to supplement sensor data, IndustrialInternet of Things device data, and/or other data that is used by thedynamic models. In embodiments, the digital twin dynamic model system15508 may obtain data from a machine vision system. The machine visionsystem may use video and/or still images to provide measurements (e.g.,locations, statuses, and the like) that may be used as inputs by thedynamic models.

In embodiments, the digital twin dynamic model system 15508 may feedthis data into one or more of the dynamic models discussed above toobtain one or more outputs. These outputs may include calculatedvibration fault level states, vibration severity unit values, vibrationcharacteristics, probability of failure values, probability of downtimevalues, probability of shutdown values, cost of downtime values, cost ofshutdown values, time to failure values, temperature values, pressurevalues, humidity values, precipitation values, visibility values, airquality values, strain values, stress values, displacement values,velocity values, acceleration values, location values, performancevalues, financial values, manufacturing KPI values, electrodynamicvalues, thermodynamic values, fluid flow rate values, and the like. Theclient application 15570 may then initiate a digital twin visualizationevent using the results obtained by the digital twin dynamic modelsystem 15508. In embodiments, the visualization may be a heat mapvisualization.

In embodiments, the digital twin dynamic model system 15508 may receiverequests to update one or more properties of digital twins of industrialentities and/or environments such that the digital twins represent theindustrial entities and/or environments in real-time. At 159100, thedigital twin dynamic model system 15508 receives a request to update oneor more properties of one or more of the digital twins of industrialentities and/or environments. For example, the digital twin dynamicmodel system 15508 may receive the request from a client application15570 or from another process executed by the digital twin system 15500(e.g., a predictive maintenance process). The request may indicate theone or more properties and the digital twin or digital twins implicatedby the request. Referring to FIG. 159 , in step 159102, the digital twindynamic model system 15508 determines the one or more digital twinsrequired to fulfill the request and retrieves the one or more requireddigital twins, including any embedded digital twins, from digital twindatastore 15516. At 159104, digital twin dynamic model system 15508determines one or more dynamic models required to fulfill the requestand retrieves the one or more required dynamic models from digital twindynamic model datastore 155102. At 159106, the digital twin dynamicmodel system 15508 selects one or more sensors from sensor system 15530,data collected from Internet of Things connected devices 15524, and/orother data sources from digital twin I/O system 15504 based on availabledata sources and the one or more required inputs of the dynamicmodel(s). In embodiments, the data sources may be defined in the inputsrequired by the one or more dynamic models or may be selected using alookup table. At 159108, the digital twin dynamic model system 15508retrieves the selected data from digital twin I/O system 15504. At159110, digital twin dynamic model system 15508 runs the dynamicmodel(s) using the retrieved input data (e.g., vibration sensor data,Industrial Internet of Things device data, and the like) as inputs anddetermines one or more output values based on the dynamic model(s) andthe input data. At 159112, the digital twin dynamic model system 15508updates the values of one or more properties of the one or more digitaltwins based on the one or more outputs of the dynamic model(s).

In example embodiments, client application 15570 may be configured toprovide a digital representation and/or visualization of the digitaltwin of an industrial entity. In embodiments, the client application15570 may include one or more software modules that are executed by oneor more server devices. These software modules may be configured toquantify properties of the digital twin, model properties of a digitaltwin, and/or to visualize digital twin behaviors. In embodiments, thesesoftware modules may enable a user to select a particular digital twinbehavior visualization for viewing. In embodiments, these softwaremodules may enable a user to select to view a digital twin behaviorvisualization playback. In some embodiments, the client application15570 may provide a selected behavior visualization to digital twindynamic model system 15508.

In embodiments, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to update properties of a digitaltwin in order to enable a digital representation of an industrial entityand/or environment wherein the real-time digital representation is avisualization of the digital twin. In embodiments, a digital twin may berendered by a computing device, such that a human user can view thedigital representations of real-world industrial assets, devices,workers, processes and/or environments. For example, the digital twinmay be rendered and outcome to a display device. In embodiments, dynamicmodel outputs and/or related data may be overlaid on the rendering ofthe digital twin. In embodiments, dynamic model outputs and/or relatedinformation may appear with the rendering of the digital twin in adisplay interface. In embodiments, the related information may includereal-time video footage associated with the real-world entityrepresented by the digital twin. In embodiments, the related informationmay include a sum of each of the vibration fault level states in themachine. In embodiments, the related information may be graphicalinformation. In embodiments, the graphical information may depict motionand/or motion as a function of frequency for individual machinecomponents. In embodiments, graphical information may depict motionand/or motion as a function of frequency for individual machinecomponents, wherein a user is enabled to select a view of the graphicalinformation in the x, y, and z dimensions. In embodiments, graphicalinformation may depict motion and/or motion as a function of frequencyfor individual machine components, wherein the graphical informationincludes harmonic peaks and peaks. In embodiments, the relatedinformation may be cost data, including the cost of downtime per daydata, cost of repair data, cost of new part data, cost of new machinedata, and the like. In embodiments, related information may be aprobability of downtime data, probability of failure data, and the like.In embodiments, related information may be time to failure data.

In embodiments, the related information may be recommendations and/orinsights. For example, recommendations or insights received from thecognitive intelligence system related to a machine may appear with therendering of the digital twin of a machine in a display interface.

In embodiments, clicking, touching, or otherwise interacting with thedigital twin rendered in the display interface can allow a user to“drill down” and see underlying subsystems or processes and/or embeddeddigital twins. For example, in response to a user clicking on a machinebearing rendered in the digital twin of a machine, the display interfacecan allow a user to drill down and see information related to thebearing, view a 3D visualization of the bearing's vibration, and/or viewa digital twin of the bearing.

In embodiments, clicking, touching, or otherwise interacting withinformation related to the digital twin rendered in the displayinterface can allow a user to “drill down” and see underlyinginformation.

FIG. 160 illustrates example embodiments of a display interface thatrenders the digital twin of a dryer centrifuge and other informationrelated to the dryer centrifuge.

In some embodiments, the digital twin may be rendered and output in avirtual reality display. For example, a user may view a 3D rendering ofan environment (e.g., using a monitor or a virtual reality headset). Theuser may also inspect and/or interact with digital twins of industrialentities. In embodiments, a user may view processes being performed withrespect to one or more digital twins (e.g., collecting measurements,movements, interactions, inventorying, loading, packing, shipping, andthe like). In embodiments, a user may provide input that controls one ormore properties of a digital twin via a graphical user interface.

In some embodiments, the digital twin dynamic model system 15508 mayreceive requests from client application 15570 to update properties of adigital twin in order to enable a digital representation of industrialentities and/or environments wherein the digital representation is aheat map visualization of the digital twin. In embodiments, a platformis provided having heat maps displaying collected data from the sensorsystem 15530, Internet of Things connected devices 15524, and dataoutputs from dynamic models 155100 for providing input to a displayinterface. In embodiments, the heat map interface is provided as anoutput for digital twin data, such as for handling and providinginformation for visualization of various sensor data, dynamic modeloutput data, and other data (such as map data, analog sensor data, andother data), such as to another system, such as a mobile device, tablet,dashboard, computer, AR/VR device, or the like. A digital twinrepresentation may be provided in a form factor (e.g., user device,VR-enabled device, AR-enabled device, or the like) suitable fordelivering visual input to a user, such as the presentation of a mapthat includes indicators of levels of analog sensor data, digital sensordata, and output values from the dynamic models (such as data indicatingvibration fault level states, vibration severity unit values,probability of downtime values, cost of downtime values, probability ofshutdown values, time to failure values, probability of failure values,manufacturing KPIs, temperatures, levels of rotation, vibrationcharacteristics, fluid flow, heating or cooling, pressure, substanceconcentrations, and many other output values). In embodiments, signalsfrom various sensors or input sources (or selective combinations,permutations, mixes, and the like) as well as data determined by thedigital twin dynamic model system 15508 may provide input data to a heatmap. Coordinates may include real world location coordinates (such asgeo-location or location on a map of an environment), as well as othercoordinates, such as time-based coordinates, frequency-basedcoordinates, or other coordinates that allow for representation ofanalog sensor signals, digital signals, dynamic model outputs, inputsource information, and various combinations, in a map-basedvisualization, such that colors may represent varying levels of inputalong the relevant dimensions. For example, among many otherpossibilities, if an industrial machine component is at a criticalvibration fault level state, the heat map interface may alert a user byshowing the machine component in orange. In the example of a heat map,clicking, touching, or otherwise interacting with the heat map can allowa user to drill down and see underlying sensor, dynamic model outputs,or other input data that is used as an input to the heat map display. Inother examples, such as ones where a digital twin is displayed in a VRor AR environment, if an industrial machine component is vibratingoutside of normal operation (e.g., at a suboptimal, critical, or alarmvibration fault level), a haptic interface may induce vibration when auser touches a representation of the machine component, or if a machinecomponent is operating in an unsafe manner, a directional sound signalmay direct a user's attention toward the machine in digital twin, suchas by playing in a particular speaker of a headset or other soundsystem.

In embodiments, the digital twin dynamic model system 15508 may take aset of ambient environmental data and/or other data and automaticallyupdate a set of properties of a digital twin of an industrial entity orfacility based on the impact of the environmental data and/or other datain the set of dynamic models 155100 that are used to enable the digitaltwin. Ambient environmental data may include temperature data, pressuredata, humidity data, wind data, rainfall data, tide data, storm surgedata, cloud cover data, snowfall data, visibility data, water leveldata, and the like. Additionally or alternatively, the digital twindynamic model system 15508 may use a set of environmental datameasurements collected by a set of Internet of Things connected devices15524 disposed in an industrial setting as inputs for the set of dynamicmodels 155100 that are used to enable the digital twin. For example,digital twin dynamic model system 15508 may feed the dynamic models155100 data collected, handled or exchanged by Internet of Thingsconnected devices 15524, such as cameras, monitors, embedded sensors,mobile devices, diagnostic devices and systems, instrumentation systems,telematics systems, and the like, such as for monitoring variousparameters and features of machines, devices, components, parts,operations, functions, conditions, states, events, workflows and otherelements (collectively encompassed by the term “states”) of industrialenvironments. Other examples of Internet of Things connected devicesinclude smart fire alarms, smart security systems, smart air qualitymonitors, smart/learning thermostats, and smart lighting systems.

FIG. 161 illustrates example embodiments of a method for updating a setof vibration fault level states for a set of bearings in a digital twinof a machine. In this example, a client application 15570, whichinterfaces with digital twin dynamic model system 15508, may beconfigured to provide a visualization of the fault level states of thebearings in the digital twin of the machine.

In this example, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to update the vibration faultlevel states of the machine digital twin. At 161200, digital twindynamic model system 15508 receives a request from client application15570 to update one or more vibration fault level states of the machinedigital twin. Next, in step 161202, digital twin dynamic model system15508 determines the one or more digital twins required to fulfill therequest and retrieves the one or more required digital twins fromdigital twin datastore 15516. In this example, the digital twin dynamicmodel system 15508 may retrieve the digital twin of the machine and anyembedded digital twins, such as any embedded motor digital twins andbearing digital twins, and any digital twins that embed the machinedigital twin, such as the manufacturing facility digital twin. At161204, digital twin dynamic model system 15508 determines one or moredynamic models required to fulfill the request and retrieves the one ormore required dynamic models from the digital twin dynamic modeldatastore 155102. At 161206, the digital twin dynamic model system 15508selects dynamic model input data sources (e.g., one or more sensors fromsensor system 15530, data from Internet of Things connected devices15524, and any other suitable data) via digital twin I/O system 15504based on available data sources (e.g., available sensors from a set ofsensors in sensor system 15530) and the and the one or more requiredinputs of the dynamic model(s). In the present example, the retrieveddynamic model(s) 155100 may take one or more vibration sensormeasurements from vibration sensors 15536 as inputs to the dynamicmodels. In embodiments, vibration sensors 15536 may be optical vibrationsensors, single axis vibration sensors, tri-axial vibration sensors, andthe like. At 161208, digital twin dynamic model system 15508 retrievesone or more measurements from each of the selected data sources from thedigital twin I/O system 15504. Next, At 161210, digital twin dynamicmodel system 15508 runs the dynamic model(s), using the retrievedvibration sensor measurements as inputs, and calculates one or moreoutputs that represent bearing vibration fault level states. Next, At161212, the digital twin dynamic model system 15508 updates one or morebearing fault level states of the manufacturing facility digital twin,machine digital twin, motor digital twin, and/or bearing digital twinsbased on the one or more outputs of the dynamic model(s). The clientapplication 15570 may obtain vibration fault level states of thebearings and may display the obtained vibration fault level stateassociated with each bearing and/or display colors associated with faultlevel severity (e.g., red for alarm, orange for critical, yellow forsuboptimal, green for normal operation) in the rendering of one or moreof the digital twins on a display interface.

In another example, a client application 15570 may be an augmentedreality application. In some embodiments of this example, the clientapplication 15570 may obtain vibration fault level states of bearings ina field of view of an AR-enabled device (e.g., smart glasses) hostingthe client application from the digital twin of the industrialenvironment (e.g., via an API of the digital twin system 15500) and maydisplay the obtained vibration fault level states on the display of theAR-enabled device, such that the vibration fault level state displayedcorresponds to the location in the field of view of the AR-enableddevice. In this way, a vibration fault level state may be displayed evenif there are no vibration sensors located within the field of view ofthe AR-enabled device.

FIG. 162 illustrates example embodiments of a method for updating a setof vibration severity unit values of bearings in a digital twin of amachine. Vibration severity units may be measured as displacement,velocity, and acceleration.

In this example, client application 15570 that interfaces with thedigital twin dynamic model system 15508 may be configured to provide avisualization of the three-dimensional vibration characteristics ofbearings in a digital twin of a machine.

In this example, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to update the vibration severityunit values for bearings in the digital twin of a machine. At 162300,digital twin dynamic model system 15508 receives a request from clientapplication 15570 to update one or more vibration severity unit value(s)of the manufacturing facility digital twin. Next, in step 162302,digital twin dynamic model system 15508 determines the one or moredigital twins required to fulfill the request and retrieves the one ormore required digital twins from digital twin datastore 15516. In thisexample, the digital twin dynamic model system 15508 may retrieve thedigital twin of the machine and any embedded digital twins (e.g.,digital twins of bearings and other components). At 162304, digital twindynamic model system 15508 determines one or more dynamic modelsrequired to fulfill the request and retrieves the one or more requireddynamic models from dynamic model datastore 155102. At 162306, thedigital twin dynamic model system 15508 selects dynamic model input datasources (e.g., one or more sensors from sensor system 15530, data fromInternet of Things connected devices 15524, and any other suitable data)via digital twin I/O system 15504 based on available data sources (e.g.,available sensors from a set of sensors in sensor system 15530) and theone or more required inputs of the dynamic model(s). In the presentexample, the retrieved dynamic models may be configured to take one ormore vibration sensor measurements as inputs and provide severity unitvalues for bearings in the machine. At 162308, digital twin dynamicmodel system 15508 retrieves one or more measurements from each of theselected sensors. In the present example, the digital twin dynamic modelsystem 15508 retrieves measurements from vibration sensors 15536 viadigital twin I/O system 15504. At 162310, digital twin dynamic modelsystem 15508 runs the dynamic model(s) using the retrieved vibrationmeasurements as inputs and calculates one or more output values thatrepresent vibration severity unit values for bearings in the machine.Next, at 162312, the digital twin dynamic model system 15508 updates oneor more vibration severity unit values of the bearings in the machinedigital twin and all other embedded digital twins or digital twins thatembed the machine digital twin based on the one or more values output bythe dynamic model(s).

FIG. 163 illustrates example embodiments of a method for updating a setof probability of failure values for machine components in the digitaltwin of a machine.

In this example, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to update the probability offailure values for components in a machine digital twin. At 156400,digital twin dynamic model system 15508 receives a request from clientapplication 15570 to update one or more probability of failure value(s)of the machine digital twin, any embedded component digital twins, andany digital twins that embed the machine digital twin such as amanufacturing facility digital twin. Next, in step 163402, digital twindynamic model system 15508 determines the one or more digital twinsrequired to fulfill the request and retrieves the one or more requireddigital twins. In this example, the digital twin dynamic model system15508 may retrieve the digital twin of the manufacturing facility, thedigital twin of the machine, and the digital twins of machine componentsfrom digital twin datastore 15516. At 163404, digital twin dynamic modelsystem 15508 determines one or more dynamic models required to fulfillthe request and retrieves the one or more required dynamic models fromdynamic model datastore 155102. At 163406, the digital twin dynamicmodel system 15508 selects, via digital twin I/O system 15504, dynamicmodel input data sources (e.g., one or more sensors from sensor system15530, data from Internet of Things connected devices 15524, and anyother suitable data) based on available data sources (e.g., availablesensors from a set of sensors in sensor system 15530) and the and theone or more required inputs of the dynamic model(s). In the presentexample, the retrieved dynamic models may take one or more vibrationmeasurements from vibration sensors 15536 and historical failure data asdynamic model inputs and output probability of failure values for themachine components in the digital twin of the machine. At 163408,digital twin dynamic model system 15508 retrieves data from each of theselected sensors and/or Internet of Things connected devices via digitaltwin I/O system 15504. At 163410, digital twin dynamic model system15508 runs the dynamic model(s) using the retrieved vibration data andhistorical failure data as inputs and calculates one or more outputsthat represent probability of failure values for bearings in the machinedigital twin. Next, At 163412, the digital twin dynamic model system15508 updates one or more probability of failure values of the bearingsin the machine digital twin, all embedded digital twins, and all digitaltwins that embed the machine digital twin based on the output of thedynamic model(s).

FIG. 164 illustrates example embodiments of a method for updating a setof probability of downtime for machines in the digital twin of amanufacturing facility.

In this example, client application 15570, which interfaces with thedigital twin dynamic model system 15508, may be configured to provide avisualization of the probability of downtime values of a manufacturingfacility in the digital twin of the manufacturing facility.

In this example, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to assign probability of downtimevalues to machines in a manufacturing facility digital twin. At 164500,digital twin dynamic model system 16208 receives a request from clientapplication 15570 to update one or more probability of downtime valuesof machines in the manufacturing facility digital twin and any embeddeddigital twins such as the individual machine digital twins. Next, instep 164502, digital twin dynamic model system 15508 determines the oneor more digital twins required to fulfill the request and retrieves theone or more required digital twins from digital twin datastore 15516. Inthis example, the digital twin dynamic model system 15508 may retrievethe digital twin of the manufacturing facility and any embedded digitaltwins from digital twin datastore 15516. At 164504, digital twin dynamicmodel system 15508 determines one or more dynamic models required tofulfill the request and retrieves the one or more required dynamicmodels from dynamic model datastore 155102. At 164506, the digital twindynamic model system 15508 selects dynamic model input data sources(e.g., one or more sensors from sensor system 15530, data from Internetof Things connected devices 15524, and any other suitable data) based onavailable data sources (e.g., available sensors from a set of sensors insensor system 15530) and the and the one or more required inputs of thedynamic model(s) via digital twin I/O system 15504. In the presentexample, the dynamic model(s) may be configured to take vibrationmeasurements from vibration sensors and historical downtime data asinputs and output probability of downtime values for different machinesthroughout the manufacturing facility. At 164508, digital twin dynamicmodel system 15508 retrieves one or more measurements from each of theselected sensors via digital twin I/O system 15504. At 164510, digitaltwin dynamic model system 15508 runs the dynamic model(s) using theretrieved vibration measurements and historical downtime data as inputsand calculates one or more outputs that represent probability ofdowntime values for machines in the manufacturing facility. Next, At164512, the digital twin dynamic model system 15508 updates one or moreprobability of downtime values for machines in the manufacturingfacility digital twins and all embedded digital twins based on the oneor more outputs of the dynamic models.

FIG. 165 illustrates example embodiments of a method for updating one ormore probability of shutdown values in the digital twin of an enterprisehaving a set of manufacturing facilities.

In the present example, the digital twin dynamic model system 15508 mayreceive requests from client application 15570 to update the probabilityof shutdown values for the set of manufacturing facilities within anenterprise digital twin. At 165600, digital twin dynamic model system15508 receives a request from client application 15570 to update one ormore probability of shutdown values of the enterprise digital twin andany embedded digital twins. Next, in step 165602, digital twin dynamicmodel system 15508 determines the one or more digital twins required tofulfill the request and retrieves the one or more required digital twinsfrom digital twin datastore 15516. In this example, the digital twindynamic model system 15508 may retrieve the digital twin of theenterprise and any embedded digital twins. At 165604, digital twindynamic model system 15508 determines one or more dynamic modelsrequired to fulfill the request and retrieves the one or more requireddynamic models from dynamic model datastore 155102. At 165606, thedigital twin dynamic model system 15508 selects dynamic model input datasources (e.g., one or more sensors from sensor system 15530, data fromInternet of Things connected devices 15524, and any other suitable data)based on available data sources (e.g., available sensors from a set ofsensors in sensor system 15530) and the and the one or more requiredinputs of the dynamic model(s) via digital twin I/O system 15504. In thepresent example, the retrieved dynamic models may be configured to takeone or more vibration measurements from vibration sensors 15536 and/orother suitable data as inputs and output probability of shutdown valuesfor each manufacturing entity in the enterprise digital twin. At 165608,digital twin dynamic model system 15508 retrieves one or more vibrationmeasurements from each of the selected vibration sensors 15536 fromdigital twin I/O system 15504. At 165610, digital twin dynamic modelsystem 15508 runs the dynamic model(s) using the retrieved vibrationmeasurements and historical shut down data as inputs and calculates oneor more outputs that represent probability of shutdown values formanufacturing facilities within the enterprise digital twin. Next, at165612, the digital twin dynamic model system 15508 updates one or moreprobability of shutdown values of the enterprise digital twin and allembedded digital twins based on the one or more outputs of the dynamicmodel(s).

FIG. 166 illustrates example embodiments of a method for updating a setof cost of downtime values in machines in the digital twin of amanufacturing facility. In the present example, the digital twin dynamicmodel system 15508 may receive requests from a client application 15570to populate real-time cost of downtime values associated with machinesin a manufacturing facility digital twin. At 166700, digital twindynamic model system 15508 receives a request from the clientapplication 15570 to update one or more cost of downtime values of themanufacturing facility digital twin and any embedded digital twins(e.g., machines, machine parts, and the like) from the clientapplication 15570. Next, in step 166702, the digital twin dynamic modelsystem 15508 determines the one or more digital twins required tofulfill the request and retrieves the one or more required digitaltwins. In this example, the digital twin dynamic model system 15508 mayretrieve the digital twins of the manufacturing facility, the machines,the machine parts, and any other embedded digital twins from digitaltwin datastore 15516. At 166704, digital twin dynamic model system 15508determines one or more dynamic models required to fulfill the requestand retrieves the one or more required dynamic models from dynamic modeldatastore 155102. At 166706, the digital twin dynamic model system 15508selects dynamic model input data sources (e.g., one or more sensors fromsensor system 15530, data from Internet of Things connected devices15524, and any other suitable data) based on available data sources(e.g., available sensors from a set of sensors in sensor system 15530)and the and the one or more required inputs of the dynamic model(s) viadigital twin I/O system 15504. In the present example, the retrieveddynamic model(s) may be configured to take historical downtime data andoperational data as inputs and output data representing cost of downtimeper day for machines in the manufacturing facility. At 166708, digitaltwin dynamic model system 15508 retrieves historical downtime data andoperational data from digital twin I/O system 15504. At 166710, digitaltwin dynamic model system 15508 runs the dynamic model(s) using theretrieved data as input and calculates one or more outputs thatrepresent cost of downtime per day for machines in the manufacturingfacility. Next, at 166712, the digital twin dynamic model system 15508updates one or more cost of downtime values of the manufacturingfacility digital twins and machine digital twins based on the one ormore outputs of the dynamic model(s).

FIG. 167 illustrates example embodiments of a method for updating a setof manufacturing KPI values in the digital twin of a manufacturingfacility. In embodiments, the manufacturing KPI is selected from the setof uptime, capacity utilization, on standard operating efficiency,overall operating efficiency, overall equipment effectiveness, machinedowntime, unscheduled downtime, machine set up time, inventory turns,inventory accuracy, quality (e.g., percent defective), first pass yield,rework, scrap, failed audits, on-time delivery, customer returns,training hours, employee turnover, reportable health & safety incidents,revenue per employee, and profit per employee, schedule attainment,total cycle time, throughput, changeover time, yield, plannedmaintenance percentage, availability, and customer return rate.

In the present example, the digital twin dynamic model system 15508 mayreceive requests from a client application 15570 to populate real-timemanufacturing KPI values in a manufacturing facility digital twin. At167800, digital twin dynamic model system 15508 receives a request fromthe client application 15570 to update one or more KPI values of themanufacturing facility digital twin and any embedded digital twins(e.g., machines, machine parts, and the like) from the clientapplication 15570. Next, in step 167802, the digital twin dynamic modelsystem 15508 determines the one or more digital twins required tofulfill the request and retrieves the one or more required digitaltwins. In this example, the digital twin dynamic model system 15508 mayretrieve the digital twins of the manufacturing facility, the machines,the machine parts, and any other embedded digital twins from digitaltwin datastore 15516. At 167804, digital twin dynamic model system 15508determines one or more dynamic models required to fulfill the requestand retrieves the one or more required dynamic models from dynamic modeldatastore 155102. At 167806, the digital twin dynamic model system 15508selects dynamic model input data sources (e.g., one or more sensors fromsensor system 15530, data from Internet of Things connected devices15524, and any other suitable data) based on available data sources(e.g., available sensors from a set of sensors in sensor system 15530)and the and the one or more required inputs of the dynamic model(s) viadigital twin I/O system 15504. In the present example, the retrieveddynamic model(s) may be configured to take one or more vibrationmeasurements obtained from vibration sensors 15536 and other operationaldata as inputs and output one or more manufacturing KPIs for thefacility. At 167808, digital twin dynamic model system 15508 retrievesone or more vibration measurements from each of the selected vibrationsensors 15536 and operational data from digital twin I/O system 15504.At 167810, digital twin dynamic model system 15508 runs the dynamicmodel(s) using the retrieved vibration measurements and operational dataas inputs and calculates one or more outputs that representmanufacturing KPIs for the manufacturing facility. Next, At 167812, thedigital twin dynamic model system 15508 updates one or more KPI valuesof the manufacturing facility digital twins, machine digital twins,machine part digital twins, and all other embedded digital twins basedon the one or more outputs of the dynamic model(s).

Further embodiments may include the following examples. FIG. 155illustrates an example environment of a digital twin system 15500. Inembodiments, the digital twin system 15500 generates a set of digitaltwins of a set of industrial environments 15520 and/or industrialentities within the set of industrial environments. In embodiments, thedigital twin system 15500 maintains a set of states of the respectiveindustrial environments 15520, such as using sensor data obtained fromrespective sensor systems 15530 that monitor the industrial environments15520. In embodiments, the digital twin system 15500 may include adigital twin management system 15502, a digital twin I/O system 15504, adigital twin simulation system 15506, a digital twin dynamic modelsystem 15508, a cognitive intelligence system 15510, and/or anenvironment control module 15512. In embodiments, the digital twinsystem 15500 may provide a real time sensor API that provides a set ofcapabilities for enabling a set of interfaces for the sensors of therespective sensor systems 15530. In embodiments, the digital twin system15500 may include and/or employ other suitable APIs, brokers,connectors, bridges, gateways, hubs, ports, routers, switches, dataintegration systems, peer-to-peer systems, and the like to facilitatethe transferring of data to and from the digital twin system 15500. Inthese embodiments, these connective components may allow an IoT sensoror an intermediary device (e.g., a relay, an edge device, a switch, orthe like) within a sensor system 15530 to communicate data to thedigital twin system 155300 and/or to receive data (e.g., configurationdata, control data, or the like) from the digital twin system 15500 oranother external system. In embodiments, the digital twin system 15500may further include a digital twin datastore 15516 that stores digitaltwins 15518 of various industrial environments 15520 and the objects15522, devices 15524, sensors 15526, and/or humans 15528 in theenvironment 15520.

A digital twin may refer to a digital representation of one or moreindustrial entities, such as an industrial environment 15520, a physicalobject 15522, a device 15524, a sensor 15526, a human 15528, or anycombination thereof. Examples of industrial environments 15520 include,but are not limited to, a factory, a power plant, a food productionfacility (which may include an inspection facility), a commercialkitchen, an indoor growing facility, a natural resources excavation site(e.g., a mine, an oil field, etc.), and the like. Depending on the typeof environment, the types of objects, devices, and sensors that arefound in the environments will differ. Non-limiting examples of physicalobjects 15522 include raw materials, manufactured products, excavatedmaterials, containers (e.g., boxes, dumpsters, cooling towers, vats,pallets, barrels, palates, bins, and the like), furniture (e.g., tables,counters, workstations, shelving, etc.), and the like. Non-limitingexamples of devices 15524 include robots, computers, vehicles (e.g.,cars, trucks, tankers, trains, forklifts, cranes, etc.),machinery/equipment (e.g., tractors, tillers, drills, presses, assemblylines, conveyor belts, etc.), and the like. The sensors 15526 may be anysensor devices and/or sensor aggregation devices that are found in asensor system 15530 within an environment. Non-limiting examples ofsensors 15526 that may be implemented in a sensor system 15530 mayinclude temperature sensors 15532, humidity sensors 15534, vibrationsensors 15536, LIDAR sensors 15538, motion sensors 15540, chemicalsensors 15542, audio sensors 15544, pressure sensors 15546, weightsensors 15548, radiation sensors 15550, video sensors 15552, wearabledevices 15554, relays 15556, edge devices 15558, crosspoint switches15560, and/or any other suitable sensors. Examples of different types ofphysical objects 15522, devices 15524, sensors 15526, and environments15520 are referenced throughout the disclosure.

In some embodiments, on-device sensor fusion and data storage forindustrial IoT devices is supported, including on-device sensor fusionand data storage for an industrial IoT device, where data from multiplesensors is multiplexed at the device for storage of a fused data stream.For example, pressure and temperature data may be multiplexed into adata stream that combines pressure and temperature in a time series,such as in a byte-like structure (where time, pressure, and temperatureare bytes in a data structure, so that pressure and temperature remainlinked in time, without requiring separate processing of the streams byoutside systems), or by adding, dividing, multiplying, subtracting, orthe like, such that the fused data can be stored on the device. Any ofthe sensor data types described throughout this disclosure, includingvibration data, can be fused in this manner, and stored in a local datapool, in storage, or on an IoT device, such as a data collector, acomponent of a machine, or the like.

In some embodiments, a set of digital twins may represent an entireorganization, such as energy production organizations, oil and gasorganizations, renewable energy production organizations, aerospacemanufacturers, vehicle manufacturers, heavy equipment manufacturers,mining organizations, drilling organizations, offshore platformorganizations, and the like. In these examples, the digital twins mayinclude digital twins of one or more industrial facilities of theorganization.

In embodiments, the digital twin management system 15502 generatesdigital twins. A digital twin may be comprised of (e.g., via reference)other digital twins. In this way, a discrete digital twin may becomprised of a set of other discrete digital twins. For example, adigital twin of a machine may include digital twins of sensors on themachine, digital twins of components that make up the machine, digitaltwins of other devices that are incorporated in or integrated with themachine (such as systems that provide inputs to the machine or takeoutputs from it), and/or digital twins of products or other items thatare made by the machine. Taking this example one step further, a digitaltwin of an industrial facility (e.g., a factory) may include a digitaltwin representing the layout of the industrial facility, including thearrangement of physical assets and systems in or around the facility, aswell as digital assets of the assets within the facility (e.g., thedigital twin of the machine), as well as digital twins of storage areasin the facility, digital twins of humans collecting vibrationmeasurements from machines throughout the facility, and the like. Inthis second example, the digital twin of the industrial facility mayreference the embedded digital twins, which may then reference otherdigital twins embedded within those digital twins.

In some embodiments, a digital twin may represent abstract entities,such as workflows and/or processes, including inputs, outputs, sequencesof steps, decision points, processing loops, and the like that make upsuch workflows and processes. For example, a digital twin may be adigital representation of a manufacturing process, a logistics workflow,an agricultural process, a mineral extraction process, or the like. Inthese embodiments, the digital twin may include references to theindustrial entities that are included in the workflow or process. Thedigital twin of the manufacturing process may reflect the various stagesof the process. In some of these embodiments, the digital twin system15500 receives real-time data from the industrial facility (e.g., from asensor system 15530 of the environment 15520) in which the manufacturingprocess takes place and reflects a current (or substantially current)state of the process in real-time.

In embodiments, the digital representation may include a set of datastructures (e.g., classes) that collectively define a set of propertiesof a represented physical object 15522, device 15524, sensor 15526, orenvironment 15520 and/or possible behaviors thereof. For example, theset of properties of a physical object 15522 may include a type of thephysical object, the dimensions of the object, the mass of the object,the density of the object, the material(s) of the object, the physicalproperties of the material(s), the surface of the physical object, thestatus of the physical object, a location of the physical object,identifiers of other digital twins contained within the object, and/orother suitable properties. Examples of behavior of a physical object mayinclude a state of the physical object (e.g., a solid, liquid, or gas),a melting point of the physical object, a density of the physical objectwhen in a liquid state, a viscosity of the physical object when in aliquid state, a freezing point of the physical object, a density of thephysical object when in a solid state, a hardness of the physical objectwhen in a solid state, the malleability of the physical object, thebuoyancy of the physical object, the conductivity of the physicalobject, a burning point of the physical object, the manner by whichhumidity affects the physical object, the manner by which water or otherliquids affect the physical object, a terminal velocity of the physicalobject, and the like. In another example, the set of properties of adevice may include a type of the device, the dimensions of the device,the mass of the device, the density of the density of the device, thematerial(s) of the device, the physical properties of the material(s),the surface of the device, the output of the device, the status of thedevice, a location of the device, a trajectory of the device, vibrationcharacteristics of the device, identifiers of other digital twins thatthe device is connected to and/or contains, and the like. Examples ofthe behaviors of a device may include a maximum acceleration of adevice, a maximum speed of a device, ranges of motion of a device, aheating profile of a device, a cooling profile of a device, processesthat are performed by the device, operations that are performed by thedevice, and the like. Example properties of an environment may includethe dimensions of the environment, the boundaries of the environment,the temperature of the environment, the humidity of the environment, theairflow of the environment, the physical objects in the environment,currents of the environment (if a body of water), and the like. Examplesof behaviors of an environment may include scientific laws that governthe environment, processes that are performed in the environment, rulesor regulations that must be adhered to in the environment, and the like.

In embodiments, the properties of a digital twin may be adjusted. Forexample, the temperature of a digital twin, a humidity of a digitaltwin, the shape of a digital twin, the material of a digital twin, thedimensions of a digital twin, or any other suitable parameters may beadjusted. As the properties of the digital twin are adjusted, otherproperties may be affected as well. For example, if the temperature ofan environment 15520 is increased, the pressure within the environmentmay increase as well, such as a pressure of a gas in accordance with theideal gas law. In another example, if a digital twin of a subzeroenvironment is increased to above freezing temperatures, the propertiesof an embedded twin of water in a solid state (i.e., ice) may changeinto a liquid state over time.

Digital twins may be represented in a number of different forms. Inembodiments, a digital twin may be a visual digital twin that isrendered by a computing device, such that a human user can view digitalrepresentations of an environment 15520 and/or the physical objects15522, devices 15524, and/or the sensors 15526 within an environment. Inembodiments, the digital twin may be rendered and output to a displaydevice. In some of these embodiments, the digital twin may be renderedin a graphical user interface, such that a user may interact with thedigital twin. For example, a user may “drill down” on a particularelement (e.g., a physical object or device) to view additionalinformation regarding the element (e.g., a state of a physical object ordevice, properties of the physical object or device, or the like). Insome embodiments, the digital twin may be rendered and output in avirtual reality display. For example, a user may view a 3D rendering ofan environment (e.g., using monitor or a virtual reality headset). Whiledoing so, the user may view/inspect digital twins of physical assets ordevices in the environment.

In some embodiments, a data structure of the visual digital twins (i.e.,digital twins that are configured to be displayed in a 2D or 3D manner)may include surfaces (e.g., splines, meshes, polygons meshes, or thelike). In some embodiments, the surfaces may include texture data,shading information, and/or reflection data. In this way, a surface maybe displayed in a more realistic manner. In some embodiments, suchsurfaces may be rendered by a visualization engine (not shown) when thedigital twin is within a field of view and/or when existing in a largerdigital twin (e.g., a digital twin of an industrial environment). Inthese embodiments, the digital twin system 15500 may render the surfacesof digital objects, whereby a rendered digital twin may be depicted as aset of adjoined surfaces.

In embodiments, a user may provide input that controls one or moreproperties of a digital twin via a graphical user interface. Forexample, a user may provide input that changes a property of a digitaltwin. In response, the digital twin system 15500 can calculate theeffects of the changed property and may update the digital twin and anyother digital twins affected by the change of the property.

In embodiments, a user may view processes being performed with respectto one or more digital twins (e.g., manufacturing of a product,extracting minerals from a mine or well, a livestock inspection line,and the like). In these embodiments, a user may view the entire processor specific steps within a process.

In some embodiments, a digital twin (and any digital twins embeddedtherein) may be represented in a non-visual representation (or “datarepresentation”). In these embodiments, a digital twin and any embeddeddigital twins exist in a binary representation but the relationshipsbetween the digital twins are maintained. For example, in embodiments,each digital twin and/or the components thereof may be represented by aset of physical dimensions that define a shape of the digital twin (orcomponent thereof). Furthermore, the data structure embodying thedigital twin may include a location of the digital twin. In someembodiments, the location of the digital twin may be provided in a setof coordinates. For example, a digital twin of an industrial environmentmay be defined with respect to a coordinate space (e.g., a Cartesiancoordinate space, a polar coordinate space, or the like). Inembodiments, embedded digital twins may be represented as a set of oneor more ordered triples (e.g., [x coordinate, y coordinate, zcoordinates] or other vector-based representations). In some of theseembodiments, each ordered triple may represent a location of a specificpoint (e.g., center point, top point, bottom point, or the like) on theindustrial entity (e.g., object, device, or sensor) in relation to theenvironment in which the industrial entity resides. In some embodiments,a data structure of a digital twin may include a vector that indicates amotion of the digital twin with respect to the environment. For example,fluids (e.g., liquids or gasses) or solids may be represented by avector that indicates a velocity (e.g., direction and magnitude ofspeed) of the entity represented by the digital twin. In embodiments, avector within a twin may represent a microscopic subcomponent, such as aparticle within a fluid, and a digital twin may represent physicalproperties, such as displacement, velocity, acceleration, momentum,kinetic energy, vibrational characteristics, thermal properties,electromagnetic properties, and the like.

In some embodiments, a set of two or more digital twins may berepresented by a graph database that includes nodes and edges thatconnect the nodes. In some implementations, an edge may represent aspatial relationship (e.g., “abuts,” “rests upon,” “contains”, and thelike). In these embodiments, each node in the graph database representsa digital twin of an entity (e.g., an industrial entity) and may includethe data structure defining the digital twin. In these embodiments, eachedge in the graph database may represent a relationship between twoentities represented by connected nodes. In some implementations, anedge may represent a spatial relationship (e.g., “abuts,” “rests upon,”“interlocks with,” “bears”, “contains”, and the like). In embodiments,various types of data may be stored in a node or an edge. Inembodiments, a node may store property data, state data, and/or metadatarelating to a facility, system, subsystem, and/or component. Types ofproperty data and state data will differ based on the entity representedby a node. For example, a node representing a robot may include propertydata that indicates a material of the robot, the dimensions of the robot(or components thereof), a mass of the robot, and the like. In thisexample, the state data of the robot may include a current pose of therobot, a location of the robot, and the like. In embodiments, an edgemay store relationship data and metadata data relating to a relationshipbetween two nodes. Examples of relationship data may include the natureof the relationship, whether the relationship is permanent (e.g., afixed component would have a permanent relationship with the structureto which it is attached or resting on), and the like. In embodiments, anedge may include metadata concerning the relationship between twoentities. For example, if a product was produced on an assembly line,one relationship that may be documented between a digital twin of theproduct and the assembly line may be “created by.” In these embodiments,an example edge representing the “created by” relationship may include atimestamp indicating a date and time that the product was created. Inanother example, a sensor may take measurements relating to a state of adevice, whereby one relationship between the sensor and the device mayinclude “measured” and may define a measurement type that is measured bythe sensor. In this example, the metadata stored in an edge may includea list of N measurements taken and a timestamp of each respectivemeasurement. In this way, temporal data relating to the nature of therelationship between two entities may be maintained, thereby allowingfor an analytics engine, machine-learning engine, and/or visualizationengine to leverage such temporal relationship data, such as by aligningdisparate data sets with a series of points in time, such as tofacilitate cause-and-effect analysis used for prediction systems.

In some embodiments, a graph database may be implemented in ahierarchical manner, such that the graph database relates a set offacilities, systems, and components. For example, a digital twin of amanufacturing environment may include a node representing themanufacturing environment. The graph database may further include nodesrepresenting various systems within the manufacturing environment, suchas nodes representing an HVAC system, a lighting system, a manufacturingsystem, and the like, all of which may connect to the node representingthe manufacturing system. In this example, each of the systems mayfurther connect to various subsystems and/or components of the system.For example, within the HVAC system, the HVAC system may connect to asubsystem node representing a cooling system of the facility, a secondsubsystem node representing a heating system of the facility, a thirdsubsystem node representing the fan system of the facility, and one ormore nodes representing a thermostat of the facility (or multiplethermostats). Carrying this example further, the subsystem nodes and/orcomponent nodes may connect to lower level nodes, which may includesubsystem nodes and/or component nodes. For example, the subsystem noderepresenting the cooling subsystem may be connected to a component noderepresenting an air conditioner unit. Similarly, a component noderepresenting a thermostat device may connect to one or more componentnodes representing various sensors (e.g., temperature sensors, humiditysensors, and the like).

In embodiments where a graph database is implemented, a graph databasemay relate to a single environment or may represent a larger enterprise.In the latter scenario, a company may have various manufacturing anddistribution facilities. In these embodiments, an enterprise noderepresenting the enterprise may connect to environment nodes of eachrespective facility. In this way, the digital twin system 15500 maymaintain digital twins for multiple industrial facilities of anenterprise.

In embodiments, the digital twin system 15500 may use a graph databaseto generate a digital twin that may be rendered and displayed and/or maybe represented in a data representation. In the former scenario, thedigital twin system 15500 may receive a request to render a digitaltwin, whereby the request includes one or more parameters that areindicative of a view that will be depicted. For example, the one or moreparameters may indicate an industrial environment to be depicted and thetype of rendering (e.g., “real-world view” that depicts the environmentas a human would see it, an “infrared view” that depicts objects as afunction of their respective temperature, an “airflow view” that depictsthe airflow in a digital twin, or the like). In response, the digitaltwin system 15500 may traverse a graph database and may determine aconfiguration of the environment to be depicted based on the nodes inthe graph database that are related (either directly or through a lowerlevel node) to the environment node of the environment and the edgesthat define the relationships between the related nodes. Upondetermining a configuration, the digital twin system 15500 may identifythe surfaces that are to be depicted and may render those surfaces. Thedigital twin system 15500 may then render the requested digital twin byconnecting the surfaces in accordance with the configuration. Therendered digital twin may then be output to a viewing device (e.g., VRheadset, monitor, or the like). In some scenarios, the digital twinsystem 15500 may receive real-time sensor data from a sensor system15530 of an environment 15520 and may update the visual digital twinbased on the sensor data. For example, the digital twin system 15500 mayreceive sensor data (e.g., vibration data from a vibration sensor 15536)relating to a motor and its set of bearings. Based on the sensor data,the digital twin system 15500 may update the visual digital twin toindicate the approximate vibrational characteristics of the set ofbearings within a digital twin of the motor.

In scenarios where the digital twin system 15500 is providing datarepresentations of digital twins (e.g., for dynamic modeling,simulations, machine learning), the digital twin system 15500 maytraverse a graph database and may determine a configuration of theenvironment to be depicted based on the nodes in the graph database thatare related (either directly or through a lower level node) to theenvironment node of the environment and the edges that define therelationships between the related nodes. In some scenarios, the digitaltwin system 15500 may receive real-time sensor data from a sensor system15530 of an environment 15520 and may apply one or more dynamic modelsto the digital twin based on the sensor data. In other scenarios, a datarepresentation of a digital twin may be used to perform simulations, asis discussed in greater detail throughout the specification.

In some embodiments, the digital twin system 15500 may execute a digitalghost that is executed with respect to a digital twin of an industrialenvironment. In these embodiments, the digital ghost may monitor one ormore sensors of a sensor system 15530 of an industrial environment todetect anomalies that may indicate a malicious virus or other securityissues.

As discussed, the digital twin system 15500 may include a digital twinmanagement system 15502, a digital twin I/O system 15504, a digital twinsimulation system 15506, a digital twin dynamic model system 15508, acognitive intelligence system 15510, and/or an environment controlmodule 15512.

In embodiments, the digital twin management system 15502 creates newdigital twins, maintains/updates existing digital twins, and/or rendersdigital twins. The digital twin management system 15502 may receive userinput, uploaded data, and/or sensor data to create and maintain existingdigital twins. Upon creating a new digital twin, the digital twinmanagement system 15502 may store the digital twin in the digital twindatastore 15516. Creating, updating, and rendering digital twins arediscussed in greater detail throughout the disclosure.

In embodiments, the digital twin I/O system 15504 receives input fromvarious sources and outputs data to various recipients. In embodiments,the digital twin I/O system receives sensor data from one or more sensorsystems 15530. In these embodiments, each sensor system 15530 mayinclude one or more IoT sensors that output respective sensor data. Eachsensor may be assigned an IP address or may have another suitableidentifier. Each sensor may output sensor packets that include anidentifier of the sensor and the sensor data. In some embodiments, thesensor packets may further include a timestamp indicating a time atwhich the sensor data was collected. In some embodiments, the digitaltwin I/O system 15504 may interface with a sensor system 15530 via thereal-time sensor API 15514. In these embodiments, one or more devices(e.g., sensors, aggregators, edge devices) in the sensor system 15530may transmit the sensor packets containing sensor data to the digitaltwin I/O system 15504 via the API. The digital twin I/O system maydetermine the sensor system 15530 that transmitted the sensor packetsand the contents thereof, and may provide the sensor data and any otherrelevant data (e.g., time stamp, environment identifier/sensor systemidentifier, and the like) to the digital twin management system 15502.

In embodiments, the digital twin I/O system 15504 may receive importeddata from one or more sources. For example, the digital twin system15500 may provide a portal for users to create and manage their digitaltwins. In these embodiments, a user may upload one or more files (e.g.,image files, LIDAR scans, blueprints, and the like) in connection with anew digital twin that is being created. In response, the digital twinI/O system 15504 may provide the imported data to the digital twinmanagement system 15502. The digital twin I/O system 15504 may receiveother suitable types of data without departing from the scope of thedisclosure.

In some embodiments, the digital twin simulation system 15506 isconfigured to execute simulations using the digital twin. For example,the digital twin simulation system 15506 may iteratively adjust one ormore parameters of a digital twin and/or one or more embedded digitaltwins. In embodiments, the digital twin simulation system 15506, foreach set of parameters, executes a simulation based on the set ofparameters and may collect the simulation outcome data resulting fromthe simulation. Put another way, the digital twin simulation system15506 may collect the properties of the digital twin and the digitaltwins within or containing the digital twin used during the simulationas well as any outcomes stemming from the simulation. For example, inrunning a simulation on a digital twin of an indoor agriculturalfacility, the digital twin simulation system 15506 can vary thetemperature, humidity, airflow, carbon dioxide and/or other relevantparameters and can execute simulations that output outcomes resultingfrom different combinations of the parameters. In another example, thedigital twin simulation system 15506 may simulate the operation of aspecific machine within an industrial facility that produces an outputgiven a set of inputs. In some embodiments, the inputs may be varied todetermine an effect of the inputs on the machine and the output thereof.In another example, the digital twin simulation system 15506 maysimulate the vibration of a machine and/or machine components. In thisexample, the digital twin of the machine may include a set of operatingparameters, interfaces, and capabilities of the machine. In someembodiments, the operating parameters may be varied to evaluate theeffectiveness of the machine. The digital twin simulation system 15506is discussed in further detail throughout the disclosure.

In embodiments, the digital twin dynamic model system 15508 isconfigured to model one or more behaviors with respect to a digital twinof an environment. In embodiments, the digital twin dynamic model system15508 may receive a request to model a certain type of behaviorregarding an environment or a process and may model that behavior usinga dynamic model, the digital twin of the environment or process, andsensor data collected from one or more sensors that are monitoring theenvironment or process. For example, an operator of a machine havingbearings may wish to model the vibration of the machine and bearings todetermine whether the machine and/or bearings can withstand an increasein output. In this example, the digital twin dynamic model system 15508may execute a dynamic model that is configured to determine whether anincrease in output would result in adverse consequences (e.g., failures,downtime, or the like). The digital twin dynamic model system 15508 isdiscussed in further detail throughout the disclosure.

In embodiments, the cognitive processes system 15510 performs machinelearning and artificial intelligence related tasks on behalf of thedigital twin system. In embodiments, the cognitive processes system15510 may train any suitable type of model, including but not limited tovarious types of neural networks, regression models, random forests,decision trees, Hidden Markov models, Bayesian models, and the like. Inembodiments, the cognitive processes system 15510 trains machine learnedmodels using the output of simulations executed by the digital twinsimulation system 15506. In some of these embodiments, the outcomes ofthe simulations may be used to supplement training data collected fromreal-world environments and/or processes. In embodiments, the cognitiveprocesses system 15510 leverages machine learned models to makepredictions, identifications, classifications and provide decisionsupport relating to the real-world environments and/or processesrepresented by respective digital twins.

For example, a machine-learned prediction model may be used to predictthe cause of irregular vibrational patterns (e.g., a suboptimal,critical, or alarm vibration fault state) for a bearing of an engine inan industrial facility. In this example, the cognitive processes system15510 may receive vibration sensor data from one or more vibrationsensors disposed on or near the engine and may receive maintenance datafrom the industrial facility and may generate a feature vector based onthe vibration sensor data and the maintenance data. The cognitiveprocesses system 15510 may input the feature vector into amachine-learned model trained specifically for the engine (e.g., using acombination of simulation data and real-world data of causes ofirregular vibration patterns) to predict the cause of the irregularvibration patterns. In this example, the causes of the irregularvibrational patterns could be a loose bearing, a lack of bearinglubrication, a bearing that is out of alignment, a worn bearing, thephase of the bearing may be aligned with the phase of the engine, loosehousing, loose bolt, and the like.

In another example, a machine-learned model may be used to providedecision support to bring a bearing of an engine in an industrialfacility operating at a suboptimal vibration fault level state to anormal operation vibration fault level state. In this example, thecognitive processes system 15510 may receive vibration sensor data fromone or more vibration sensors disposed on or near the engine and mayreceive maintenance data from the industrial facility and may generate afeature vector based on the vibration sensor data and the maintenancedata. The cognitive processes system 15510 may input the feature vectorinto a machine-learned model trained specifically for the engine (e.g.,using a combination of simulation data and real-world data of solutionsto irregular vibration patterns) to provide decision support inachieving a normal operation fault level state of the bearing. In thisexample, the decision support could be a recommendation to tighten thebearing, lubricate the bearing, re-align the bearing, order a newbearing, order a new part, collect additional vibration measurements,change operating speed of the engine, tighten housings, tighten bolts,and the like.

In another example, a machine-learned model may be used to providedecision support relating to vibration measurement collection by aworker. In this example, the cognitive processes system 15510 mayreceive vibration measurement history data from the industrial facilityand may generate a feature vector based on the vibration measurementhistory data. The cognitive processes system 15510 may input the featurevector into a machine-learned model trained specifically for the engine(e.g., using a combination of simulation data and real-world vibrationmeasurement history data) to provide decision support in selectingvibration measurement locations.

In yet another example, a machine-learned model may be used to identifyvibration signatures associated with machine and/or machine componentproblems. In this example, the cognitive processes system 15510 mayreceive vibration measurement history data from the industrial facilityand may generate a feature vector based on the vibration measurementhistory data. The cognitive processes system 15510 may input the featurevector into a machine-learned model trained specifically for the engine(e.g., using a combination of simulation data and real-world vibrationmeasurement history data) to identify vibration signatures associatedwith a machine and/or machine component. The foregoing examples arenon-limiting examples and the cognitive processes system 15510 may beused for any other suitable AI/machine-learning related tasks that areperformed with respect to industrial facilities.

In embodiments, the environment control system 15512 controls one ormore aspects of industrial facilities. In some of these embodiments, theenvironment control system 15512 may control one or more devices withinan industrial environment. For example, the environment control system15512 may control one or more machines within an environment, robotswithin an environment, an HVAC system of the environment, an alarmsystem of the environment, an assembly line in an environment, or thelike. In embodiments, the environment control system 15512 may leveragethe digital twin simulation system 15506, the digital twin dynamic modelsystem 15508, and/or the cognitive processes system 15510 to determineone or more control instructions. In embodiments, the environmentcontrol system 15512 may implement a rules-based and/or amachine-learning approach to determine the control instructions. Inresponse to determining a control instruction, the environment controlsystem 15512 may output the control instruction to the intended devicewithin a specific environment via the digital twin I/O system 15504.

FIG. 156 illustrates an example digital twin management system 15502according to some embodiments of the present disclosure. In embodiments,the digital twin management system 15502 may include, but is not limitedto, a digital twin creation module 15564, a digital twin update module15566, and a digital twin visualization module 15568.

In embodiments, the digital twin creation module 15564 may create a setof new digital twins of a set of environments using input from users,imported data (e.g., blueprints, specifications, and the like), imagescans of the environment, 3D data from a LIDAR device and/or SLAMsensor, and other suitable data sources. For example, a user (e.g., auser affiliated with an organization/customer account) may, via a clientapplication 15570, provide input to create a new digital twin of anenvironment. In doing so, the user may upload 2D or 3D image scans ofthe environment and/or a blueprint of the environment. The user may alsoupload 3D data, such as taken by a camera, a LIDAR device, an IRscanner, a set of SLAM sensors, a radar device, an EMF scanner, or thelike. In response to the provided data, the digital twin creation module15564 may create a 3D representation of the environment, which mayinclude any objects that were captured in the image data/detected in the3D data. In embodiments, the cognitive processes system 15572 mayanalyze input data (e.g., blueprints, image scans, 3D data) to classifyrooms, pathways, equipment, and the like to assist in the generation ofthe 3D representation. In some embodiments, the digital twin creationmodule 15564 may map the digital twin to a 3D coordinate space (e.g., aCartesian space having x, y, and z axes).

In some embodiments, the digital twin creation module 15564 may outputthe 3D representation of the environment to a graphical user interface(GUI). In some of these embodiments, a user may identify certain areasand/or objects and may provide input relating to the identified areasand/or objects. For example, a user may label specific rooms, equipment,machines, and the like. Additionally or alternatively, the user mayprovide data relating to the identified objects and/or areas. Forexample, in identifying a piece of equipment, the user may provide amake/model number of the equipment. In some embodiments, the digitaltwin creation module 15564 may obtain information from a manufacturer ofa device, a piece of equipment, or machinery. This information mayinclude one or more properties and/or behaviors of the device,equipment, or machinery. In some embodiments, the user may, via the GUI,identify locations of sensors throughout the environment. For eachsensor, the user may provide a type of sensor and related data (e.g.,make, model, IP address, and the like). The digital twin creation module15564 may record the locations (e.g., the x, y, z coordinates of thesensors) in the digital twin of the environment. In embodiments, thedigital twin system 15500 may employ one or more systems that automatethe population of digital twins. For example, the digital twin system15500 may employ a machine vision-based classifier that classifies makesand models of devices, equipment, or sensors. Additionally oralternatively, the digital twin system 15500 may iteratively pingdifferent types of known sensors to identify the presence of specifictypes of sensors that are in an environment. Each time a sensor respondsto a ping, the digital twin system 15500 may extrapolate the make andmodel of the sensor.

In some embodiments, the manufacturer may provide or make availabledigital twins of their products (e.g., sensors, devices, machinery,equipment, raw materials, and the like). In these embodiments, thedigital twin creation module 15564 may import the digital twins of oneor more products that are identified in the environment and may embedthose digital twins in the digital twin of the environment. Inembodiments, embedding a digital twin within another digital twin mayinclude creating a relationship between the embedded digital twin withthe other digital twin. In these embodiments, the manufacturer of thedigital twin may define the behaviors and/or properties of therespective products. For example, a digital twin of a machine may definethe manner by which the machine operates, the inputs/outputs of themachine, and the like. In this way, the digital twin of the machine mayreflect the operation of the machine given a set of inputs.

In embodiments, a user may define one or more processes that occur in anenvironment. In these embodiments, the user may define the steps in theprocess, the machines/devices that perform each step in the process, theinputs to the process, and the outputs of the process.

In embodiments, the digital twin creation module 15564 may create agraph database that defines the relationships between a set of digitaltwins. In these embodiments, the digital twin creation module 15564 maycreate nodes for the environment, systems and subsystems of theenvironment, devices in the environment, sensors in the environment,workers that work in the environment, processes that are performed inthe environment, and the like. In embodiments, the digital twin creationmodule 15564 may write the graph database representing a set of digitaltwins to the digital twin datastore 15516.

In embodiments, the digital twin creation module 15564 may, for eachnode, include any data relating to the entity in the node representingthe entity. For example, in defining a node representing an environment,the digital twin creation module 15564 may include the dimensions,boundaries, layout, pathways, and other relevant spatial data in thenode. Furthermore, the digital twin creation module 15564 may define acoordinate space with respect to the environment. In the case that thedigital twin may be rendered, the digital twin creation module 15564 mayinclude a reference in the node to any shapes, meshes, splines,surfaces, and the like that may be used to render the environment. Inrepresenting a system, subsystem, device, or sensor, the digital twincreation module 15564 may create a node for the respective entity andmay include any relevant data. For example, the digital twin creationmodule 15564 may create a node representing a machine in theenvironment. In this example, the digital twin creation module 15564 mayinclude the dimensions, behaviors, properties, location, and/or anyother suitable data relating to the machine in the node representing themachine. The digital twin creation module 15564 may connect nodes ofrelated entities with an edge, thereby creating a relationship betweenthe entities. In doing so, the created relationship between the entitiesmay define the type of relationship characterized by the edge. Inrepresenting a process, the digital twin creation module 15564 maycreate a node for the entire process or may create a node for each stepin the process. In some of these embodiments, the digital twin creationmodule 15564 may relate the process nodes to the nodes that representthe machinery/devices that perform the steps in the process. Inembodiments, where an edge connects the process step nodes to themachinery/device that performs the process step, the edge or one of thenodes may contain information that indicates the input to the step, theoutput of the step, the amount of time the step takes, the nature ofprocessing of inputs to produce outputs, a set of states or modes theprocess can undergo, and the like.

In embodiments, the digital twin update module 15566 updates sets ofdigital twins based on a current status of one or more industrialentities. In some embodiments, the digital twin update module 15566receives sensor data from a sensor system 15530 of an industrialenvironment and updates the status of the digital twin of the industrialenvironment and/or digital twins of any affected systems, subsystems,devices, workers, processes, or the like. As discussed, the digital twinI/O system 15504 may receive the sensor data in one or more sensorpackets. The digital twin I/O system 15504 may provide the sensor datato the digital twin update module 15566 and may identify the environmentfrom which the sensor packets were received and the sensor that providedthe sensor packet. In response to the sensor data, the digital twinupdate module 15566 may update a state of one or more digital twinsbased on the sensor data. In some of these embodiments, the digital twinupdate module 15566 may update a record (e.g., a node in a graphdatabase) corresponding to the sensor that provided the sensor data toreflect the current sensor data. In some scenarios, the digital twinupdate module 15566 may identify certain areas within the environmentthat are monitored by the sensor and may update a record (e.g., a nodein a graph database) to reflect the current sensor data. For example,the digital twin update module 15566 may receive sensor data reflectingdifferent vibrational characteristics of a machine and/or machinecomponents. In this example, the digital twin update module 15566 mayupdate the records representing the vibration sensors that provided thevibration sensor data and/or the records representing the machine and/orthe machine components to reflect the vibration sensor data. In anotherexample, in some scenarios, workers in an industrial environment (e.g.,manufacturing facility, industrial storage facility, a mine, a drillingoperation, or the like) may be required to wear wearable devices (e.g.,smart watches, smart helmets, smart shoes, or the like). In theseembodiments, the wearable devices may collect sensor data relating tothe worker (e.g., location, movement, heartrate, respiration rate, bodytemperature, or the like) and/or the environment surrounding the workerand may communicate the collected sensor data to the digital twin system15500 (e.g., via the real-time sensor API 15514) either directly or viaan aggregation device of the sensor system. In response to receiving thesensor data from the wearable device of a worker, the digital twinupdate module 15566 may update a digital twin of a worker to reflect,for example, a location of the worker, a trajectory of the worker, ahealth status of the worker, or the like. In some of these embodiments,the digital twin update module 15566 may update a node representing aworker and/or an edge that connects the node representing theenvironment with the collected sensor data to reflect the current statusof the worker.

In some embodiments, the digital twin update module 15566 may providethe sensor data from one or more sensors to the digital twin dynamicmodel system 15508, which may model a behavior of the environment and/orone or more industrial entities to extrapolate additional state data.

In embodiments, the digital twin visualization module 15568 receivesrequests to view a visual digital twin or a portion thereof. Inembodiments, the request may indicate the digital twin to be viewed(e.g., an environment identifier). In response, the digital twinvisualization module 15568 may determine the requested digital twin andany other digital twins implicated by the request. For example, inrequesting to view a digital twin of an environment, the digital twinvisualization module 15568 may further identify the digital twins of anyindustrial entities within the environment. In embodiments, the digitaltwin visualization module 15568 may identify the spatial relationshipsbetween the industrial entities and the environment based on, forexample, the relationships defined in a graph database. In theseembodiments, the digital twin visualization module 15568 can determinethe relative location of embedded digital twins within the containingdigital twin, relative locations of adjoining digital twins, and/or thetransience of the relationship (e.g., is an object fixed to a point ordoes the object move). The digital twin visualization module 15568 mayrender the requested digital twins and any other implicated digital twinbased on the identified relationships. In some embodiments, the digitaltwin visualization module 15568 may, for each digital twin, determinethe surfaces of the digital twin. In some embodiments, the surfaces of adigital may be defined or referenced in a record corresponding to thedigital twin, which may be provided by a user, determined from importedimages, or defined by a manufacturer of an industrial entity. In thescenario that an object can take different poses or shapes (e.g., anindustrial robot), the digital twin visualization module 15568 maydetermine a pose or shape of the object for the digital twin. Thedigital twin visualization module 15568 may embed the digital twins intothe requested digital twin and may output the requested digital twin toa client application.

In some of these embodiments, the request to view a digital twin mayfurther indicate the type of view. As discussed, in some embodiments,digital twins may be depicted in a number of different view types. Forexample, an environment or device may be viewed in a “real-world” viewthat depicts the environment or device as they typically appear, in a“heat” view that depicts the environment or device in a manner that isindicative of a temperature of the environment or device, in a“vibration” view that depicts the machines and/or machine components inan industrial environment in a manner that is indicative of vibrationalcharacteristics of the machines and/or machine components, in a“filtered” view that only displays certain types of objects within anenvironment or components of a device (such as objects that requireattention resulting from, for example, recognition of a fault condition,an alert, an updated report, or other factors), an augmented view thatoverlays data on the digital twin, and/or any other suitable view types.In embodiments, digital twins may be depicted in a number of differentrole-based view types. For example, a manufacturing facility device maybe viewed in an “operator” view that depicts the facility in a mannerthat is suitable for a facility operator, a “C-Suite” view that depictsthe facility in a manner that is suitable for executive-level managers,a “marketing” view that depicts the facility in a manner that issuitable for workers in sales and/or marketing roles, a “board” viewthat depicts the facility in a manner that is suitable for members of acorporate board, a “regulatory” view that depicts the facility in amanner that is suitable for regulatory managers, and a “human resources”view that depicts the facility in a manner that is suitable for humanresources personnel. In response to a request that indicates a viewtype, the digital twin visualization module 15568 may retrieve the datafor each digital twin that corresponds to the view type. For example, ifa user has requested a vibration view of a factory floor, the digitaltwin visualization module 15568 may retrieve vibration data for thefactory floor (which may include vibration measurements taken fromdifferent machines and/or machine components and/or vibrationmeasurements that were extrapolated by the digital twin dynamic modelsystem 15508 and/or simulated vibration data from digital twinsimulation system 15506) as well as available vibration data for anyindustrial entities appearing on the factory floor. In this example, thedigital twin visualization module 15568 may determine colorscorresponding to each machine component on a factory floor thatrepresent a vibration fault level state (e.g., red for alarm, orange forcritical, yellow for suboptimal, and green for normal operation). Thedigital twin visualization module 15568 may then render the digitaltwins of the machine components within the environment based on thedetermined colors. Additionally or alternatively, the digital twinvisualization module 15568 may render the digital twins of the machinecomponents within the environment with indicators having the determinedcolors. For instance, if the vibration fault level state of an inboundbearing of a motor is suboptimal and the outbound bearing of the motoris critical, the digital twin visualization module 15568 may render thedigital twin of the inbound bearing having an indicator in a shade ofyellow (e.g., suboptimal) and the outbound bearing having an indicatorin a shade of orange (e.g., critical). It is noted that in someembodiments, the digital twin system 15500 may include an analyticssystem (not shown) that determine the manner by which the digital twinvisualization system 15568 presents information to a human user. Forexample, the analytics system may track outcomes relating to humaninteractions with real-world environments or objects in response toinformation presented in a visual digital twin. In some embodiments, theanalytics system may apply cognitive models to determine the mosteffective manner to display visualized information (e.g., what colors touse to denote an alarm condition, what kind of movements or animationsbring attention to an alarm condition, or the like) or audio information(what sounds to use to denote an alarm condition) based on the outcomedata. In some embodiments, the analytics system may apply cognitivemodels to determine the most suitable manner to display visualizedinformation based on the role of the user. In embodiments, thevisualization may include display of information related to thevisualized digital twins, including graphical information, graphicalinformation depicting vibration characteristics, graphical informationdepicting harmonic peaks, graphical information depicting peaks,vibration severity units data, vibration fault level state data,recommendations from cognitive intelligence system 15510, predictionsfrom cognitive intelligence system 15510, probability of failure data,maintenance history data, time to failure data, cost of downtime data,probability of downtime data, cost of repair data, cost of machinereplace data, probability of shutdown data, manufacturing KPIs, and thelike.

In another example, a user may request a filtered view of a digital twinof a process, whereby the digital twin of the process only showscomponents (e.g., machine or equipment) that are involved in theprocess. In this example, the digital twin visualization module 15568may retrieve a digital twin of the process, as well as any relateddigital twins (e.g., a digital twin of the environment and digital twinsof any machinery or devices that impact the process). The digital twinvisualization module 15568 may then render each of the digital twins(e.g., the environment and the relevant industrial entities) and thenmay perform the process on the rendered digital twins. It is noted thatas a process may be performed over a period of time and may includemoving items and/or parts, the digital twin visualization module 15568may generate a series of sequential frames that demonstrate the process.In this scenario, the movements of the machines and/or devicesimplicated by the process may be determined according to the behaviorsdefined in the respective digital twins of the machines and/or devices.

As discussed, the digital twin visualization module 15568 may output therequested digital twin to a client application 15570. In someembodiments, the client application 15570 is a virtual realityapplication, whereby the requested digital twin is displayed on avirtual reality headset. In some embodiments, the client application15570 is an augmented reality application, whereby the requested digitaltwin is depicted in an AR-enabled device. In these embodiments, therequested digital twin may be filtered such that visual elements and/ortext are overlaid on the display of the AR-enabled device.

It is noted that while a graph database is discussed, the digital twinsystem 15500 may employ other suitable data structures to storeinformation relating to a set of digital twins. In these embodiments,the data structures, and any related storage system, may be implementedsuch that the data structures provide for some degree of feedback loopsand/or recursion when representing iteration of flows.

FIG. 131 illustrates an example of a digital twin I/O system 15504 thatinterfaces with the environment 15520, the digital twin system 15500,and/or components thereof to provide bi-directional transfer of databetween coupled components according to some embodiments of the presentdisclosure.

In embodiments, the transferred data includes signals (e.g., requestsignals, command signals, response signals, etc.) between connectedcomponents, which may include software components, hardware components,physical devices, virtualized devices, simulated devices, combinationsthereof, and the like. The signals may define material properties (e.g.,physical quantities of temperature, pressure, humidity, density,viscosity, etc.), measured values (e.g., contemporaneous or storedvalues acquired by the device or system), device properties (e.g.,device ID or properties of the device's design specifications,materials, measurement capabilities, dimensions, absolute position,relative position, combinations thereof, and the like), set points(e.g., targets for material properties, device properties, systemproperties, combinations thereof, and the like), and/or critical points(e.g., threshold values such as minimum or maximum values for materialproperties, device properties, system properties, etc.). The signals maybe received from systems or devices that acquire (e.g., directly measureor generate) or otherwise obtain (e.g., receive, calculate, look-up,filter, etc.) the data, and may be communicated to or from the digitaltwin I/O system 15504 at predetermined times or in response to a request(e.g., polling) from the digital twin I/O system 15504. Thecommunications may occur through direct or indirect connections (e.g.,via intermediate modules within a circuit and/or intermediate devicesbetween the connected components). The values may correspond toreal-world elements 131302 r (e.g., an input or output for a tangiblevibration sensor) or virtual elements 131302 v (e.g., an input or outputfor a digital twin 131302 d and/or a simulated element 131302 s thatprovide vibration data).

In embodiments, the real-world elements 131302 r may be elements withinthe industrial environment 15520. The real-world elements 131302 r mayinclude, for example, non-networked objects 15522, the devices 15524(smart or non-smart), sensors 15526, and humans 15528. The real-worldelements 131302 r may be process or non-process equipment within theindustrial environments 15520. For example, process equipment mayinclude motors, pumps, mills, fans, painters, welders, smelters, etc.,and non-process equipment may include personal protective equipment,safety equipment, emergency stations or devices (e.g., safety showers,eyewash stations, fire extinguishers, sprinkler systems, etc.),warehouse features (e.g., walls, floor layout, etc.), obstacles (e.g.,persons or other items within the environment 15520, etc.), etc.

In embodiments, the virtual elements 131302 v may be digitalrepresentations of or that correspond to contemporaneously existingreal-world elements 131302 r. Additionally or alternatively, the virtualelements 131302 v may be digital representations of or that correspondto real-world elements 131302 r that may be available for later additionand implementation into the environment 15520. The virtual elements mayinclude, for example, simulated elements 131302 s and/or digital twins131302 d. In embodiments, the simulated elements 131302 s may be digitalrepresentations of real-world elements 131302 s that are not presentwithin the industrial environment 15520. The simulated elements 131302 smay mimic desired physical properties which may be later integratedwithin the environment 15520 as real-world elements 131302 r (e.g., a“black box” that mimics the dimensions of a real-world elements 131302r). The simulated elements 131302 s may include digital twins ofexisting objects (e.g., a single simulated element 131302 s may includeone or more digital twins 131302 d for existing sensors). Informationrelated to the simulated elements 131302 s may be obtained, for example,by evaluating behavior of corresponding real-world elements 131302 rusing mathematical models or algorithms, from libraries that defineinformation and behavior of the simulated elements 131302 s (e.g.,physics libraries, chemistry libraries, or the like).

In embodiments, the digital twin 131302 d may be a digitalrepresentation of one or more real-world elements 131302 r. The digitaltwins 131302 d are configured to mimic, copy, and/or model behaviors andresponses of the real-world elements 131302 r in response to inputs,outputs, and/or conditions of the surrounding or ambient environment.Data related to physical properties and responses of the real-worldelements 131302 r may be obtained, for example, via user input, sensorinput, and/or physical modeling (e.g., thermodynamic models,electrodynamic models, mechanodynamic models, etc.). Information for thedigital twin 131302 d may correspond to and be obtained from the one ormore real-world elements 131302 r corresponding to the digital twin131302 d. For example, in some embodiments, the digital twin 131302 dmay correspond to one real-world element 131302 r that is a fixeddigital vibration sensor 15536 on a machine component, and vibrationdata for the digital twin 131302 d may be obtained by polling orfetching vibration data measured by the fixed digital vibration sensoron the machine component. In a further example, the digital twin 131302d may correspond to a plurality of real-world elements 131302 r suchthat each of the elements can be a fixed digital vibration sensor on amachine component, and vibration data for the digital twin 131302 d maybe obtained by polling or fetching vibration data measured by each ofthe fixed digital vibration sensors on the plurality of real-worldelements 131302 r. Additionally or alternatively, vibration data of afirst digital twin 131302 d may be obtained by fetching vibration dataof a second digital twin 157302 d that is embedded within the firstdigital twin 157302 d, and vibration data for the first digital twin157302 d may include or be derived from vibration data for the seconddigital twin 157302 d. For example, the first digital twin may be adigital twin 157302 d of an environment 15520 (alternatively referred toas an “environmental digital twin”) and the second digital twin 157302 dmay be a digital twin 157302 d corresponding to a vibration sensordisposed within the environment 15520 such that the vibration data forthe first digital twin 157302 d is obtained from or calculated based ondata including the vibration data for the second digital twin 157302 d.

In embodiments, the digital twin system 15500 monitors properties of thereal-world elements 157302 r using the sensors 15526 within a respectiveenvironment 15520 that is or may be represented by a digital twin 157302d and/or outputs of models for one or more simulated elements 157302 s.In embodiments, the digital twin system 15500 may minimize networkcongestion while maintaining effective monitoring of processes byextending polling intervals and/or minimizing data transfer for sensorscorresponding that correspond to affected real-world elements 157302 rand performing simulations (e.g., via the digital-twin simulation system15506) during the extended interval using data that was obtained fromother sources (e.g., sensors that are physically proximate to or have aneffect on the affected real-world elements 157302 r). Additionally oralternatively, error checking may be performed by comparing thecollected sensor data with data obtained from the digital-twinsimulation system 15506. For example, consistent deviations orfluctuations between sensor data obtained from the real-world element157302 r and the simulated element 157302 s may indicate malfunction ofthe respective sensor or another fault condition.

In embodiments, the digital twin system 15500 may optimize features ofthe environment through use of one or more simulated elements 157302 s.For example, the digital twin system 15500 may evaluate effects of thesimulated elements 157302 s within a digital twin of an environment toquickly and efficiently determine costs and/or benefits flowing frominclusion, exclusion, or substitution of real-world elements 157302 rwithin the environment 15520. The costs and benefits may include, forexample, increased machinery costs (e.g., capital investment andmaintenance), increased efficiency (e.g., process optimization to reducewaste or increase throughput), decreased or altered footprint within theenvironment 15520, extension or optimization of useful lifespans,minimization of component faults, minimization of component downtime,etc.

In embodiments, the digital twin I/O system 15504 may include one ormore software modules that are executed by one or more controllers ofone or more devices (e.g., server devices, user devices, and/ordistributed devices) to affect the described functions. The digital twinI/O system 15504 may include, for example, an input module 157304, anoutput module 157306, and an adapter module 157308.

In embodiments, the input module 157304 may obtain or import data fromdata sources in communication with the digital twin I/O system 15504,such as the sensor system 15530 and the digital twin simulation system15506. The data may be immediately used by or stored within the digitaltwin system 15500. The imported data may be ingested from data streams,data batches, in response to a triggering event, combinations thereof,and the like. The input module 157304 may receive data in a format thatis suitable to transfer, read, and/or write information within thedigital twin system 15500.

In embodiments, the output module 157306 may output or export data toother system components (e.g., the digital twin datastore 15516, thedigital twin simulation system 15506, the cognitive intelligence system15510, etc.), devices 15524, and/or the client application 15570. Thedata may be output in data streams, data batches, in response to atriggering event (e.g., a request), combinations thereof, and the like.The output module 157306 may output data in a format that is suitable tobe used or stored by the target element (e.g., one protocol for outputto the client application and another protocol for the digital twindatastore 15516).

In embodiments, the adapter module 157308 may process and/or convertdata between the input module 157304 and the output module 157306. Inembodiments, the adapter module 157308 may convert and/or route dataautomatically (e.g., based on data type) or in response to a receivedrequest (e.g., in response to information within the data).

In embodiments, the digital twin system 15500 may represent a set ofindustrial workpiece elements in a digital twin, and the digital twinsimulation system 15506 simulates a set of physical interactions of aworker with the workpiece elements.

In embodiments, the digital twin simulation system 15506 may determineprocess outcomes for the simulated physical interactions accounting forsimulated human factors. For example, variations in workpiece throughputmay be modeled by the digital twin system 15500 including, for example,worker response times to events, worker fatigue, discontinuity withinworker actions (e.g., natural variations in human-movement speed,differing positioning times, etc.), effects of discontinuities ondownstream processes, and the like. In embodiments, individualizedworker interactions may be modeled using historical data that iscollected, acquired, and/or stored by the digital twin system 15500. Thesimulation may begin based on estimated amounts (e.g., worker age,industry averages, workplace expectations, etc.). The simulation mayalso individualize data for each worker (e.g., comparing estimatedamounts to collected worker-specific outcomes).

In embodiments, information relating to workers (e.g., fatigue rates,efficiency rates, and the like) may be determined by analyzingperformance of specific workers over time and modeling said performance.

In embodiments, the digital twin system 15500 includes a plurality ofproximity sensors within the sensor system 15530. The proximity sensorsare or may be configured to detect elements of the environment 15520that are within a predetermined area. For example, proximity sensors mayinclude electromagnetic sensors, light sensors, and/or acoustic sensors.

The electromagnetic sensors are or may be configured to sense objects orinteractions via one or more electromagnetic fields (e.g., emittedelectromagnetic radiation or received electromagnetic radiation). Inembodiments, the electromagnetic sensors include inductive sensors(e.g., radio-frequency identification sensors), capacitive sensors(e.g., contact, and contactless capacitive sensors), combinationsthereof, and the like.

The light sensors are or may be configured to sense objects orinteractions via electromagnetic radiation in, for example, thefar-infrared, near-infrared, optical, and/or ultraviolet spectra. Inembodiments, the light sensors may include image sensors (e.g.,charge-coupled devices and CMOS active-pixel sensors), photoelectricsensors (e.g., through-beam sensors, retroreflective sensors, anddiffuse sensors), combinations thereof, and the like. Further, the lightsensors may be implemented as part of a system or subsystem, such as alight detection and ranging (“LIDAR”) sensor.

The acoustic sensors are or may be configured to sense objects orinteractions via sound waves that are emitted and/or received by theacoustic sensors. In embodiments, the acoustic sensors may includeinfrasonic, sonic, and/or ultrasonic sensors. Further, the acousticsensors may be grouped as part of a system or subsystem, such as a soundnavigation and ranging (“SONAR”) sensor.

In embodiments, the digital twin system 15500 stores and collects datafrom a set of proximity sensors within the environment 15520 or portionsthereof. The collected data may be stored, for example, in the digitaltwin datastore 15516 for use by components the digital twin system 15500and/or visualization by a user. Such use and/or visualization may occurcontemporaneously with or after collection of the data (e.g., duringlater analysis and/or optimization of processes).

In embodiments, data collection may occur in response to a triggeringcondition. These triggering conditions may include, for example,expiration of a static or a dynamic predetermined interval, obtaining avalue short of or in excess of a static or dynamic value, receiving anautomatically generated request or instruction from the digital twinsystem 15500 or components thereof, interaction of an element with therespective sensor or sensors (e.g., in response to a worker or machinebreaking a beam or coming within a predetermined distance from theproximity sensor), interaction of a user with a digital twin (e.g.,selection of an environmental digital twin, a sensor array digital twin,or a sensor digital twin), combinations thereof, and the like.

In some embodiments, the digital twin system 15500 collects and/orstores RFID data in response to interaction of a worker with areal-world element 157302 r. For example, in response to a workerinteraction with a real-world environment, the digital twin will collectand/or store RFID data from RFID sensors within or associated with thecorresponding environment 15520. Additionally or alternatively, workerinteraction with a sensor-array digital twin will collect and/or storeRFID data from RFID sensors within or associated with the correspondingsensor array. Similarly, worker interaction with a sensor digital twinwill collect and/or store RFID data from the corresponding sensor. TheRFID data may include suitable data attainable by RFID sensors such asproximate RFID tags, RFID tag position, authorized RFID tags,unauthorized RFID tags, unrecognized RFID tags, RFID type (e.g., activeor passive), error codes, combinations thereof, and the like.

In embodiments, the digital twin system 15500 may further embed outputsfrom one or more devices within a corresponding digital twin. Inembodiments, the digital twin system 15500 embeds output from a set ofindividual-associated devices into an industrial digital twin. Forexample, the digital twin I/O system 15504 may receive informationoutput from one or more wearable devices 15554 or mobile devices (notshown) associated with an individual within an industrial environment.The wearable devices may include image capture devices (e.g., bodycameras or augmented-reality headwear), navigation devices (e.g., GPSdevices, inertial guidance systems), motion trackers, acoustic capturedevices (e.g., microphones), radiation detectors, combinations thereof,and the like.

In embodiments, upon receiving the output information, the digital twinI/O system 15504 routes the information to the digital twin creationmodule 15564 to check and/or update the environment digital twin and/orassociated digital twins within the environment (e.g., a digital twin ofa worker, machine, or robot position at a given time). Further, thedigital twin system 15500 may use the embedded output to determinecharacteristics of the environment 15520.

In embodiments, the digital twin system 15500 embeds output from a LIDARpoint cloud system into an industrial digital twin. For example, thedigital twin I/O system 15504 may receive information output from one ormore Lidar devices 15538 within an industrial environment. The Lidardevices 15538 are configured to provide a plurality of points havingassociated position data (e.g., coordinates in absolute or relative x,y, and z values). Each of the plurality of points may include furtherLIDAR attributes, such as intensity, return number, total returns, lasercolor data, return color data, scan angle, scan direction, etc. TheLidar devices 15538 may provide a point cloud that includes theplurality of points to the digital twin system 15500 via, for example,the digital twin I/O system 15504. Additionally or alternatively, thedigital twin system 15500 may receive a stream of points and assemblethe stream into a point cloud, or may receive a point cloud and assemblethe received point cloud with existing point cloud data, map data, orthree dimensional (3D)-model data.

In embodiments, upon receiving the output information, the digital twinI/O system 15504 routes the point cloud information to the digital twincreation module 15564 to check and/or update the environment digitaltwin and/or associated digital twins within the environment (e.g., adigital twin of a worker, machine, or robot position at a given time).In some embodiments, the digital twin system 15500 is further configuredto determine closed-shape objects within the received LIDAR data. Forexample, the digital twin system 15500 may group a plurality of pointswithin the point cloud as an object and, if necessary, estimateobstructed faces of objects (e.g., a face of the object contacting oradjacent a floor or a face of the object contacting or adjacent anotherobject such as another piece of equipment). The system may use suchclosed-shape objects to narrow search space for digital twins andthereby increase efficiency of matching algorithms (e.g., ashape-matching algorithm).

In embodiments, the digital twin system 15500 embeds output from asimultaneous location and mapping (“SLAM”) system in an environmentaldigital twin. For example, the digital twin I/O system 15504 may receiveinformation output from the SLAM system, such as Slam sensor 15562, andembed the received information within an environment digital twincorresponding to the location determined by the SLAM system. Inembodiments, upon receiving the output information from the SLAM system,the digital twin I/O system 15504 routes the information to the digitaltwin creation module 15564 to check and/or update the environmentdigital twin and/or associated digital twins within the environment(e.g., a digital twin of a workpiece, furniture, movable object, orautonomous object). Such updating provides digital twins ofnon-connected elements (e.g., furnishings or persons) automatically andwithout need of user interaction with the digital twin system 15500.

In embodiments, the digital twin system 15500 can leverage known digitaltwins to reduce computational requirements for the Slam sensor 15562 byusing suboptimal map-building algorithms. For example, the suboptimalmap-building algorithms may allow for a higher uncertainty toleranceusing simple bounded-region representations and identifying possibledigital twins. Additionally or alternatively, the digital twin system15500 may use a bounded-region representation to limit the number ofdigital twins, analyze the group of potential twins for distinguishingfeatures, then perform higher precision analysis for the distinguishingfeatures to identify and/or eliminate categories of, groups of, orindividual digital twins and, in the event that no matching digital twinis found, perform a precision scan of only the remaining areas to bescanned.

In embodiments, the digital twin system 15500 may further reduce computerequired to build a location map by leveraging data captured from othersensors within the environment (e.g., captured images or video, radioimages, etc.) to perform an initial map-building process (e.g., a simplebounded-region map or other suitable photogrammetry methods), associatedigital twins of known environmental objects with features of the simplebounded-region map to refine the simple bounded-region map, and performmore precise scans of the remaining simple bounded regions to furtherrefine the map. In some embodiments, the digital twin system 15500 maydetect objects within received mapping information and, for eachdetected object, determine whether the detected object corresponds to anexisting digital twin of a real-world-element. In response todetermining that the detected object does not correspond to an existingreal-world-element digital twin, the digital twin system 15500 may use,for example, the digital twin creation module 15564 to generate a newdigital twin corresponding to the detected object (e.g., adetected-object digital twin) and add the detected-object digital twinto the real-world-element digital twins within the digital twindatastore. Additionally or alternatively, in response to determiningthat the detected object corresponds to an existing real-world-elementdigital twin, the digital twin system 15500 may update thereal-world-element digital twin to include new information detected bythe simultaneous location and mapping sensor, if any.

In embodiments, the digital twin system 15500 represents locations ofautonomously or remotely moveable elements and attributes thereof withinan industrial digital twin. Such movable elements may include, forexample, workers, persons, vehicles, autonomous vehicles, robots, etc.The locations of the moveable elements may be updated in response to atriggering condition. Such triggering conditions may include, forexample, expiration of a static or a dynamic predetermined interval,receiving an automatically generated request or instruction from thedigital twin system 15500 or components thereof, interaction of anelement with a respective sensor or sensors (e.g., in response to aworker or machine breaking a beam or coming within a predetermineddistance from a proximity sensor), interaction of a user with a digitaltwin (e.g., selection of an environmental digital twin, a sensor arraydigital twin, or a sensor digital twin), combinations thereof, and thelike.

In embodiments, the time intervals may be based on probability of therespective movable element having moved within a time period. Forexample, the time interval for updating a worker location may berelatively shorter for workers expected to move frequently (e.g., aworker tasked with lifting and carrying objects within and through theenvironment 15520) and relatively longer for workers expected to moveinfrequently (e.g., a worker tasked with monitoring a process stream).Additionally or alternatively, the time interval may be dynamicallyadjusted based on applicable conditions, such as increasing the timeinterval when no movable elements are detected, decreasing the timeinterval as or when the number of moveable elements within anenvironment increases (e.g., increasing number of workers and workerinteractions), increasing the time interval during periods of reducedenvironmental activity (e.g., breaks such as lunch), decreasing the timeinterval during periods of abnormal environmental activity (e.g., tours,inspections, or maintenance), decreasing the time interval whenunexpected or uncharacteristic movement is detected (e.g., frequentmovement by a typically sedentary element or coordinated movement, forexample, of workers approaching an exit or moving cooperatively to carrya large object), combinations thereof, and the like. Further, the timeinterval may also include additional, semi-random acquisitions. Forexample, occasional mid-interval locations may be acquired by thedigital twin system 15500 to reinforce or evaluate the efficacy of theparticular time interval.

In embodiments, the digital twin system 15500 may analyze data receivedfrom the digital twin I/O system 15504 to refine, remove, or addconditions. For example, the digital twin system 15500 may optimize datacollection times for movable elements that are updated more frequentlythan needed (e.g., multiple consecutive received positions beingidentical or within a predetermined margin of error).

In embodiments, the digital twin system 15500 may receive, identify,and/or store a set of states 15540 a-n related to the environment 15520.The states 15540 a-n may be, for example, data structures that include aplurality of attributes 158404 a-n and a set of identifying criteria158406 a-n to uniquely identify each respective state 15540 a-n. Inembodiments, the states 15540 a-n may correspond to states where it isdesirable for the digital twin system 15500 to set or alter conditionsof real-world elements 157302 r and/or the environment 15520 (e.g.,increase/decrease monitoring intervals, alter operating conditions,etc.).

In embodiments, the set of states 15540 a-n may further include, forexample, minimum monitored attributes for each state 15540 a-n, the setof identifying criteria 158406 a-n for each state 15540 a-n, and/oractions available to be taken or recommended to be taken in response toeach state 15540 a-n. Such information may be stored by, for example,the digital twin datastore 15516 or another datastore. The states 15540a-n or portions thereof may be provided to, determined by, or altered bythe digital twin system 15500. Further, the set of states 15540 a-n mayinclude data from disparate sources. For example, details to identifyand/or respond to occurrence of a first state may be provided to thedigital twin system 15500 via user input, details to identify and/orrespond to occurrence of a second state may be provided to the digitaltwin system 15500 via an external system, details to identify and/orrespond to occurrence of a third state may be determined by the digitaltwin system 15500 (e.g., via simulations or analysis of process data),and details to identify and/or respond to occurrence of a fourth statemay be stored by the digital twin system 15500 and altered as desired(e.g., in response to simulated occurrence of the state or analysis ofdata collected during an occurrence of and response to the state).

In embodiments, the plurality of attributes 158404 a-n includes at leastthe attributes 158404 a-n needed to identify the respective state 15540a-n. The plurality of attributes 158404 a-n may further includeadditional attributes that are or may be monitored in determining therespective state 15540 a-n, but are not needed to identify therespective state 15540 a-n. For example, the plurality of attributes158404 a-n for a first state may include relevant information such asrotational speed, fuel level, energy input, linear speed, acceleration,temperature, strain, torque, volume, weight, etc.

The set of identifying criteria 158406 a-n may include information foreach of the set of attributes 158404 a-n to uniquely identify therespective state. The identifying criteria 158406 a-n may include, forexample, rules, thresholds, limits, ranges, logical values, conditions,comparisons, combinations thereof, and the like.

The change in operating conditions or monitoring may be any suitablechange. For example, after identifying occurrence of a respective state158406 a-n, the digital twin system 15500 may increase or decreasemonitoring intervals for a device (e.g., decreasing monitoring intervalsin response to a measured parameter differing from nominal operation)without altering operation of the device. Additionally or alternatively,the digital twin system 15500 may alter operation of the device (e.g.,reduce speed or power input) without altering monitoring of the device.In further embodiments, the digital twin system 15500 may alteroperation of the device (e.g., reduce speed or power input) and altermonitoring intervals for the device (e.g., decreasing monitoringintervals).

FIG. 151 illustrates an example set of identified states 15540 a-nrelated to industrial environments that the digital twin system 15500may identify and/or store for access by intelligent systems (e.g., thecognitive intelligence system 15510) or users of the digital twin system15500, according to some embodiments of the present disclosure. Thestates 15540 a-n may include operational states (e.g., suboptimal,normal, optimal, critical, or alarm operation of one or morecomponents), excess or shortage states (e.g., supply-side, oroutput-side quantities), combinations thereof, and the like.

In embodiments, the digital twin system 15500 may monitor attributes151404 a-n of real-world elements 157302 r and/or digital twins 157302 dto determine the respective state 15540 a-n. The attributes 151404 a-nmay be, for example, operating conditions, set points, critical points,status indicators, other sensed information, combinations thereof, andthe like. For example, the attributes 151404 a-n may include power input151404 a, operational speed 151404 b, critical speed 151404 c, andoperational temperature 151404 d of the monitored elements. While theillustrated example illustrates uniform monitored attributes, themonitored attributes may differ by target device (e.g., the digital twinsystem 15500 would not monitor rotational speed for an object with norotatable components).

Each of the states 15540 a-n includes a set of identifying criteria151406 a-n meeting particular criteria that are unique among the groupof monitored states 15540 a-n. The digital twin system 15500 mayidentify the overspeed state 15540 a, for example, in response to themonitored attributes 151404 a-n meeting a first set of identifyingcriteria 151406 a (e.g., operational speed 151404 b being higher thanthe critical speed 151404 c, while the operational temperature 151404 dis nominal).

In response to determining that one or more states 15540 a-n exists orhas occurred, the digital twin system 15500 may update triggeringconditions for one or more monitoring protocols, issue an alert ornotification, or trigger actions of subcomponents of the digital twinsystem 15500. For example, subcomponents of the digital twin system15500 may take actions to mitigate and/or evaluate impacts of thedetected states 15540 a-n. When attempting to take actions to mitigateimpacts of the detected states 15540 a-n on real-world elements 157302r, the digital twin system 15500 may determine whether instructionsexist (e.g., are stored in the digital twin datastore 15516) or shouldbe developed (e.g., developed via simulation and cognitive intelligenceor via user or worker input). Further, the digital twin system 15500 mayevaluate impacts of the detected states 15540 a-n, for example,concurrently with the mitigation actions or in response to determiningthat the digital twin system 15500 has no stored mitigation instructionsfor the detected states 15540 a-n.

In embodiments, the digital twin system 15500 employs the digital twinsimulation system 15506 to simulate one or more impacts, such asimmediate, upstream, downstream, and/or continuing effects, ofrecognized states. The digital twin simulation system 15506 may collectand/or be provided with values relevant to the evaluated states 15540a-n. In simulating the impact of the one or more states 15540 a-n, thedigital twin simulation system 15506 may recursively evaluateperformance characteristics of affected digital twins 157302 d untilconvergence is achieved. The digital twin simulation system 15506 maywork, for example, in tandem with the cognitive intelligence system15510 to determine response actions to alleviate, mitigate, inhibit,and/or prevent occurrence of the one or more states 15540 a-n. Forexample, the digital twin simulation system 15506 may recursivelysimulate impacts of the one or more states 15540 a-n until achieving adesired fit (e.g., convergence is achieved), provide the simulatedvalues to the cognitive intelligence system 15510 for evaluation anddetermination of potential actions, receive the potential actions,evaluate impacts of each of the potential actions for a respectivedesired fit (e.g., cost functions for minimizing production disturbance,preserving critical components, minimizing maintenance and/or downtime,optimizing system, worker, user, or personal safety, etc.).

In embodiments, the digital twin simulation system 15506 and thecognitive intelligence system 15510 may repeatedly share and update thesimulated values and response actions for each desired outcome untildesired conditions are met (e.g., convergence for each evaluated costfunction for each evaluated action). The digital twin system 15500 maystore the results in the digital twin datastore 15516 for use inresponse to determining that one or more states 15540 a-n has occurred.Additionally, simulations and evaluations by the digital twin simulationsystem 15506 and/or the cognitive intelligence system 15510 may occur inresponse to occurrence or detection of the event.

In embodiments, simulations and evaluations are triggered only whenassociated actions are not present within the digital twin system 15500.In further embodiments, simulations and evaluations are performedconcurrently with use of stored actions to evaluate the efficacy oreffectiveness of the actions in real time and/or evaluate whetherfurther actions should be employed or whether unrecognized states mayhave occurred. In embodiments, the cognitive intelligence system 15510may also be provided with notifications of instances of undesiredactions with or without data on the undesired aspects or results of suchactions to optimize later evaluations.

In embodiments, the digital twin system 15500 evaluates and/orrepresents the impact of machine downtime within a digital twin of amanufacturing facility. For example, the digital twin system 15500 mayemploy the digital twin simulation system 15506 to simulate theimmediate, upstream, downstream, and/or continuing effects of a machinedowntime state 15540 b. The digital twin simulation system 15506 maycollect or be provided with performance-related values such as optimal,suboptimal, and minimum performance requirements for elements (e.g.,real-world elements 157302 r and/or nested digital twins 157302 d)within the affected digital twins 157302 d, and/or characteristicsthereof that are available to the affected digital twins 157302 d,nested digital twins 157302 d, redundant systems within the affecteddigital twins 157302 d, combinations thereof, and the like.

In embodiments, the digital twin system 15500 is configured to: simulateone or more operating parameters for the real-world elements in responseto the industrial environment being supplied with given characteristicsusing the real-world-element digital twins; calculate a mitigatingaction to be taken by one or more of the real-world elements in responseto being supplied with the contemporaneous characteristics; and actuate,in response to detecting the contemporaneous characteristics, themitigating action. The calculation may be performed in response todetecting contemporaneous characteristics or operating parametersfalling outside of respective design parameters or may be determined viaa simulation prior to detection of such characteristics.

Additionally or alternatively, the digital twin system 15500 may providealerts to one or more users or system elements in response to detectingstates.

In embodiments, the digital twin I/O system 15504 includes a pathingmodule 157310. The pathing module 157310 may ingest navigational datafrom the elements 157302, provide and/or request navigational data tocomponents of the digital twin system 15500 (e.g., the digital twinsimulation system 15506, the digital twin behavior system, and/or thecognitive intelligence system 15510), and/or output navigational data toelements 157302 (e.g., to the wearable devices 15554). The navigationaldata may be collected or estimated using, for example, historical data,guidance data provided to the elements 157302, combinations thereof, andthe like.

For example, the navigational data may be collected or estimated usinghistorical data stored by the digital twin system 15500. The historicaldata may include or be processed to provide information such asacquisition time, associated elements 157302, polling intervals, taskperformed, laden or unladen conditions, whether prior guidance data wasprovided and/or followed, conditions of the environment 15520, otherelements 157302 within the environment 15520, combinations thereof, andthe like. The estimated data may be determined using one or moresuitable pathing algorithms. For example, the estimated data may becalculated using suitable order-picking algorithms, suitable path-searchalgorithms, combinations thereof, and the like. The order-pickingalgorithm may be, for example, a largest gap algorithm, an s-shapealgorithm, an aisle-by-aisle algorithm, a combined algorithm,combinations thereof, and the like. The path-search algorithms may be,for example, Dijkstra's algorithm, the A* algorithm, hierarchicalpath-finding algorithms, incremental path-finding algorithms, any anglepath-finding algorithms, flow field algorithms, combinations thereof,and the like.

Additionally or alternatively, the navigational data may be collected orestimated using guidance data of the worker. The guidance data mayinclude, for example, a calculated route provided to a device of theworker (e.g., a mobile device or the wearable device 15554). In anotherexample, the guidance data may include a calculated route provided to adevice of the worker that instructs the worker to collect vibrationmeasurements from one or more locations on one or more machines alongthe route. The collected and/or estimated navigational data may beprovided to a user of the digital twin system 15500 for visualization,used by other components of the digital twin system 15500 for analysis,optimization, and/or alteration, provided to one or more elements157302, combinations thereof, and the like.

In embodiments, the digital twin system 15500 ingests navigational datafor a set of workers for representation in a digital twin. Additionallyor alternatively, the digital twin system 15500 ingests navigationaldata for a set of mobile equipment assets of an industrial environmentinto a digital twin.

In embodiments, the digital twin system 15500 ingests a system formodeling traffic of mobile elements in an industrial digital twin. Forexample, the digital twin system 15500 may model traffic patterns forworkers or persons within the environment 15520, mobile equipmentassets, combinations thereof, and the like. The traffic patterns may beestimated based on modeling traffic patterns from and historical dataand contemporaneous ingested data. Further, the traffic patterns may becontinuously or intermittently updated depending on conditions withinthe environment 15520 (e.g., a plurality of autonomous mobile equipmentassets may provide information to the digital twin system 15500 at aslower update interval than the environment 15520 including both workersand mobile equipment assets).

The digital twin system 15500 may alter traffic patterns (e.g., byproviding updated navigational data to one or more of the mobileelements) to achieve one or more predetermined criteria. Thepredetermined criteria may include, for example, increasing processefficiency, decreasing interactions between laden workers and mobileequipment assets, minimizing worker path length, routing mobileequipment around paths or potential paths of persons, combinationsthereof, and the like.

In embodiments, the digital twin system 15500 may provide traffic dataand/or navigational information to mobile elements in an industrialdigital twin. The navigational information may be provided asinstructions or rule sets, displayed path data, or selective actuationof devices. For example, the digital twin system 15500 may provide a setof instructions to a robot to direct the robot to and/or along a desiredroute for collecting vibration data from one or more specified locationson one or more specified machines along the route using a vibrationsensor. The robot may communicate updates to the system includingobstructions, reroutes, unexpected interactions with other assets withinthe environment 15520, etc.

In some embodiments, an ant-based system 15574 enables industrialentities, including robots, to lay a trail with one or more messages forother industrial entities, including themselves, to follow in laterjourneys. In embodiments, the messages include information related tovibration measurement collection. In embodiments, the messages includeinformation related to vibration sensor measurement locations. In someembodiments, the trails may be configured to fade over time. In someembodiments, the ant-based trails may be experienced via an augmentedreality system. In some embodiments, the ant-based trails may beexperienced via a virtual reality system. In some embodiments, theant-based trails may be experienced via a mixed reality system. In someembodiments, ant-based tagging of areas can trigger a pain-responseand/or accumulate into a warning signal. In embodiments, the ant-basedtrails may be configured to generate an information filtering response.In some embodiments, the ant-based trails may be configured to generatean information filtering response wherein the information filteringresponse is a heightened sense of visual awareness. In some embodiments,the ant-based trails may be configured to generate an informationfiltering response wherein the information filtering response is aheightened sense of acoustic awareness. In some embodiments, themessages include vectorized data.

In embodiments, the digital twin system 15500 includes designspecification information for representing a real-world element 157302 rusing a digital twin 157302 d. The digital may correspond to an existingreal-world element 157302 r or a potential real-world element 157302 r.The design specification information may be received from one or moresources. For example, the design specification information may includedesign parameters set by user input, determined by the digital twinsystem 15500 (e.g., the via digital twin simulation system 15506),optimized by users or the digital twin simulation system 15506,combinations thereof, and the like. The digital twin simulation system15506 may represent the design specification information for thecomponent to users, for example, via a display device or a wearabledevice. The design specification information may be displayedschematically (e.g., as part of a process diagram or table ofinformation) or as part of an augmented reality or virtual realitydisplay. The design specification information may be displayed, forexample, in response to a user interaction with the digital twin system15500 (e.g., via user selection of the element or user selection togenerally include design specification information within displays).Additionally or alternatively, the design specification information maybe displayed automatically, for example, upon the element coming withinview of an augmented reality or virtual reality device. In embodiments,the displayed design specification information may further includeindicia of information source (e.g., different displayed colors indicateuser input versus digital twin system 15500 determination), indicia ofmismatches (e.g., between design specification information andoperational information), combinations thereof, and the like.

In embodiments, the digital twin system 15500 embeds a set of controlinstructions for a wearable device within an industrial digital twin,such that the control instructions are provided to the wearable deviceto induce an experience for a wearer of the wearable device uponinteraction with an element of the industrial digital twin. The inducedexperience may be, for example, an augmented reality experience or avirtual reality experience. The wearable device, such as a headset, maybe configured to output video, audio, and/or haptic feedback to thewearer to induce the experience. For example, the wearable device mayinclude a display device and the experience may include display ofinformation related to the respective digital twin. The informationdisplayed may include maintenance data associated with the digital twin,vibration data associated with the digital twin, vibration measurementlocation data associated with the digital twin, financial dataassociated with the digital twin, such as a profit or loss associatedwith operation of the digital twin, manufacturing KPIs associated withthe digital twin, information related to an occluded element (e.g., asub-assembly) that is at least partially occluded by a foregroundelement (e.g., a housing), a virtual model of the occluded elementoverlaid on the occluded element and visible with the foregroundelement, operating parameters for the occluded element, a comparison toa design parameter corresponding to the operating parameter displayed,combinations thereof, and the like. Comparisons may include, forexample, altering display of the operating parameter to change a color,size, and/or display period for the operating parameter.

In some embodiments, the displayed information may include indicia forremovable elements that are or may be configured to provide access tothe occluded element with each indicium being displayed proximate to oroverlying the respective removable element. Further, the indicia may besequentially displayed such that a first indicium corresponding to afirst removable element (e.g., a housing) is displayed, and a secondindicium corresponding to a second removable element (e.g., an accesspanel within the housing) is displayed in response to the workerremoving the first removable element. In some embodiments, the inducedexperience allows the wearer to see one or more locations on a machinefor optimal vibration measurement collection. In an example, the digitaltwin system 15500 may provide an augmented reality view that includeshighlighted vibration measurement collection locations on a machineand/or instructions related to collecting vibration measurements.Furthering the example, the digital twin system 15500 may provide anaugmented reality view that includes instructions related to timing ofvibration measurement collection. Information utilized in displaying thehighlighted placement locations may be obtained using information storedby the digital twin system 15500. In some embodiments, mobile elementsmay be tracked by the digital twin system 15500 (e.g., via observationalelements within the environment 15520 and/or via pathing informationcommunicated to the digital twin system 15500) and continually displayedby the wearable device within the occluded view of the worker. Thisoptimizes movement of elements within the environment 15520, increasesworker safety, and minimizes down time of elements resulting fromdamage.

In some embodiments, the digital twin system 15500 may provide anaugmented reality view that displays mismatches between designparameters or expected parameters of real-world elements 157302 r to thewearer. The displayed information may correspond to real-world elements157302 r that are not within the view of the wearer (e.g., elementswithin another room or obscured by machinery). This allows the worker toquickly and accurately troubleshoot mismatches to determine one or moresources for the mismatch. The cause of the mismatch may then bedetermined, for example, by the digital twin system 15500 and correctiveactions ordered. In example embodiments, a wearer may be able to viewmalfunctioning subcomponents of machines without removing occludingelements (e.g., housings or shields). Additionally or alternatively, thewearer may be provided with instructions to repair the device, forexample, including display of the removal process (e.g., location offasteners to be removed), assemblies or subassemblies that should betransported to other areas for repair (e.g., dust-sensitive components),assemblies or subassemblies that need lubrication, and locations ofobjects for reassembly (e.g., storing location that the wearer hasplaced removed objects and directing the wearer or another wearer to thestored locations to expedite reassembly and minimize further disassemblyor missing parts in the reassembled element). This can expedite repairwork, minimize process impact, allow workers to disassemble andreassemble equipment (e.g., by coordinating disassembly without directcommunication between the workers), increase equipment longevity andreliability (e.g., by assuring that all components are properly replacedprior to placing back in service), combinations thereof, and the like.

In some embodiments, the induced experience includes a virtual realityview or an augmented reality view that allows the wearer to seeinformation related to existing or planned elements. The information maybe unrelated to physical performance of the element (e.g., financialperformance such as asset value, energy cost, input material cost,output material value, legal compliance, and corporate operations). Oneor more wearers may perform a virtual walkthrough or an augmentedwalkthrough of the industrial environment 15520.

In examples, the wearable device displays compliance information thatexpedites inspections or performance of work.

In further examples, the wearable device displays financial informationthat is used to identify targets for alteration or optimization. Forexample, a manager or officer may inspect the environment 15520 forcompliance with updated regulations, including information regardingcompliance with former regulations, “grandfathered,” and/or exceptedelements. This can be used to reduce unnecessary downtime (e.g.,scheduling upgrades for the least impactful times, such as duringplanned maintenance cycles), prevent unnecessary upgrades (e.g.,replacing grandfathered or excepted equipment), and reduce capitalinvestment.

Referring back to FIG. 155 , in embodiments, the digital twin system15500 may include, integrate, integrate with, manage, handle, link to,take input from, provide output to, control, coordinate with, orotherwise interact with a digital twin dynamic model system 15508. Thedigital twin dynamic model system 15508 can update the properties of aset of digital twins of a set of industrial entities and/orenvironments, including properties of physical industrial assets,workers, processes, manufacturing facilities, warehouses, and the like(or any of the other types of entities or environments described in thisdisclosure or in the documents incorporated by reference herein) in sucha manner that the digital twins may represent those industrial entitiesand environments, and properties or attributes thereof, in real-time orvery near real-time. In some embodiments, the digital twin dynamic modelsystem 15508 may obtain sensor data received from a sensor system 15530and may determine one or more properties of an industrial environment oran industrial entity within an environment based on the sensor data andbased on one or more dynamic models.

In embodiments, the digital twin dynamic model system 15508 mayupdate/assign values of various properties in a digital twin and/or oneor more embedded digital twins, including, but not limited to, vibrationvalues, vibration fault level states, probability of failure values,probability of downtime values, cost of downtime values, probability ofshutdown values, financial values, KPI values, temperature values,humidity values, heat flow values, fluid flow values, radiation values,substance concentration values, velocity values, acceleration values,location values, pressure values, stress values, strain values, lightintensity values, sound level values, volume values, shapecharacteristics, material characteristics, and dimensions.

In embodiments, a digital twin may be comprised of (e.g., via reference)of other embedded digital twins. For example, a digital twin of amanufacturing facility may include an embedded digital twin of a machineand one or more embedded digital twins of one or more respective motorsenclosed within the machine. A digital twin may be embedded, forexample, in the memory of an industrial machine that has an onboard ITsystem (e.g., the memory of an Onboard Diagnostic System, control system(e.g., SCADA system) or the like). Other non-limiting examples of wherea digital twin may be embedded include the following: on a wearabledevice of a worker; in memory on a local network asset, such as aswitch, router, access point, or the like; in a cloud computing resourcethat is provisioned for an environment or entity; and on an asset tag orother memory structure that is dedicated to an entity.

In one example, the digital twin dynamic model system 15508 can updatevibration characteristics throughout an industrial environment digitaltwin based on captured vibration sensor data measured at one or morelocations in the industrial environment and one or more dynamic modelsthat model vibration within the industrial environment digital twin. Theindustrial digital twin may, before updating, already containinformation about properties of the industrial entities and/orenvironment that can be used to feed a dynamic model, such asinformation about materials, shapes/volumes (e.g., of conduits),positions, connections/interfaces, and the like, such that vibrationcharacteristics can be represented for the entities and/or environmentin the digital twin. Alternatively, the dynamic models may be configuredusing such information.

In embodiments, the digital twin dynamic model system 15508 can updatethe properties of a digital twin and/or one or more embedded digitaltwins on behalf of a client application 15570. In embodiments, a clientapplication 15570 may be an application relating to an industrialcomponent or environment (e.g., monitoring an industrial facility or acomponent within, simulating an industrial environment, or the like). Inembodiments, the client application 15570 may be used in connection withboth fixed and mobile data collection systems. In embodiments, theclient application 15570 may be used in connection with IndustrialInternet of Things sensor system 15530.

In embodiments, the digital twin dynamic model system 15508 leveragesdigital twin dynamic models 155100 to model the behavior of anindustrial entity and/or environment. Dynamic models 155100 may enabledigital twins to represent physical reality, including the interactionsof industrial entities, by using a limited number of measurements toenrich the digital representation of an industrial entity and/orenvironment, such as based on scientific principles. In embodiments, thedynamic models 155100 are formulaic or mathematical models. Inembodiments, the dynamic models 155100 adhere to scientific laws, lawsof nature, and formulas (e.g., Newton's laws of motion, second law ofthermodynamics, Bernoulli's principle, ideal gas law, Dalton's law ofpartial pressures, Hooke's law of elasticity, Fourier's law of heatconduction, Archimedes' principle of buoyancy, and the like). Inembodiments, the dynamic models are machine-learned models.

In embodiments, the digital twin system 15500 may have a digital twindynamic model datastore 155102 for storing dynamic models 155100 thatmay be represented in digital twins. In embodiments, digital twindynamic model datastore can be searchable and/or discoverable. Inembodiments, digital twin dynamic model datastore can contain metadatathat allows a user to understand what characteristics a given dynamicmodel can handle, what inputs are required, what outputs are provided,and the like. In some embodiments, digital twin dynamic model datastore155102 can be hierarchical (such as where a model can be deepened ormade more simple based on the extent of available data and/or inputs,the granularity of the inputs, and/or situational factors (such as wheresomething becomes of high interest and a higher fidelity model isaccessed for a period of time).

In embodiments, a digital twin or digital representation of anindustrial entity or facility may include a set of data structures thatcollectively define a set of properties of a represented physicalindustrial asset, device, worker, process, facility, and/or environment,and/or possible behaviors thereof. In embodiments, the digital twindynamic model system 15508 may leverage the dynamic models 155100 toinform the set of data structures that collectively define a digitaltwin with real-time data values. The digital twin dynamic models 155100may receive one or more sensor measurements, Industrial Internet ofThings device data, and/or other suitable data as inputs and calculateone or more outputs based on the received data and one or more dynamicmodels 155100. The digital twin dynamic model system 15508 then uses theone or more outputs to update the digital twin data structures.

In one example, the set of properties of a digital twin of an industrialasset that may be updated by the digital twin dynamic model system 15508using dynamic models 155100 may include the vibration characteristics ofthe asset, temperature(s) of the asset, the state of the asset (e.g., asolid, liquid, or gas), the location of the asset, the displacement ofthe asset, the velocity of the asset, the acceleration of the asset,probability of downtime values associated with the asset, cost ofdowntime values associated with the asset, probability of shutdownvalues associated with the asset, manufacturing KPIs associated with theasset, financial information associated with the asset, heat flowcharacteristics associated with the asset, fluid flow rates associatedwith the asset (e.g., fluid flow rates of a fluid flowing through apipe), identifiers of other digital twins embedded within the digitaltwin of the asset and/or identifiers of digital twins embedding thedigital twin of the asset, and/or other suitable properties. Dynamicmodels 155100 associated with a digital twin of an asset can beconfigured to calculate, interpolate, extrapolate, and/or output valuesfor such asset digital twin properties based on input data collectedfrom sensors and/or devices disposed in the industrial setting and/orother suitable data and subsequently populate the asset digital twinwith the calculated values.

In some embodiments, the set of properties of a digital twin of anindustrial device that may be updated by the digital twin dynamic modelsystem 15508 using dynamic models 155100 may include the status of thedevice, a location of the device, the temperature(s) of a device, atrajectory of the device, identifiers of other digital twins that thedigital twin of the device is embedded within, embeds, is linked to,includes, integrates with, takes input from, provides output to, and/orinteracts with and the like. Dynamic models 155100 associated with adigital twin of a device can be configured to calculate or output valuesfor these device digital twin properties based on input data andsubsequently update the device digital twin with the calculated values.

In some embodiments, the set of properties of a digital twin of anindustrial worker that may be updated by the digital twin dynamic modelsystem 15508 using dynamic models 155100 may include the status of theworker, the location of the worker, a stress measure for the worker, atask being performed by the worker, a performance measure for theworker, and the like. Dynamic models associated with a digital twin ofan industrial worker can be configured to calculate or output values forsuch properties based on input data, which then may be used to populateindustrial worker digital twin. In embodiments, industrial workerdynamic models (e.g., psychometric models) can be configured to predictreactions to stimuli, such as cues that are given to workers to directthem to undertake tasks and/or alerts or warnings that are intended toinduce safe behavior. In embodiments, industrial worker dynamic modelsmay be workflow models (Gantt charts and the like), failure mode effectsanalysis models (FMEA), biophysical models (such as to model workerfatigue, energy utilization, hunger), and the like.

Example properties of a digital twin of an industrial environment thatmay be updated by the digital twin dynamic model system 15508 usingdynamic models 155100 may include the dimensions of the industrialenvironment, the temperature(s) of the industrial environment, thehumidity value(s) of the industrial environment, the fluid flowcharacteristics in the industrial environment, the heat flowcharacteristics of the industrial environment, the lightingcharacteristics of the industrial environment, the acousticcharacteristics of the industrial environment the physical objects inthe environment, processes occurring in the industrial environment,currents of the industrial environment (if a body of water), and thelike. Dynamic models associated with a digital twin of an industrialenvironment can be configured to calculate or output these propertiesbased on input data collected from sensors and/or devices disposed inthe industrial environment and/or other suitable data and subsequentlypopulate the industrial environment digital twin with the calculatedvalues.

In embodiments, dynamic models 155100 may adhere to physical limitationsthat define boundary conditions, constants, or variables for digitaltwin modeling. For example, the physical characterization of a digitaltwin of an industrial entity or industrial environment may include agravity constant (e.g., 9.8 m/s2), friction coefficients of surfaces,thermal coefficients of materials, maximum temperatures of assets,maximum flow capacities, and the like. Additionally or alternatively,the dynamic models may adhere to the laws of nature. For example,dynamic models may adhere to the laws of thermodynamics, laws of motion,laws of fluid dynamics, laws of buoyancy, laws of heat transfer, laws ofradiation, laws of quantum dynamics, and the like. In some embodiments,dynamic models may adhere to biological aging theories or mechanicalaging principles. Thus, when the digital twin dynamic model system 15508facilitates a real-time digital representation, the digitalrepresentation may conform to dynamic models, such that the digitalrepresentations mimic real world conditions. In some embodiments, theoutput(s) from a dynamic model can be presented to a human user and/orcompared against real-world data to ensure convergence of the dynamicmodels with the real world. Furthermore, as dynamic models are basedpartly on assumptions, the properties of a digital twin may be improvedand/or corrected when a real-world behavior differs from that of thedigital twin. In embodiments, additional data collection and/orinstrumentation can be recommended based on the recognition that aninput is missing from a desired dynamic model, that a model in operationis not working as expected (perhaps due to missing and/or faulty sensorinformation), that a different result is needed (such as due tosituational factors that make something of high interest), and the like.

Dynamic models may be obtained from a number of different sources. Insome embodiments, a user can upload a model created by the user or athird party. Additionally or alternatively, the models may be created onthe digital twin system using a graphical user interface. The dynamicmodels may include bespoke models that are configured for a particularenvironment and/or set of industrial entities and/or agnostic modelsthat are applicable to similar types of digital twins. The dynamicmodels may be machine-learned models.

FIG. 159 illustrates example embodiments of a method for updating a setof properties of a digital twin and/or one or more embedded digitaltwins on behalf of client applications 15570. In embodiments, digitaltwin dynamic model system 15508 leverages one or more dynamic models155100 to update a set of properties of a digital twin and/or one ormore embedded digital twins on behalf of client application 15570 basedon the impact of collected sensor data from sensor system 15530, datacollected from Internet of Things connected devices 15524, and/or othersuitable data in the set of dynamic models 155100 that are used toenable the industrial digital twins. In embodiments, the digital twindynamic model system 15508 may be instructed to run specific dynamicmodels using one or more digital twins that represent physicalindustrial assets, devices, workers, processes, and/or industrialenvironments that are managed, maintained, and/or monitored by theclient applications 15570.

In embodiments, the digital twin dynamic model system 15508 may obtaindata from other types of external data sources that are not necessarilyindustrial-related data sources, but may provide data that can be usedas input data for the dynamic models. For example, weather data, newsevents, social media data, and the like may be collected, crawled,subscribed to, and the like to supplement sensor data, IndustrialInternet of Things device data, and/or other data that is used by thedynamic models. In embodiments, the digital twin dynamic model system15508 may obtain data from a machine vision system. The machine visionsystem may use video and/or still images to provide measurements (e.g.,locations, statuses, and the like) that may be used as inputs by thedynamic models.

In embodiments, the digital twin dynamic model system 15508 may feedthis data into one or more of the dynamic models discussed above toobtain one or more outputs. These outputs may include calculatedvibration fault level states, vibration severity unit values, vibrationcharacteristics, probability of failure values, probability of downtimevalues, probability of shutdown values, cost of downtime values, cost ofshutdown values, time to failure values, temperature values, pressurevalues, humidity values, precipitation values, visibility values, airquality values, strain values, stress values, displacement values,velocity values, acceleration values, location values, performancevalues, financial values, manufacturing KPI values, electrodynamicvalues, thermodynamic values, fluid flow rate values, and the like. Theclient application 15570 may then initiate a digital twin visualizationevent using the results obtained by the digital twin dynamic modelsystem 15508. In embodiments, the visualization may be a heat mapvisualization.

In embodiments, the digital twin dynamic model system 15508 may receiverequests to update one or more properties of digital twins of industrialentities and/or environments such that the digital twins represent theindustrial entities and/or environments in real-time. At 159100, thedigital twin dynamic model system 15508 receives a request to update oneor more properties of one or more of the digital twins of industrialentities and/or environments. For example, the digital twin dynamicmodel system 15508 may receive the request from a client application15570 or from another process executed by the digital twin system 15500(e.g., a predictive maintenance process). The request may indicate theone or more properties and the digital twin or digital twins implicatedby the request. In step 159102, the digital twin dynamic model system15508 determines the one or more digital twins required to fulfill therequest and retrieves the one or more required digital twins, includingany embedded digital twins, from digital twin datastore 15516. At159104, digital twin dynamic model system 15508 determines one or moredynamic models required to fulfill the request and retrieves the one ormore required dynamic models from digital twin dynamic model store155102. At 159106, the digital twin dynamic model system 15508 selectsone or more sensors from sensor system 15530, data collected fromInternet of Things connected devices 15524, and/or other data sourcesfrom digital twin I/O system 15504 based on available data sources andthe one or more required inputs of the dynamic model(s). In embodiments,the data sources may be defined in the inputs required by the one ormore dynamic models or may be selected using a lookup table. At 159108,the digital twin dynamic model system 15508 retrieves the selected datafrom digital twin I/O system 15504. At 159110, digital twin dynamicmodel system 15508 runs the dynamic model(s) using the retrieved inputdata (e.g., vibration sensor data, Industrial Internet of Things devicedata, and the like) as inputs and determines one or more output valuesbased on the dynamic model(s) and the input data. At 159112, the digitaltwin dynamic model system 15508 updates the values of one or moreproperties of the one or more digital twins based on the one or moreoutputs of the dynamic model(s).

In example embodiments, client application 15570 may be configured toprovide a digital representation and/or visualization of the digitaltwin of an industrial entity. In embodiments, the client application15570 may include one or more software modules that are executed by oneor more server devices. These software modules may be configured toquantify properties of the digital twin, model properties of a digitaltwin, and/or to visualize digital twin behaviors. In embodiments, thesesoftware modules may enable a user to select a particular digital twinbehavior visualization for viewing. In embodiments, these softwaremodules may enable a user to select to view a digital twin behaviorvisualization playback. In some embodiments, the client application15570 may provide a selected behavior visualization to digital twindynamic model system 15508.

In embodiments, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to update properties of a digitaltwin in order to enable a digital representation of an industrial entityand/or environment wherein the real-time digital representation is avisualization of the digital twin. In embodiments, a digital twin may berendered by a computing device, such that a human user can view thedigital representations of real-world industrial assets, devices,workers, processes and/or environments. For example, the digital twinmay be rendered and outcome to a display device. In embodiments, dynamicmodel outputs and/or related data may be overlaid on the rendering ofthe digital twin. In embodiments, dynamic model outputs and/or relatedinformation may appear with the rendering of the digital twin in adisplay interface. In embodiments, the related information may includereal-time video footage associated with the real-world entityrepresented by the digital twin. In embodiments, the related informationmay include a sum of each of the vibration fault level states in themachine. In embodiments, the related information may be graphicalinformation. In embodiments, the graphical information may depict motionand/or motion as a function of frequency for individual machinecomponents. In embodiments, graphical information may depict motionand/or motion as a function of frequency for individual machinecomponents, wherein a user is enabled to select a view of the graphicalinformation in the x, y, and z dimensions. In embodiments, graphicalinformation may depict motion and/or motion as a function of frequencyfor individual machine components, wherein the graphical informationincludes harmonic peaks and peaks. In embodiments, the relatedinformation may be cost data, including the cost of downtime per daydata, cost of repair data, cost of new part data, cost of new machinedata, and the like. In embodiments, related information may be aprobability of downtime data, probability of failure data, and the like.In embodiments, related information may be time to failure data.

In embodiments, the related information may be recommendations and/orinsights. For example, recommendations or insights received from thecognitive intelligence system related to a machine may appear with therendering of the digital twin of a machine in a display interface.

In embodiments, clicking, touching, or otherwise interacting with thedigital twin rendered in the display interface can allow a user to“drill down” and see underlying subsystems or processes and/or embeddeddigital twins. For example, in response to a user clicking on a machinebearing rendered in the digital twin of a machine, the display interfacecan allow a user to drill down and see information related to thebearing, view a 3D visualization of the bearing's vibration, and/or viewa digital twin of the bearing.

In embodiments, clicking, touching, or otherwise interacting withinformation related to the digital twin rendered in the displayinterface can allow a user to “drill down” and see underlyinginformation.

FIG. 160 illustrates example embodiments of a display interface thatrenders the digital twin of a dryer centrifuge and other informationrelated to the dryer centrifuge.

In some embodiments, the digital twin may be rendered and output in avirtual reality display. For example, a user may view a 3D rendering ofan environment (e.g., using a monitor or a virtual reality headset). Theuser may also inspect and/or interact with digital twins of industrialentities. In embodiments, a user may view processes being performed withrespect to one or more digital twins (e.g., collecting measurements,movements, interactions, inventorying, loading, packing, shipping, andthe like). In embodiments, a user may provide input that controls one ormore properties of a digital twin via a graphical user interface.

In some embodiments, the digital twin dynamic model system 15508 mayreceive requests from client application 15570 to update properties of adigital twin in order to enable a digital representation of industrialentities and/or environments wherein the digital representation is aheat map visualization of the digital twin. In embodiments, a platformis provided having heat maps displaying collected data from the sensorsystem 15530, Internet of Things connected devices 15524, and dataoutputs from dynamic models 155100 for providing input to a displayinterface. In embodiments, the heat map interface is provided as anoutput for digital twin data, such as for handling and providinginformation for visualization of various sensor data, dynamic modeloutput data, and other data (such as map data, analog sensor data, andother data), such as to another system, such as a mobile device, tablet,dashboard, computer, AR/VR device, or the like. A digital twinrepresentation may be provided in a form factor (e.g., user device,VR-enabled device, AR-enabled device, or the like) suitable fordelivering visual input to a user, such as the presentation of a mapthat includes indicators of levels of analog sensor data, digital sensordata, and output values from the dynamic models (such as data indicatingvibration fault level states, vibration severity unit values,probability of downtime values, cost of downtime values, probability ofshutdown values, time to failure values, probability of failure values,manufacturing KPIs, temperatures, levels of rotation, vibrationcharacteristics, fluid flow, heating or cooling, pressure, substanceconcentrations, and many other output values). In embodiments, signalsfrom various sensors or input sources (or selective combinations,permutations, mixes, and the like) as well as data determined by thedigital twin dynamic model system 15508 may provide input data to a heatmap. Coordinates may include real world location coordinates (such asgeo-location or location on a map of an environment), as well as othercoordinates, such as time-based coordinates, frequency-basedcoordinates, or other coordinates that allow for representation ofanalog sensor signals, digital signals, dynamic model outputs, inputsource information, and various combinations, in a map-basedvisualization, such that colors may represent varying levels of inputalong the relevant dimensions. For example, among many otherpossibilities, if an industrial machine component is at a criticalvibration fault level state, the heat map interface may alert a user byshowing the machine component in orange. In the example of a heat map,clicking, touching, or otherwise interacting with the heat map can allowa user to drill down and see underlying sensor, dynamic model outputs,or other input data that is used as an input to the heat map display. Inother examples, such as ones where a digital twin is displayed in a VRor AR environment, if an industrial machine component is vibratingoutside of normal operation (e.g., at a suboptimal, critical, or alarmvibration fault level), a haptic interface may induce vibration when auser touches a representation of the machine component, or if a machinecomponent is operating in an unsafe manner, a directional sound signalmay direct a user's attention toward the machine in digital twin, suchas by playing in a particular speaker of a headset or other soundsystem.

In embodiments, the digital twin dynamic model system 15508 may take aset of ambient environmental data and/or other data and automaticallyupdate a set of properties of a digital twin of an industrial entity orfacility based on the impact of the environmental data and/or other datain the set of dynamic models 155100 that are used to enable the digitaltwin. Ambient environmental data may include temperature data, pressuredata, humidity data, wind data, rainfall data, tide data, storm surgedata, cloud cover data, snowfall data, visibility data, water leveldata, and the like. Additionally or alternatively, the digital twindynamic model system 15508 may use a set of environmental datameasurements collected by a set of Internet of Things connected devices15524 disposed in an industrial setting as inputs for the set of dynamicmodels 155100 that are used to enable the digital twin. For example,digital twin dynamic model system 15508 may feed the dynamic models155100 data collected, handled or exchanged by Internet of Thingsconnected devices 15524, such as cameras, monitors, embedded sensors,mobile devices, diagnostic devices and systems, instrumentation systems,telematics systems, and the like, such as for monitoring variousparameters and features of machines, devices, components, parts,operations, functions, conditions, states, events, workflows and otherelements (collectively encompassed by the term “states”) of industrialenvironments. Other examples of Internet of Things connected devicesinclude smart fire alarms, smart security systems, smart air qualitymonitors, smart/learning thermostats, and smart lighting systems.

FIG. 161 illustrates example embodiments of a method for updating a setof vibration fault level states for a set of bearings in a digital twinof a machine. In this example, a client application 15570, whichinterfaces with digital twin dynamic model system 15508, may beconfigured to provide a visualization of the fault level states of thebearings in the digital twin of the machine.

In this example, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to update the vibration faultlevel states of the machine digital twin. At 161200, digital twindynamic model system 15508 receives a request from client application15570 to update one or more vibration fault level states of the machinedigital twin. Next, in step 161202, digital twin dynamic model system15508 determines the one or more digital twins required to fulfill therequest and retrieves the one or more required digital twins fromdigital twin datastore 15516. In this example, the digital twin dynamicmodel system 15508 may retrieve the digital twin of the machine and anyembedded digital twins, such as any embedded motor digital twins andbearing digital twins, and any digital twins that embed the machinedigital twin, such as the manufacturing facility digital twin. At161204, digital twin dynamic model system 15508 determines one or moredynamic models required to fulfill the request and retrieves the one ormore required dynamic models from the digital twin dynamic modeldatastore 155102. At 161206, the digital twin dynamic model system 15508selects dynamic model input data sources (e.g., one or more sensors fromsensor system 15530, data from Internet of Things connected devices15524, and any other suitable data) via digital twin I/O system 15504based on available data sources (e.g., available sensors from a set ofsensors in sensor system 15530) and the and the one or more requiredinputs of the dynamic model(s). In the present example, the retrieveddynamic model(s) 155100 may take one or more vibration sensormeasurements from vibration sensors 15536 as inputs to the dynamicmodels. In embodiments, vibration sensors 15536 may be optical vibrationsensors, single axis vibration sensors, tri-axial vibration sensors, andthe like. At 161208, digital twin dynamic model system 15508 retrievesone or more measurements from each of the selected data sources from thedigital twin I/O system 15504. Next, At 161210, digital twin dynamicmodel system 15508 runs the dynamic model(s), using the retrievedvibration sensor measurements as inputs, and calculates one or moreoutputs that represent bearing vibration fault level states. Next, At161212, the digital twin dynamic model system 15508 updates one or morebearing fault level states of the manufacturing facility digital twin,machine digital twin, motor digital twin, and/or bearing digital twinsbased on the one or more outputs of the dynamic model(s). The clientapplication 15570 may obtain vibration fault level states of thebearings and may display the obtained vibration fault level stateassociated with each bearing and/or display colors associated with faultlevel severity (e.g., red for alarm, orange for critical, yellow forsuboptimal, green for normal operation) in the rendering of one or moreof the digital twins on a display interface.

In another example, a client application 15570 may be an augmentedreality application. In some embodiments of this example, the clientapplication 15570 may obtain vibration fault level states of bearings ina field of view of an AR-enabled device (e.g., smart glasses) hostingthe client application from the digital twin of the industrialenvironment (e.g., via an API of the digital twin system 15500) and maydisplay the obtained vibration fault level states on the display of theAR-enabled device, such that the vibration fault level state displayedcorresponds to the location in the field of view of the AR-enableddevice. In this way, a vibration fault level state may be displayed evenif there are no vibration sensors located within the field of view ofthe AR-enabled device.

FIG. 155 illustrates example embodiments of a method for updating a setof vibration severity unit values of bearings in a digital twin of amachine. Vibration severity units may be measured as displacement,velocity, and acceleration.

In this example, client application 15570 that interfaces with thedigital twin dynamic model system 15508 may be configured to provide avisualization of the three-dimensional vibration characteristics ofbearings in a digital twin of a machine.

In this example, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to update the vibration severityunit values for bearings in the digital twin of a machine. At 155300,digital twin dynamic model system 15508 receives a request from clientapplication 15570 to update one or more vibration severity unit value(s)of the manufacturing facility digital twin. Next, in step 155302,digital twin dynamic model system 15508 determines the one or moredigital twins required to fulfill the request and retrieves the one ormore required digital twins from digital twin datastore 15516. In thisexample, the digital twin dynamic model system 15508 may retrieve thedigital twin of the machine and any embedded digital twins (e.g.,digital twins of bearings and other components). At 155304, digital twindynamic model system 15508 determines one or more dynamic modelsrequired to fulfill the request and retrieves the one or more requireddynamic models from dynamic model datastore 155102. At 155306, thedigital twin dynamic model system 15508 selects dynamic model input datasources (e.g., one or more sensors from sensor system 15530, data fromInternet of Things connected devices 15524, and any other suitable data)via digital twin I/O system 15504 based on available data sources (e.g.,available sensors from a set of sensors in sensor system 15530) and theone or more required inputs of the dynamic model(s). In the presentexample, the retrieved dynamic models may be configured to take one ormore vibration sensor measurements as inputs and provide severity unitvalues for bearings in the machine. At 155308, digital twin dynamicmodel system 15508 retrieves one or more measurements from each of theselected sensors. In the present example, the digital twin dynamic modelsystem 15508 retrieves measurements from vibration sensors 15536 viadigital twin I/O system 15504. At 155310, digital twin dynamic modelsystem 15508 runs the dynamic model(s) using the retrieved vibrationmeasurements as inputs and calculates one or more output values thatrepresent vibration severity unit values for bearings in the machine.Next, at 155312, the digital twin dynamic model system 15508 updates oneor more vibration severity unit values of the bearings in the machinedigital twin and all other embedded digital twins or digital twins thatembed the machine digital twin based on the one or more values output bythe dynamic model(s).

FIG. 163 illustrates example embodiments of a method for updating a setof probability of failure values for machine components in the digitaltwin of a machine.

In this example, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to update the probability offailure values for components in a machine digital twin. At 163400,digital twin dynamic model system 15508 receives a request from clientapplication 15570 to update one or more probability of failure value(s)of the machine digital twin, any embedded component digital twins, andany digital twins that embed the machine digital twin such as amanufacturing facility digital twin. Next, in step 163402, digital twindynamic model system 15508 determines the one or more digital twinsrequired to fulfill the request and retrieves the one or more requireddigital twins. In this example, the digital twin dynamic model system15508 may retrieve the digital twin of the manufacturing facility, thedigital twin of the machine, and the digital twins of machine componentsfrom digital twin datastore 15516. At 163404, digital twin dynamic modelsystem 15508 determines one or more dynamic models required to fulfillthe request and retrieves the one or more required dynamic models fromdynamic model datastore 155102. At 163406, the digital twin dynamicmodel system 15508 selects, via digital twin I/O system 15504, dynamicmodel input data sources (e.g., one or more sensors from sensor system15530, data from Internet of Things connected devices 15524, and anyother suitable data) based on available data sources (e.g., availablesensors from a set of sensors in sensor system 15530) and the and theone or more required inputs of the dynamic model(s). In the presentexample, the retrieved dynamic models may take one or more vibrationmeasurements from vibration sensors 15536 and historical failure data asdynamic model inputs and output probability of failure values for themachine components in the digital twin of the machine. At 163408,digital twin dynamic model system 15508 retrieves data from each of theselected sensors and/or Internet of Things connected devices via digitaltwin I/O system 15504. At 163410, digital twin dynamic model system15508 runs the dynamic model(s) using the retrieved vibration data andhistorical failure data as inputs and calculates one or more outputsthat represent probability of failure values for bearings in the machinedigital twin. Next, At 163412, the digital twin dynamic model system15508 updates one or more probability of failure values of the bearingsin the machine digital twin, all embedded digital twins, and all digitaltwins that embed the machine digital twin based on the output of thedynamic model(s).

FIG. 164 illustrates example embodiments of a method for updating a setof probability of downtime for machines in the digital twin of amanufacturing facility.

In this example, client application 15570, which interfaces with thedigital twin dynamic model system 15508, may be configured to provide avisualization of the probability of downtime values of a manufacturingfacility in the digital twin of the manufacturing facility.

In this example, the digital twin dynamic model system 15508 may receiverequests from client application 15570 to assign probability of downtimevalues to machines in a manufacturing facility digital twin. At 164500,digital twin dynamic model system 15508 receives a request from clientapplication 15570 to update one or more probability of downtime valuesof machines in the manufacturing facility digital twin and any embeddeddigital twins such as the individual machine digital twins. Next, instep 164502, digital twin dynamic model system 15508 determines the oneor more digital twins required to fulfill the request and retrieves theone or more required digital twins from digital twin datastore 15516. Inthis example, the digital twin dynamic model system 15508 may retrievethe digital twin of the manufacturing facility and any embedded digitaltwins from digital twin datastore 15516. At 164504, digital twin dynamicmodel system 15508 determines one or more dynamic models required tofulfill the request and retrieves the one or more required dynamicmodels from dynamic model datastore 155102. At 164506, the digital twindynamic model system 15508 selects dynamic model input data sources(e.g., one or more sensors from sensor system 15530, data from Internetof Things connected devices 15524, and any other suitable data) based onavailable data sources (e.g., available sensors from a set of sensors insensor system 15530) and the and the one or more required inputs of thedynamic model(s) via digital twin I/O system 15504. In the presentexample, the dynamic model(s) may be configured to take vibrationmeasurements from vibration sensors and historical downtime data asinputs and output probability of downtime values for different machinesthroughout the manufacturing facility. At 164508, digital twin dynamicmodel system 15508 retrieves one or more measurements from each of theselected sensors via digital twin I/O system 15504. At 164510, digitaltwin dynamic model system 15508 runs the dynamic model(s) using theretrieved vibration measurements and historical downtime data as inputsand calculates one or more outputs that represent probability ofdowntime values for machines in the manufacturing facility. Next, At164512, the digital twin dynamic model system 15508 updates one or moreprobability of downtime values for machines in the manufacturingfacility digital twins and all embedded digital twins based on the oneor more outputs of the dynamic models.

FIG. 165 illustrates example embodiments of a method for updating one ormore probability of shutdown values in the digital twin of an enterprisehaving a set of manufacturing facilities.

In the present example, the digital twin dynamic model system 15508 mayreceive requests from client application 15570 to update the probabilityof shutdown values for the set of manufacturing facilities within anenterprise digital twin. At 165600, digital twin dynamic model system15508 receives a request from client application 15570 to update one ormore probability of shutdown values of the enterprise digital twin andany embedded digital twins. Next, in step 165602, digital twin dynamicmodel system 15508 determines the one or more digital twins required tofulfill the request and retrieves the one or more required digital twinsfrom digital twin datastore 15516. In this example, the digital twindynamic model system 15508 may retrieve the digital twin of theenterprise and any embedded digital twins. At 165604, digital twindynamic model system 15508 determines one or more dynamic modelsrequired to fulfill the request and retrieves the one or more requireddynamic models from dynamic model datastore 155102. At 165606, thedigital twin dynamic model system 15508 selects dynamic model input datasources (e.g., one or more sensors from sensor system 15530, data fromInternet of Things connected devices 15524, and any other suitable data)based on available data sources (e.g., available sensors from a set ofsensors in sensor system 15530) and the and the one or more requiredinputs of the dynamic model(s) via digital twin I/O system 15504. In thepresent example, the retrieved dynamic models may be configured to takeone or more vibration measurements from vibration sensors 15536 and/orother suitable data as inputs and output probability of shutdown valuesfor each manufacturing entity in the enterprise digital twin. At 165608,digital twin dynamic model system 15508 retrieves one or more vibrationmeasurements from each of the selected vibration sensors 15536 fromdigital twin I/O system 15504. At 165610, digital twin dynamic modelsystem 15508 runs the dynamic model(s) using the retrieved vibrationmeasurements and historical shut down data as inputs and calculates oneor more outputs that represent probability of shutdown values formanufacturing facilities within the enterprise digital twin. Next, At165612, the digital twin dynamic model system 15508 updates one or moreprobability of shutdown values of the enterprise digital twin and allembedded digital twins based on the one or more outputs of the dynamicmodel(s).

FIG. 159 illustrates example embodiments of a method for updating a setof cost of downtime values in machines in the digital twin of amanufacturing facility. In embodiments, the manufacturing

In the present example, the digital twin dynamic model system 15508 mayreceive requests from a client application 15570 to populate real-timecost of downtime values associated with machines in a manufacturingfacility digital twin. At 159700, digital twin dynamic model system15508 receives a request from the client application 15570 to update oneor more cost of downtime values of the manufacturing facility digitaltwin and any embedded digital twins (e.g., machines, machine parts, andthe like) from the client application 15570. Next, in step 159702, thedigital twin dynamic model system 15508 determines the one or moredigital twins required to fulfill the request and retrieves the one ormore required digital twins. In this example, the digital twin dynamicmodel system 15508 may retrieve the digital twins of the manufacturingfacility, the machines, the machine parts, and any other embeddeddigital twins from digital twin datastore 15516. At 159704, digital twindynamic model system 15508 determines one or more dynamic modelsrequired to fulfill the request and retrieves the one or more requireddynamic models from dynamic model datastore 155102. At 159706, thedigital twin dynamic model system 15508 selects dynamic model input datasources (e.g., one or more sensors from sensor system 15530, data fromInternet of Things connected devices 15524, and any other suitable data)based on available data sources (e.g., available sensors from a set ofsensors in sensor system 15530) and the and the one or more requiredinputs of the dynamic model(s) via digital twin I/O system 15504. In thepresent example, the retrieved dynamic model(s) may be configured totake historical downtime data and operational data as inputs and outputdata representing cost of downtime per day for machines in themanufacturing facility. At 159708, digital twin dynamic model system15508 retrieves historical downtime data and operational data fromdigital twin I/O system 15504. At 159710, digital twin dynamic modelsystem 15508 runs the dynamic model(s) using the retrieved data as inputand calculates one or more outputs that represent cost of downtime perday for machines in the manufacturing facility. Next, at 159712, thedigital twin dynamic model system 15508 updates one or more cost ofdowntime values of the manufacturing facility digital twins and machinedigital twins based on the one or more outputs of the dynamic model(s).

FIG. 160 illustrates example embodiments of a method for updating a setof manufacturing KPI values in the digital twin of a manufacturingfacility. In embodiments, the manufacturing KPI is selected from the setof uptime, capacity utilization, on standard operating efficiency,overall operating efficiency, overall equipment effectiveness, machinedowntime, unscheduled downtime, machine set up time, inventory turns,inventory accuracy, quality (e.g., percent defective), first pass yield,rework, scrap, failed audits, on-time delivery, customer returns,training hours, employee turnover, reportable health & safety incidents,revenue per employee, and profit per employee, schedule attainment,total cycle time, throughput, changeover time, yield, plannedmaintenance percentage, availability, and customer return rate.

In the present example, the digital twin dynamic model system 15508 mayreceive requests from a client application 15570 to populate real-timemanufacturing KPI values in a manufacturing facility digital twin. At159700, digital twin dynamic model system 15508 receives a request fromthe client application 15570 to update one or more KPI values of themanufacturing facility digital twin and any embedded digital twins(e.g., machines, machine parts, and the like) from the clientapplication 15570. Next, in step 159702, the digital twin dynamic modelsystem 15508 determines the one or more digital twins required tofulfill the request and retrieves the one or more required digitaltwins. In this example, the digital twin dynamic model system 15508 mayretrieve the digital twins of the manufacturing facility, the machines,the machine parts, and any other embedded digital twins from digitaltwin datastore 15516. At 159704, digital twin dynamic model system 15508determines one or more dynamic models required to fulfill the requestand retrieves the one or more required dynamic models from dynamic modeldatastore 155102. At 159706, the digital twin dynamic model system 15508selects dynamic model input data sources (e.g., one or more sensors fromsensor system 15530, data from Internet of Things connected devices15524, and any other suitable data) based on available data sources(e.g., available sensors from a set of sensors in sensor system 15530)and the and the one or more required inputs of the dynamic model(s) viadigital twin I/O system 15504. In the present example, the retrieveddynamic model(s) may be configured to take one or more vibrationmeasurements obtained from vibration sensors 15536 and other operationaldata as inputs and output one or more manufacturing KPIs for thefacility. At 167708, digital twin dynamic model system 15508 retrievesone or more vibration measurements from each of the selected vibrationsensors 15536 and operational data from digital twin I/O system 15504.At 159710, digital twin dynamic model system 15508 runs the dynamicmodel(s) using the retrieved vibration measurements and operational dataas inputs and calculates one or more outputs that representmanufacturing KPIs for the manufacturing facility. Next, At 159712, thedigital twin dynamic model system 15508 updates one or more KPI valuesof the manufacturing facility digital twins, machine digital twins,machine part digital twins, and all other embedded digital twins basedon the one or more outputs of the dynamic model(s).

With the proliferation of vibration sensors and other IndustrialInternet of Things (IIoT) sensors, there are vast amounts of dataavailable relating to industrial environments. This data is useful inpredicting the need for maintenance and for classifying potential issuesin the industrial environments. There are, however, many unexplored usesfor vibration sensor data and other IIoT sensor data that can improvethe operation and uptime of the industrial environments and provideindustrial entities with agility in responding to problems before theproblems become catastrophic.

Industrial enterprises that rely on industrial experts struggle tocapture the knowledge of these experts when they move on to anotherenterprise or leave the workforce. There exists a need in the art tocapture industrial expertise and to use the captured industrialexpertise in guiding newer workers or mobile electronic industrialentities to perform industrial-related tasks.

A knowledge distribution process and related technologies now will bedescribed more fully hereinafter with reference to the accompanyingdrawings, in which illustrative embodiments are shown. The knowledgedistribution process and technologies may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the disclosure and inventions to those skilled inthe art. The knowledge distribution process may use a knowledgedistribution platform or system that utilizes blockchain technology forstoring digital knowledge and providing convenient and secure control ofthe digital knowledge.

Where digital knowledge may be cryptographically secured, there can be anumber of practical obstacles to the sharing of knowledge, such as theabsence of trust between parties that could potentially benefit fromsharing of the knowledge. For example, a manufacturer might benefit fromhaving a supplier access trade secrets of the manufacturer in order tomake components or materials on behalf of the manufacturer, but sharingthe trade secrets creates a risk that the supplier may use the tradesecrets on its own behalf or on behalf of competitors. Similarly, anengineer may be willing to share valuable code or instruction sets withothers but be fearful of the misuse of that code. A need exists for adigital knowledge distribution system that facilitates orchestration ofthe sharing of knowledge by providing a high degree of control over theextent to which counterparties can access shared knowledge.

Even where knowledge is secure and well-controlled, some types ofknowledge are so sensitive that an owner may be unwilling to share theentire set of knowledge with a single counterparty. For example, aproprietary process may be divided among different suppliers in order tokeep any one supplier from deducing or reverse engineering the entireprocess. However, dividing knowledge presents operational challenges, asthe owner may orchestrate a series of secure interactions with allinvolved parties in order to assure that the full set of knowledge maybe maintained and accurately implemented. A need exists for a digitalknowledge distribution system that facilitates handling and control ofsubsets of knowledge, including automated handling of aggregation ofknowledge, or related outputs, that result from division of knowledgesubsets.

Referring to FIG. 168 , a knowledge distribution system 16802 isconfigured to facilitate management of digital knowledge 16804 by one ormore users via a distributed ledger 16808. The digital knowledge 16804may include any suitable knowledge that is conveyable from one party toanother, such as in a digital format. Users and/or parties may includeone or more knowledge providers 16806 and/or one or more knowledgerecipients 16818. The knowledge providers 16806 are parties that provideknowledge to be managed via the knowledge distribution system 16802,such as by uploading one or more instances of the digital knowledge16804 to the knowledge distribution system 16802 and/or the distributedledger 16808. Uploading one or more instances of the digital knowledge16804 and/or hosting one or more instances of the digital knowledge16804 on the distributed ledger 16808 may include uploading an instanceof the digital knowledge 16804 itself to the distributed ledger 16808(e.g., which may be tokenized, contained in the smart contract, and/orstored in an associated database) and/or providing a reference to anaccessible location of the instance of the digital knowledge 16804 andany other information required to retrieve the digital knowledge fromthe accessible location. When reference is made to receiving digitalknowledge 16804, the digital knowledge 16804 itself or a referencethereto may be received. The term knowledge recipients 16818 may referto parties that receive knowledge from the knowledge providers 16806 viathe knowledge distribution system 16802 (including via reference or linkto digital knowledge 16804) and/or via a distributed ledger 16808 thatstores digital knowledge 16804. In some embodiments, the knowledgedistribution system 16802 may facilitate management of digital knowledge16804 by facilitating establishment of a chain of work, possession,and/or title of one or more instances of digital knowledge, such as byserving as a log of ownership of an instance of knowledge. The log ofownership may include a chain of log entries including an indication ofa set of owners and/or contributors. For example, in some embodimentsthe knowledge distribution system 16802 may facilitate establishment ofa chain of work, a chain of possession, and/or a chain of titlecorresponding to a 3D-printing instruction set for 3D printing an object(e.g., a custom-designed part, a replacement part, a toy, a medicaldevice, a tool, or the like). In some embodiments, the knowledgedistribution system 16802 may facilitate establishment of a sellerdatabase where the schematic may be stored prior to one or more of sale,transfer of the schematic to a buyer database, entry of a serial numberof the custom part into a clause of a smart contract 16840, and printingof the custom part by a 3D printer owned by a buyer.

In some embodiments the knowledge distribution system 16802 mayfacilitate management of digital knowledge 16804 by managing aggregationof instances of digital knowledge 16804, such as where componentinstances of digital knowledge 16804 are aggregated to form largerinstances of digital knowledge (e.g., where chapter instances areconcatenated to form book instances, where component instances arelinked schematically to form a system, where element instances arelinked diagrammatically to generate a workflow, where partial instancesare coupled to form a whole instance (e.g., the necessary parts of aformula), where related instances are topically linked to form acluster, and via many other forms of aggregation).

In some embodiments, the knowledge distribution system 16802 mayfacilitate management of digital knowledge 16804 by facilitatingverification of one or more sources of the digital knowledge and/orproviding a chain of origination for a digital or physical item byvirtue of related knowledge. For example, in embodiments, the knowledgedistribution system 16802 may log a digital signature of a steelmanufacturer in a distributed ledger that certifies a quality grade ofsteel provided by the steel manufacturer to a factory owner and may linkthe digital signature to serial numbers of each part produced by afactory owned by the factory owner that used the steel provided by thesteel manufacturer.

In some embodiments, the knowledge distribution system 16802 mayfacilitate control of digital knowledge 16804 by facilitatingcollaboration of a plurality of knowledge providers 16806 such thatinformation related to one or more instances of digital knowledge fromone or more of disparate parties, disparate knowledge providers 16806,and disparate distributed ledgers 16808 may be tracked and/or combinedinto one or more consolidated distributed ledgers.

In some embodiments, instances of the digital knowledge 16804 mayinclude, for example, instruction sets such as process steps and othermethodologies in food production, transportation, executable algorithmiclogic such as computer programs, a firmware program, an instructions setfor a field-programmable gate array (FPGA), serverless code logic, acrystal fabrication system/process, a polymer production process, achemical synthesis process, a biological production process, partschematics, and/or production records (e.g., production records ofaircraft parts, spaceship parts, nuclear engine parts, etc.), a processand/or instruction set for semiconductor fabrication such as siliconetching and/or doping, an instruction set for a 3D printer such as forprinting a medical device, an automobile part, an airplane part, a pieceof furniture or component thereof, a replacement part for an industrialrobot or machine, algorithmic logic such as an instruction set for usein an application, AI logic and/or definitions, machine learning logicand/or definitions, cryptography logic, serverless code logic, tradesecrets and/or other intellectual property such as know-how, patentedmaterial, and works of authorship, food preparation instructions (e.g.,for industrial food preparation), coating process instructions,biological production process instructions, chemical synthesisinstructions, polymer production instructions, smart contractinstructions, data sets and/or sensor information defining and/orpopulating a set of digital twins (such as digital twins that embodydigital knowledge about one or more physical entities, includingknowledge about configurations, operating modes, instruction sets,capabilities, defects, performance parameters, and many others), and/orany other suitable type of digitally transmittable knowledge. In theseembodiments, the instruction sets may be consumed/leveraged by acomputing device, a special purpose device, or a combination of devices(e.g., factory equipment). In some embodiments, instances of the digitalknowledge 16804 may include personal and/or professional knowledgerelating to one or more organizations and/or individuals, such as aprofessional resume and/or professional history tracking information. Insome embodiments, the personal and/or professional knowledge may includeone or more records of professional credentials such as academic degreesand/or certificates. In some embodiments, the personal and/orprofessional knowledge may include one or more verifications ofprofessional positions held by the one or more individuals. In someembodiments, the personal and/or professional knowledge may includeprofessional feedback for and/or verification of work performed forand/or by one or more third parties. The personal and/or professionalknowledge may include personal and/or business financial history,personal life achievements as verified by one or more third parties. Theknowledge provider 16806 may be any party that at least partiallyprovides one or more instances of the digital knowledge 16804, such as amanufacturer, a seller, a customer, a wholesaler, a user, a manager, anotary, a factory owner, a maintenance worker, or any other suitableprovider of the digital knowledge 16804.

In embodiments, the distributed ledger 16808 may be any suitable type ofelectronic ledger 16808, such as a blockchain (e.g., Hyperledger,Solidity, Ethereum, and the like). The distributed ledger 16808 may becentralized, decentralized, or a hybrid configuration where theknowledge distribution system 16802 stores a copy of a distributedledger 16808 in addition to any number of participant nodes 16916 thatstore copies of the distributed ledger 16808. When referring to thedistributed ledger 16808, the term “distributed ledger” (and/or anylogs, records, smart contracts, blocks, tokens, and/or data storedthereon) may refer to a specific instance of a copy of the distributedledger 16808 (and/or any logs, records, smart contracts, blocks, tokens,and/or data stored thereon) and/or the collection of local copies of thedistributed ledger 16808-L stored across any number of nodes (which mayinclude the knowledge distribution system 16802), unless specificallyindicated otherwise.

In some embodiments, a private network of authorized participants, suchas one or more of the knowledge providers and/or nodes, may establishcryptography-based consensus on one or more items, such that theknowledge distribution system 16802 may provide security, transparency,auditability, immutability, and non-repudiation to transactions fordigital knowledge. In some embodiments, a trusted authority (e.g., theknowledge distribution system 16802 or another suitable authority) mayissue private key and public key pairs to each registered user of theknowledge distribution system 16802. The private key and public keypairs may be used to encrypt and decrypt data (e.g., messages, files,documents, etc.) and/or to perform operations with respect to thedistributed ledger 16808. In some embodiments, the knowledgedistribution system 16802 (or another trusted authority) may provide twoor more levels of access to users. In some embodiments, the knowledgedistribution system 16802 may define one or more classes of users, whereeach of the classes of users is granted a respective level of access. Insome of these embodiments, the knowledge distribution system 16802 mayissue one or more access keys to one or more classes of users, where theone or more access keys each correspond to a respective level of access,thereby providing users of different levels of access via theirrespective issued access keys. In embodiments, possession of certainaccess keys may be used to determine a level of access to thedistributed ledger 16808. For example, in some embodiments, a firstclass of users may be granted full viewing access of a block, while asecond class of users may be granted both viewing access of blocks andan ability to verify and/or certify one or more instances of digitalknowledge contained within a block, and while a third class of users maybe granted viewing access of blocks, an ability to verify and/or certifyone or more instances of digital knowledge contained within a block, andan ability to modify the one or more instances of digital knowledgecontained within the block. In some embodiments, a class of users may beverified as being a legitimate user of the distributed ledger 16808 inone or more roles and allowed related permissions with respect to thedistributed ledger and content stored therein. A user may be verified,for example, as a bona fide knowledge provider 16806 that uses aknowledge provider device 16890, knowledge recipient 16818 that uses aknowledge recipient device 16894, and/or crowdsourcer 16836 that uses acrowdsourcer device 16892. There may be any number of each device 16890,16892, 16894. As shown in FIG. 168 , there is one knowledge providerdevice 16890, two crowdsourcer devices 16892, and one knowledgerecipient device 16894. In other examples, as it is understood, theremay be one, two, three, or more of any device type 16890, 16892, 16894,in any combination. In other examples, there may be one of each devicetype (e.g., one knowledge provider device 16890, one crowdsourcer device16892, and one knowledge recipient device 16894). In other embodiments,these devices 16890, 16892, 16894 may be implemented as one or morecomputing devices and/or server devices (e.g., as part of a serverfarm).

In some embodiments, the knowledge distribution system 16802 may includea ledger management system 16910. In some embodiments, the ledgermanagement system 16910 manages one or more distributed ledgers (alsoreferred to as “ledgers”). In some embodiments, the ledger managementsystem 16910 may instantiate a distributed ledger for a particularknowledge provider 16806 or group of knowledge providers 16806, such asby instantiating a distributed ledger 16808 that stores instances ofdigital knowledge 16804 provided by the knowledge provider 16806 orgroup of knowledge providers 16806. The knowledge distribution system16802 may allow only the particular knowledge provider 16806 orparticular group of knowledge providers 16806 to host instances ofdigital knowledge 16804 (e.g., by using knowledge provider device 16890)on the related distributed ledger 16808 and/or for each instance ofdigital knowledge 16804, such that each distributed ledger 16808 isspecific to a respective knowledge provider 16806 and/or an instance ofdigital knowledge 16804. In some embodiments, the ledger managementsystem 16910 may instantiate a plurality of distributed ledgers 16808,one or more of the distributed ledgers 16808 being configured tofacilitate hosting, sharing, buying, selling, licensing, or otherwisemanaging a category of digital knowledge 16804. Categories of digitalknowledge may be related to, for example, one or more industries such asautomotive and/or financial, or one or more types of digital knowledge,such as 3D printing schematics. In some embodiments, the ledgermanagement system 16910 may maintain a distributed ledger thatfacilitates management of some or all of the instances of digitalknowledge 16808 and/or the knowledge providers 16806 for which relateddata is stored by the knowledge distribution system 16802.

In some embodiments, a distributed ledger 16808 is any suitable type ofblockchain. Any other suitable types of distributed ledgers may be used,however, without departing from the scope of the disclosure. Thedistributed ledger may be public or private. In embodiments, where thedistributed ledger is private, reading from the ledger and/or validationprivileges by a user such as the knowledge provider 16806 (e.g., usingknowledge provider device 16890) may be restricted to invitees, userswith one or more accounts/passwords, or by any other suitable method ofrestricting access to the distributed ledger 16808. In some embodiments,the distributed ledger 16808 may be at least partially centralized, suchthat a plurality of nodes of the distributed ledger is stored by theknowledge distribution system 16802. In some embodiments, thedistributed ledgers are federated distributed ledgers, as thedistributed ledgers may be stored on pre-selected or pre-approved nodesthat are associated with the parties to a management of digitalknowledge 16804 via the knowledge distribution system 16802. Thetechniques described herein may be applied, however, to publiclydistributed ledgers as well. In a publicly distributed ledger, anysuitably configured computing device (personal computers, user devices,servers) or set of devices (e.g., a server farm) may act as a node 16916and may store a local copy of a distributed ledger 16808-L, whether theowner of the node otherwise participates in the transactions facilitatedby the knowledge distribution system 16802. In these embodiments, suchnodes 16916 may add validate/deny new blocks, save new blocks to thedistributed ledger 16808 (if validated) to maintain a full copy (ornearly full copy) of the transaction history relating to the distributedledger 16808, and broadcast the transaction history to otherparticipating nodes 16916.

In some embodiments, the ledger management system 16910 (and/or thecollection of participant nodes 16916) may be configured to leverage adistributed ledger 16808 to create an immutable log establishing of achain of work, possession, and/or title of one or more instances ofdigital knowledge 16804, establishing verification or one or moresources of the digital knowledge 16804, and/or facilitatingcollaboration of a plurality of knowledge providers 16806. In someembodiments, the ledger management system 16804 establishingverification of 16810 may utilize a distributed ledger to manage a setof permission keys that provide access to one or more instances of thedigital knowledge 16804 and/or services associated with the knowledgedistribution system 16802. In some embodiments, the distributed ledger16808 provides provable access to the digital knowledge 16804, such asby one or more cryptographic proofs and/or techniques. In someembodiments, the distributed ledger 16808 may provide provable access tothe digital knowledge 16804 by one or more zero-knowledge prooftechniques. In some embodiments, the ledger management system 16910 maymanage the distributed ledger to facilitate cooperation and/orcollaboration between two or more knowledge providers 16806 with regardto one or more instances of digital knowledge 16804.

FIG. 169 illustrates an exemplary embodiment of the distributed ledger16808, the distributed ledger 16808 being distributed over a ledgernetwork 16970. The ledger network 16970 may include the distributedledger 16808 and a set of node computing devices 16916-1, 1691602,1691603, 16916-N that communicate via one or more communication networks16814. In some embodiments, the communication network 16814 may includethe Internet, private networks, cellular networks, and/or the like. Inembodiments, the nodes 16916 may all host a copy of the distributedledger 16808 (or a portion thereof). For example, the ledger network16970 may include a first node 16916-1, a second node 16916-2, a thirdnode 16916-3 . . . and an Nth node 16916-N that communicate with theknowledge distribution system 16802 and with other nodes 16916 in theledger network 16970. In some embodiments, the knowledge distributionsystem 16802 is configured to execute the ledger management system 16910and may store and manage a local copy of a distributed ledger 16808 thatis used in connection with facilitating management of one or moreinstances of the digital knowledge 16804 via the knowledge distributionsystem 16802. In some embodiments, the knowledge distribution system16802 (or the ledger management system 16910 executed thereon) may alsobe thought of and referred to as a node of the ledger network 16970. Insome embodiments, the ledger management system 16910 may also generateand assign private key and public key pairs to users such as one or moreof the knowledge providers 16806 and/or one or more knowledge recipients16818 of the digital knowledge 16804 (also referred to as “knowledgerecipients”) and/or to each node 16916 in the ledger network 16970, suchthat the private key and public key pairs are used to encrypt datatransmitted between nodes 16916 in the ledger network 16970.

In some embodiments, each of the nodes 16916 of the ledger network 16970(other than the knowledge distribution system 16802) may be a computingdevice or a set of connected computing devices that are associated withthe knowledge providers 16806 and/or knowledge recipients 16818. In someembodiments, the nodes 16916 may include computing devices of partiesthat are not involved in the providing or receipt of knowledge (e.g.,parties that are associated with neither the knowledge providers 16806nor any of the knowledge recipients 16818). In some embodiments, each ofthe nodes 16916 may store a respective local copy 16808-L of thedistributed ledger 16808. In some embodiments, one or more nodes maystore a partial copy of the distributed ledger 16808. In someembodiments, each of the nodes 16916, 16916-1, 16916-2, 16916-3, 16916-Nmay execute a respective agent 16920, 16920-1, 16920-2, 16920-3,16920-N. An agent 16920 may be configured to perform one or more ofmanaging the local copy 16808-L of the distributed ledger 16808associated with the node 16916 that executed the agent 16920, helpingverify blocks that were previously stored on the distributed ledger16808, helping verify requests from other nodes 16916 to store newblocks on the distributed ledger 16808, requesting permission to performoperations relating to the digital knowledge or management thereof onbehalf of a user associated with the node 16916 on which the agentresides, and/or facilitating collaboration between one or more of theknowledge providers 16806 and/or one or more of the knowledge recipients16818 (e.g., using knowledge provider device(s) 16890 and/or knowledgerecipient device(s) 16894, respectively), such as by assisting withvalidation and/or transfer of one or more instances of the digitalknowledge 16804 and/or executing one or more clauses of one or moresmart contracts 16840. It is understood that nodes may performadditional or alternative tasks without departing from the scope of thedisclosure.

In some embodiments, a knowledge recipient 16818 may receive one or moreinstances of digital knowledge 16804 via the knowledge distributionsystem 16802 via one or more knowledge recipient devices 16894. Inembodiments, a knowledge recipient device 16894 may be any device thatis configured to receive and/or use the digital knowledge 16804 from thedistributed ledger 16808, such as a computing device, and/or may bedevices for using the digital knowledge 16804, such as a 3D printer, amanufacturing device or system, and the like. In some scenarios,knowledge recipient 16818 may employ a plurality of knowledge recipientdevices 16894, such as a server or computing device configured todownload one or more instances of digital knowledge 16804 from thedistributed ledger 16808 and transmit the one or more instances ofdigital knowledge to a 3D printer, a factory machine, a manufacturingsystem, or some other suitable device for using the one or moreinstances of the digital knowledge 16804. For example, a knowledgeprovider 16806 may upload a link (e.g., using a knowledge providerdevice 16890) of a computer-aided design (CAD) file of a 3D printableairplane part to the distributed ledger 16808. In embodiments, theknowledge provider 16806 may use e.g., the knowledge provider device16890 to define or otherwise provide a smart contract that governs theuse of the digital knowledge (e.g., the design file for the airplanepart), including a cost of a use of the CAD file of the airplane part. Aknowledge recipient 16818 may transfer funds (e.g., using knowledgerecipient device 16894) to the knowledge provider 16806 (e.g., knowledgeprovider device 16890) (e.g., via the smart contract) in exchange foraccess to the CAD file via the distributed ledger 16808. A knowledgerecipient device 16894 may then download the CAD file, which may then beused to 3D print the part. For example, the knowledge recipient device16894 may be a business computer in communication with a 3D printer or asmart 3D printer itself. In the former scenario, the business computermay transfer the CAD file to the 3D printer. Upon receiving the CADfile, the 3D printer may 3D print the airplane part. In someembodiments, the digital knowledge itself (e.g., the CAD file) may becontained in the smart contract, such that the smart contract providesthe digital knowledge to the knowledge recipient device 16894 uponverifying that the knowledge recipient 16818 has satisfied theconditions of release of the digital knowledge 16804 (e.g., deposited arequisite amount of currency). In some embodiments, each time that aninstance of knowledge is used by a knowledge recipient 16818, a smartcontract, the knowledge distribution system 16802, an agent 16920,and/or the knowledge recipient device 16894 may update the distributedledger 16808 with a block indicating that the knowledge recipient usedthe instance of digital knowledge 16804.

In some embodiments, the knowledge distribution system 16802 may beconfigured to facilitate participation in management of digitalknowledge 16804 by one or more crowdsourcers 16836, such as by allowinga crowdsourcer 16836 to verify one or more aspects of an instance ofdigital knowledge 16804 (e.g., using crowdsourcer device 16892). Inembodiments, a crowdsourcer 16836 may be granted crowdsourcingpermissions, thereby allowing the crowdsourcer 16836 to view/inspect thedigital knowledge and to provide a verification vote 16926 and/oropinion. In embodiments, non-limiting examples of crowdsourcingpermissions may include one or more of reviewing an instance of digitalknowledge 16804, signing an instance of digital knowledge 16804,verifying an instance of digital knowledge 16804, and the like. Examplesof crowdsourcers 16836 include certifying entities, domain experts,customers, manufacturers, wholesalers, and any other suitable partycapable of verifying an instance of digital knowledge. In embodiments,certifying entities or domain experts may certify an instance of digitalknowledge 16804 as being authentic, accurate, and/or reliable, and/or ascoming from an authentic, accurate, and/or reliable source. Inembodiments, customers may review an instance of digital knowledge16804, such as to indicate that the digital knowledge 16804 is inworking order and/or of expected quality. In embodiments, manufacturersand/or wholesalers may sign an instance of digital knowledge 16804, suchas by applying a serial number to a piece of digital knowledge 16804before the piece of digital knowledge is transmittable to a knowledgerecipient 16818 (e.g., via knowledge recipient device 16894).Certifications, reviews, signatures, and/or any other validation indiciamade by crowdsourcers 16836 may be recorded in the distributed ledger16808, such as by adding one or more new blocks 16922 to the distributedledger 16808 that indicate the certification, review, signature, orother validation indicia. In some embodiments, the new blocks 16922 mayinclude data related to the certifications, reviews, signatures, and/orother validation indicia made by the one or more crowdsourcers 16836(e.g., an identifier of the crowdsources, a timestamp, a location,and/or the like), e.g., using crowdsourcer devices 16892. In someexamples, the knowledge distribution system 16802 may be paired with acrowdsourcing system (e.g., crowdsourcer devices 16892). Specifically,in examples, the crowdsourcing system (e.g., crowdsourcer devices 16892)may communicate with and engage with the smart contract 16840 such thatupon crowdsourcing an element of the digital knowledge 16804 via thesmart contract 16840, the digital knowledge 16804 may be embodied (e.g.,recorded) in the distributed ledger 16808. The knowledge distributionsystem 16802 may use the smart contract 16840 to facilitate managementof the digital knowledge 16804, such as by allowing the smart contract16840 and crowdsourcers 16836 to verify (and/or contribute to) one ormore aspects of an instance of digital knowledge 16804. For example, asoftware developer may provide a crowdsource request for a module orfunction in the smart contract 16840. This crowdsource request may beembedded in open source code as a request for a code element (e.g.,where a first supplier of working code may get a share of proceeds (or acredit, or a token, etc.)) of a product. For this example, crowdsourcers16836 may use the crowdsourcing system (e.g., crowdsource devices 16892)to respond to the crowdsource request by viewing/inspecting the digitalknowledge (e.g., open source code) and may provide collaboration in theform of verifications, opinions, corrections, and/or contributions tothe open source code which may relate to improvements to the open sourcecode (e.g., improve accuracy and/or reliability of software). Theseverifications, opinions, corrections, and/or contributions indiciaprovided by the crowdsourcers 16836 may be recorded in the distributedledger 16808 by adding one or more new blocks 16922 to the distributedledger 16808 that indicate the indicia. The crowdsourcers 16836 may becompensated (e.g., via the smart contract 16840) based on theirpercentage of contribution to the open source code such that theoriginal software developer may share the proceeds (or credits, ortokens, etc.) of the software product with the crowdsources. Thepercentage contribution may be based on the amount of code writtenand/or the impact of each crowdsourcer's contribution on the resultingopen source code's functionality.

In some embodiments, the digital knowledge 16804 may be tokenized (e.g.,at least partially converted to/wrapped in a knowledge token 17038). Inembodiments, tokenizing the digital knowledge 16804 may include wrappingthe digital knowledge into a knowledge token 17038 and/or wrappingaccess, licensing, ownership, and/or other suitable rights related tothe digital knowledge 16804 such that the access, licensing, ownershipand/or other suitable rights managed by one or more of the knowledgetokens 17038. By tokenizing digital knowledge 16804, the digitalknowledge 16804 may reside in and be distributed via a distributedledger 16808 and smart contracts 16840. In some embodiments, theknowledge distribution system 16802 may define permissions and/oroperations associated with the knowledge tokens 17038. For example, theknowledge token 17038 may allow the tokenized digital knowledge 16804 tobe viewed, edited, copied, bought, sold, and/or licensed based onpermissions set at a time of tokenization by the knowledge distributionsystem 16802. In embodiments, the knowledge distribution system 16802may provide for orchestration of a marketplace or exchange for digitalknowledge 16804, such as where bodies or instances of digital knowledge16804 may be exchanged, such as, without limitation, through sets ofknowledge tokens 17038 that are optionally governed by smart contractsthat may be configured by a host of a knowledge exchange or marketplaceand/or by knowledge providers 16806 (e.g., using knowledge providerdevices 16890) or knowledge recipients 16818 (e.g., using knowledgerecipient devices 16894). For example, an exchange or marketplace mayhost exchanges for specific categories of know-how, expertise,instruction sets, trade secrets, insight, or other elements of knowledgedescribed or referenced herein, where knowledge is categorized bysubject matter of interest, where transaction terms are pre-definedand/or configurable (such as with configurable smart contracts thatenable various transaction models, including bid/ask models, auctionmodels, donation models, reverse auction models, fixed price models,variable price models, contingent pricing models and others), wheremetadata is collected and/or represented about categories of knowledgeexchange, and where relevant content is presented, including marketpricing data, substantive content about knowledge areas, content aboutproviders, and the like. Such an exchange may facilitate monetization oftokenized knowledge represented in knowledge tokens 17038. Inembodiments, a knowledge exchange, as described herein, may beintegrated with or within another exchange, such as a domain-specificexchange, a geography-specific exchange, or the like, where theknowledge exchange may facilitate exchange of valuable or sensitiveknowledge related to the subject matter of the other exchange. The otherexchange may be a stock exchange, a commodities exchange, a derivativesexchange, a futures exchange, an advertising exchange, an energyexchange, a renewable energy credits exchange, a cryptocurrencyexchange, a bonds exchange, a currency exchange, a precious metalsexchange, a petroleum exchange, an exchange for goods, an exchange forservices, or any of a wide variety of others. This may includeintegration by APIs, connectors, ports, brokers, and other interfaces,as well as integration by extraction, transformation, and loading (ETL)technologies, smart contracts, wrappers, containers, or othercapabilities.

In some embodiments, the knowledge distribution system 16802 may beconfigured to create and issue one or more currency tokens associatedwith the distributed ledger 16808. The currency tokens may be digitalobjects such as cryptographic tokens, cryptographic currency, and thelike, that may be purchased, mined, assigned, and/or distributed tousers of the distributed ledger 16808. In some embodiments, the currencytokens may represent fiat currency (e.g., US Dollars, British Pounds,Euros, or the like), such that the value of the token is pegged to thefiat currency. In embodiments, the currency tokens may be used totransact digital knowledge. For example, in embodiments, smart contractsmay be used to receive and verify that a knowledge recipient 16818 haspaid the requisite amount of funds before releasing the digitalknowledge 16804 to a knowledge recipient device 16894. Additionally oralternatively, knowledge recipients 16818 may use traditional paymentmethods (e.g., credit card payments) to transact for instances ofknowledge. In some embodiments, the currency tokens may function asdigital currency. For example, the currency tokens may be paid byknowledge recipients to knowledge providers in exchange for digitalknowledge 16804 and/or paid to crowdsources (e.g., certifiers orexperts) for verifying one or more aspects of digital knowledge 16804.In some embodiments, one or more users may be awarded currency tokens asa reward for discovering, or “mining,” one or more new blocks 16922 ofthe distributed ledger 16808. In some embodiments, currency tokens maybe asset-backed tokens, such as tokens backed by one or more othercurrencies (e.g., fiat currencies), securities, ownership rights ofproperty, ownership rights of intellectual property, licensing rights ofproperty and/or intellectual property, and the like. In someembodiments, the knowledge distribution system 16802 may be configuredto track access rights and/or ownership rights of one or more of thecurrency tokens, such as by logging contents and/or balances of digitalwallets of users. In some embodiments, the knowledge distribution system16802 may be configured to issue a wallet passcode to a user, the walletpasscode being necessary to access, view, transfer, and otherwise managethe currency tokens owned (or at least partially owned) by the user towhich the wallet passcode has been issued.

In some embodiments, the knowledge distribution system 16802 may includea smart contract system 16868 configured to generate smart contracts16840 and deploy the smart contracts 16840 to the distributed ledger16808. In embodiments, a smart contract 16840 may refer to a piece ofsoftware stored on the distributed ledger 16808 and configured to manageone or more rights associated with one or more instances of the digitalknowledge 16804 and/or one or more knowledge tokens 17038. Inembodiments, the smart contract may be a computer protocol that assistswith negotiation and/or performance of terms in an agreement (e.g.,distributed on blockchain such as Ethereum blockchain). The smartcontract may be used in banking, government, management, supply chain,automobiles, real estate, health care, insurance, etc. In someembodiments, the smart contract 16840 may be contained and/or executedin a virtual machine or a container (e.g., a Docker container). In someembodiments, one or more of the nodes 16916 of the ledger network 16970may provide an execution environment for the smart contract 16840. Inembodiments, a smart contract 16840 may include information, data,and/or logic related to an instance of digital knowledge 16804, one ormore triggering events, one or more smart contract actions to beexecuted in response to detection of one or more of the triggeringevents, and the like. In embodiments, the triggering events may defineconditions that may be satisfied by events performable by one or moreusers, such as the knowledge provider 16806, the knowledge recipient16818, and/or the crowdsourcer 16836, or by one or more third parties.Examples of the triggering events include payment of one party byanother party, adherence, or lack of adherence to one or more terms of asales, licensing, insurance, or other agreement made by one or moreparties, meeting of one or more thresholds or ranges of properties ofone or more pieces of the digital knowledge 16804, such as value, userrating, production amount, or any other suitable property, passage oftime, or any other suitable triggering event. Additionally oralternatively, the triggering events defined in a smart contract 16840may include conditions that may be satisfied independently of action orinaction of a human. For example, a triggering event may be when acertain date is reached, when a stock price reaches a certain threshold,when patent rights expire, when a copyright expires, when a naturalevent occurs (e.g., a hurricane, a tornado, a drought, or the like),etc. Triggering events may be defined as different types of triggers.For example, triggers or triggering events may refer to changing states(e.g., state change event) such as where the smart contract is activeupon a set of data states (e.g., state change events). In otherexamples, triggers or triggering events may refer to events that occursuch that users may need to passively wait for the events to occur andthe knowledge distribution system 16802 may need to monitor for theseevents.

Referring to FIG. 170 , the knowledge distribution system 16802 includesdetails of the smart contract 16840 and the smart contract system 17068.In embodiments, smart contract actions 17086 may include, for example,monitoring events from a defined data source, verifying fulfillment ofobligations of one or more users and/or third parties according to oneor more conditions 17084 defined in the smart contract 16840, verifyingpayment and/or transfer of tokens, property, other goods, or services,between one or more users and/or third parties, transferring the digitalknowledge 16804 between parties or to one or more users, logging one ormore transactions in the distributed ledger 16808, performing one ormore operations with respect to the distributed ledger 16808, creatingone or more new blocks 16922 in the distributed ledger 16808, and thelike. In some embodiments, a smart contract 16840 may include an eventlistener 17080 that is configured to monitor one or more data sources(e.g., databases, data feeds, data lakes, public data sources, or thelike) for detecting events to determine whether one or more conditions17084 are met. For example, an event listener 17080 may listen to anapplication programming interface (API) that provides a connectionbetween the knowledge distribution system 16802 and a printer, such thata smart contract may trigger an obligation of a user to make a paymentwhen a printing instruction set governed by the knowledge distributionset (such as a tokenized instruction set in a knowledge token 17038) isused to print an item using the instruction set. Thus, when a predefinedset of conditions 17084 is met, then a smart contract action 17086 maybe triggered. This may include triggering a payment process (such asinitiating an authorization of a payment on a credit card), closing outa contract (such as when a prepaid number of uses of a knowledge set hasbeen reached), determining a price (such as by initiating a reference tocurrent pricing data in a marketplace or exchange), reporting on anoutcome (such as reporting a workflow or event), or the like. Inresponse to being triggered, the smart contract may automaticallyexecute the smart contract action 17086. In some embodiments, the smartcontracts are Ethereum smart contracts and may be defined in accordancewith the Ethereum specification, which may be accessed athttps://github.com/ethereum, the contents of which are incorporated byreference. In other embodiments, the smart contract system 17068 mayinclude the event listener 17080.

In some embodiments, the smart contract 16840 may be configured to“wrap” one or more instances of the digital knowledge 16804 in a smartcontract wrapper (e.g., a “smart wrapper”). Once wrapped, an instance ofdigital knowledge may be handled and/or accessed differently than whenunwrapped, such as by only being readable, editable, and/ortransferrable according to terms, conditions, and/or operations of thesmart contract 16840. The smart contract 16840 may wrap the digitalknowledge 16804 such that in order to be accessed by the knowledgerecipient 16818, the digital knowledge 16804 must first be “unwrapped,”(e.g., reverted to a pre-wrapped form). In some embodiments, thepre-wrapped form may be the tokenized form. The smart contract 16840,the distributed ledger 16808, and/or the knowledge distribution system16802 may unwrap one or more tokens and/or instances of the digitalknowledge 16804 in response to one or more triggering events. In someembodiments, the knowledge distribution system 16802, or anothersuitable system, may store a plurality of smart contract templates fromwhich the smart contract 16840 may be generated. In some embodiments,the smart contract system 17068 may include a smart contract (SC)generator 17082 that may parameterize at least one smart contracttemplate (from the plurality of smart contract templates) based on theinformation provided by a user and any conditions 17084 and/or actions17086 defined by the user. For example, the smart contract template maycorrespond to a type of digital knowledge that is to be tokenized. Thecontract template may include parameters based on the type of thedigital knowledge. These parameters may include: financial parametersfor use of the tokenized digital knowledge (e.g., financial parameters),royalty rate parameters for intellectual property (e.g., royaltyparameters), number of times an instruction set can be used parameters(e.g., usage parameters), output amount parameters that may be producedusing an instruction set (e.g., output produced parameters), allocationof consideration among parties parameters to the smart contract anddesignated beneficiaries of the smart contract (e.g., allocation ofconsideration parameters), identity parameters that may have permissionto access the distributed ledger 16808 and/or the digital knowledge(e.g., identity parameters), and/or access condition parameters for thedistributed ledger 16808 and/or the digital knowledge (e.g., accesscondition parameters). In some embodiments, the smart contract 16840 maybe configured to manage a wrapped token based on an aggregated set ofinstructions defined in the smart contract 16840.

In some embodiments, the distributed ledger 16808 may store smartcontracts 16840 configured to facilitate licensing of one or moreintellectual property rights corresponding to an instance of digitalknowledge, such as know-how, patented material, trademarks, works ofauthorship (e.g., copyrights), and/or trade secrets. In embodiments, theknowledge distribution system 16802 may be configured to allow one ormore of the knowledge providers 16806 to engage in a licensing agreementwith one or more of the knowledge recipients 16818 via a smart contract16840 (e.g., using one or more knowledge provider devices 16890 and/orone or more knowledge recipient devices 16894). In embodiments, a smartcontract 16840 may be configured to embed licensing terms for theintellectual property in one or more of the blocks 16922 of thedistributed ledger 16808, including scopes of use, waivers,indemnifications, limitations of use, geographical limitations, and/orthe like. In embodiments, one or more copies of and/or references to theone or more pieces of intellectual property may be stored on thedistributed ledger 16808, and access to the one or more pieces ofintellectual property may be governed by terms of the smart contract16840. Upon execution of the smart contract 16840, the knowledgedistribution system 16802 may automatically transfer access andlicensing rights to the intellectual property to the knowledge recipient16818 (e.g., knowledge recipient device 16894 of knowledge recipient16818) according to terms and/or operations set forth in the smartcontract 16840. In some embodiments, the knowledge distribution systemmay be configured to verify assignee rights with resources such aspublic patent assignee logs prior to transferring access and/orlicensing rights. In embodiments, the smart contract 16840 may containone or more operations to be performed with respect to the distributedledger 16808 to facilitate an execution defined by the smart contract16840. In some embodiments, the smart contract 16840 may be configuredto automatically allocate royalties in transfers between one or moreknowledge providers 16806 and knowledge recipients 16818 (e.g., usingknowledge provider devices 16890 and knowledge recipient devices 16894)involving transfer of access to, ownership of, and/or licensing rightsto intellectual property. For example, if the owner of the digitalknowledge pays licensing fees to a third-party patent owner of one ormore aspects of the digital knowledge (e.g., the inventor of aparticular product design), the smart contract may allocate a setpercentage or amount of the transaction price of the digital knowledge16804 to the licensor, such that the license to make, sell, use, and/orotherwise transact for is transferred to the recipient of the digitalknowledge 16804. In embodiments, the operations for allocating royaltiesmay be performed according to one or more terms of one or more of thesmart contracts 16840 and may have related smart contract actions 17086.

In some embodiments, the knowledge distribution system 16802 may beconfigured to aggregate intellectual property licensing terms. Thedistributed ledger 16808 may be configured to store an aggregate stackof instances of the digital knowledge 16804, where one or more aspectsof the digital knowledge 16804 are restricted in accordance with anintellectual property right of a party (e.g., a patent, copyright,trademark, or trade secret of the knowledge provider or any otherparty). In embodiments, the ledger management system 16910 mayfacilitate adding one or more instances of the intellectual property tothe aggregate stack, thereby associating the added instance ofintellectual property to the stack of intellectual property to which theinstance of intellectual property is added. Operations such as transferof control, edit, viewing, ownership, and/or licensing rights may beperformed on an entire stack of intellectual property by the knowledgedistribution system 16802, such as according to terms of one or moresmart contracts 16840. Access to, ownership of, and/or sublicensingrights to the aggregate stack of intellectual property may betransferred from one or more of the knowledge providers 16806 to one ormore of the knowledge recipients 16818 via the knowledge distributionsystem 16802 (e.g., using knowledge provider devices 16890 and knowledgerecipient devices 16894). In some embodiments, a smart contract may beconfigured to transfer rights to the aggregate stack of intellectualproperty associated with an instance of digital knowledge (e.g., theright to use, sell, offer for sale, export, import, a product, orprocess associated with the intellectual property stack) or to transferthe intellectual property stack in its entirety to the digital knowledgerecipient. In the latter scenario, the smart contract may be configuredto facilitate the assignment of the intellectual property stack to thedigital knowledge recipient (e.g., populating assignment forms that aresubmitted to patent, trademark, or copyright offices of one or morejurisdictions and to electronically file the assignment documents). Insome embodiments, the assignment of intellectual property rights may berecorded in the distributed ledger 16808 as well.

In some embodiments, the ledger management system 16910 may define oneor more operations that may handle or process commitments of one or moreparties to the smart contract 16840 and/or terms thereof. When a set ofparties (e.g., knowledge providers 16806, knowledge recipients 16818,crowdsourcers 16836 and/or third parties) commit to the terms of a smartcontract 16840 to a term of a smart contract governing the transfer ofdigital knowledge 16804, the knowledge distribution system 16802 (and/orthe smart contract 16840 itself) may handle or process commitments ofthe parties and/or identifiers of the parties to one or more portions(e.g., terms) of the smart contracts 16840. In embodiments, upon a setof parties committing to a smart contract 16840, the smart contract16840 and/or the knowledge distribution system 16802 may link one ormore of the parties to one or more of the triggering events defined inthe smart contract 16840, begin monitoring one or more data sources todetermine whether any conditions 17084 defined trigger events are met,and/or automatically perform operations/actions defined in the smartcontract (e.g., in response to the occurrence of a triggering event).For example, a knowledge provider 16806 may upload a smart contract16840 (e.g., using knowledge provider device 16890) to the distributedledger 16808 and/or customize a smart contract 16840 using a smartcontract template in connection with uploading an instance of thedigital knowledge 16804. In embodiments, the knowledge provider 16806, aknowledge recipient 16818, or some other party may indicate (e.g., viathe knowledge distribution system 16802, the distributed ledger 16808,and/or the smart contract 16840) terms of an agreement between theknowledge provider 16806 and the knowledge recipient 16818 upon anagreement being formed between the knowledge provider 16806 and theknowledge recipient 16818. In some embodiments, the smart contract 16840may include one or more rights, terms, and/or obligations provided bythe knowledge provider 16806 and/or a third party prior toidentification of and/or dealing with the knowledge recipient 16818. Theknowledge recipient 16818 may agree to be bound by rights, terms, and/orobligations defined via the smart contract 16840 upon agreeing toreceive the digital knowledge 16804 (e.g., using knowledge recipientdevice 16894). The knowledge recipient 16818 may be a user who iswilling to transact (e.g., buy, license, or otherwise make a deal withthe knowledge provider 16806) for the digital knowledge 16804. The smartcontract 16840 may commit or otherwise bind (or process commitments) theknowledge provider 16806, the knowledge recipient 16818, and/or otherparties to the agreement to terms and/or conditions 17084 of the smartcontract 16840 in response to receiving indication via the knowledgedistribution system 16802 and/or the distributed ledger 16808.

In some embodiments, the knowledge distribution system 16802 may includean account management system. In embodiments, the account managementsystem 16846 may facilitate creation and/or storage of user accountsrelated to users of the knowledge distribution system 16802, theknowledge distribution system 16802, and/or the distributed ledger16808. For example, the account management system 16846 may beconfigured to facilitate registration of one or more of the knowledgeproviders 16806, the knowledge recipients 16818, the crowdsourcers16836, and/or other third parties that may be associated with theknowledge distribution system 16802, the knowledge distribution system16802, and/or the distributed ledger 16808. In some embodiments, theaccount management system 16846 may be configured to, together with theledger management system 16910, facilitate intake of data fromregistered users of the distributed ledger 16808, such as name, address,company affiliation, financial account information (e.g., bank accountnumbers and/or routing numbers), digital identifiers (e.g., IPaddresses, MAC addresses, and the like), and any other suitableinformation related to the registered users.

The account management system 16846 may update the user account of theregistered user with data taken in and related to the registered user.In some embodiments, the account management system may facilitategeneration and/or distribution of one or more of the permission keys16932 to one or more of the registered users. The permission keys 16932,16932-1, 16932-2, 16932-3, 16932-N may provide the registered user withaccess to one or more instances of the digital knowledge 16804 and/orservices associated with the knowledge distribution system 16802.

In some embodiments, the knowledge distribution system 16802 may includea user interface system 16850 configured to present a user interface.The user interface may be configured to facilitate uploading of digitalknowledge 16804, generation and/or uploading of a smart contract 16840,and viewing of the digital knowledge 16804 and/or the smart contract16840 (and statuses thereof). The user interface may be a graphical userinterface. Information presented to users of the knowledge distributionsystem 16802 via the user interface may include descriptions of one ormore instances of the digital knowledge 16804, ownership and/orlicensing information related to the one or more instances of thedigital knowledge 16804, information related to the user viewing theuser interface and/or other users of the knowledge distribution system16802, price information related to one or more instances of the digitalknowledge 16804, statistics and/or metrics related to the distributedledger 16808 and/or contents thereof, such as node count, payouts forgeneration of additional nodes, and any other suitable information. Insome embodiments, users may view contents of their digital wallets viathe user interface, such as a balance of one or more types of currencytokens.

In some embodiments, the user interface may be configured to allow oneor more users to perform one or more of the operations related to thedigital knowledge 16804 and/or the distributed ledger 16808, such asbuying, selling, verifying, and/or reviewing the digital knowledge 16804and/or performing other operations related to the distributed ledger16808 discussed herein. For example, the knowledge provider 16806 mayselect a computer file (such as a 3D printer schematic file) to uploadto the distributed ledger 16808 via the user interface (e.g., usingknowledge provider device 16890). The user interface may present theknowledge provider 16806 with one or more options related to uploadingthe digital knowledge 16804, such as an ability to configure a smartcontract 16840 and related terms for wrapping and/or tokenizing thedigital knowledge 16804. Other options may include privacy options, suchas options pertaining to one or more users or classes of users who mayand/or may not view, buy, sell, license, rate, verify, review, orotherwise manage or interact with the digital knowledge 16804.

In some embodiments, the user interface system 16850 may include amarketplace system 16854 configured to establish and maintain a digitalmarketplace 16856. In embodiments, the digital marketplace 16856provides an environment that allows knowledge providers and potentialrecipients to engage in commerce relating to the transfer of digitalknowledge 16804. For example, the digital marketplace may be configuredto allow one or more users and/or third parties to search for one ormore pieces of digital knowledge 16804 similar to a digital storefront,transact for one or more pieces of the digital knowledge 16804 (e.g.,buy, sell, license, lease, bid on, and/or give away the digitalknowledge), receive recommendations for digital knowledge 16804, reviewone or more pieces of the digital knowledge 16804, verify sourceinformation and/or other information related to one or more pieces ofthe digital knowledge 16804, transact for one or more pieces of thedigital knowledge 16804 (e.g., buy, license, bid on one or more piecesof the digital knowledge, or the like), and/or perform any othersuitable marketplace interaction with the digital knowledge 16804, oneor more of the knowledge providers 16806, the distributed ledger 16808,one or more of the knowledge recipients 16818, one or more crowdsourcers16836, or any other user or third party. In some embodiments, thedigital marketplace 16856 may be configured to allow users to edit useraccounts associated with themselves and view user accounts associatedwith other users. In some embodiments, the digital marketplace 16856user interface may allow users to make reviews and/or ratings of otherusers.

In embodiments, the knowledge distribution system 16802 may include oneor more datastores 16858. FIG. 171 illustrates an example set ofdatastores 16858 of the knowledge distribution system 16802. In someembodiments, the knowledge distribution system 16802 may include one ormore datastores 16858 configured to store data related to the digitalknowledge 16804, the distributed ledger 16808, the knowledge providers16806, the knowledge recipients 16818, the crowdsourcers 16836, theknowledge tokens 17038, the smart contracts 16840, the accountmanagement system 16846, the marketplace system 16854, or any othersuitable type of data. A datastore may store folders, files, documents,databases, data lakes, structured data, unstructured data, or any othersuitable data.

In some embodiments, the datastores 16858 may include a knowledgedatastore 17160 configured to store data. The knowledge datastore 17160may be in communication with the user interface system 16850. The userinterface system 16850 may be configured to populate the user interfacewith data stored in the knowledge datastore 17160. In some embodiments,data stored in the knowledge datastore 17160 may include knowledgerelated to the digital knowledge 16804 such as source, reviews, price,ownership, licensing, related knowledge providers 16806, relatedknowledge recipients 16818, serial numbers, related crowdsourcers 16836,or any other suitable information. For example, the knowledge datastore17160 may contain information related to a 3D printer schematic such asorigin, date of creation, names of one or more contributing individuals,groups, and/or companies, pricing, market trends for related schematics,serial numbers and/or part identifiers, and any other suitable type ofdata related to the 3D printer schematic.

In some embodiments, the datastores 16858 may include a client datastore17162 (e.g., may include user datastore), the client datastore 17162being configured to store data relating to users of the knowledgedistribution system 16802. The client datastore 17162 may be incommunication with the account management system 16846 and may bepopulated with user accounts related to one or more of the useraccounts, data contained in one or more of the user accounts, datarelated to the one or more user accounts, and/or a combination thereof.

In some embodiments, as shown in FIGS. 170 and 171 , the datastores16858 may include a smart contract datastore 17164. In embodiments, thesmart contract datastore 17064 is configured to store data related toone or more of the smart contracts 16840 and/or smart contract templates(from which smart contracts 16840 may be parameterized andinstantiated). In embodiments, the smart contract datastore 17064 may bein communication with the ledger management system 16910. Data stored inthe smart contract datastore may include, for example, smart contracttemplates, one or more smart contracts 16840, data related to instancesof the digital knowledge 16804 related to one or more of the smartcontracts 16840, data related to parties to one or more of the smartcontracts 16840, and any other suitable data. The smart contractdatastore 17064 may be configured to store completed smart contractsthat have already been executed. The smart contract datastore 17064 maybe configured to store smart contracts that have not yet been uploadedto the distributed ledger 16808.

Referring to FIG. 168 , in some embodiments, the knowledge distributionsystem 16802 may include an analytics system 16866 configured to analyzeone or more tokenized instances of the digital knowledge 16804, such asthe knowledge token 17038, and report an analytic result. The analyticssystem may analyze the tokenized instance of the digital knowledge 16804in order to determine one or more properties and/or metrics of thetokenized instance of the digital knowledge 16804. Properties oftokenized digital knowledge 16804 may include, for example, source,reviews, price, ownership, licensing, related knowledge providers 16806,related knowledge recipients 16818, serial numbers, relatedcrowdsourcers 16836, or any other suitable information. The analyticssystem 16866 may be configured to determine one or more trends, metrics,and/or predictions related to the properties. In some embodiments, theanalytics system 16866 may include a machine learning module configuredto perform predictions and/or analyses of the properties related to thedigital knowledge 16804 via one or more machine learning techniques.

In some embodiments, properties of tokenized intellectual property maybe analyzed by the analytics system 16866. For example, the analyticssystem 16866 may be configured to analyze tokenized digital knowledge16804 including intellectual property. Analyzed properties of tokenizedintellectual property may include, for example, transaction historyincluding changes and/or assignments in ownership and/or licensingrights of the intellectual property, litigation history includinglawsuits involving the intellectual property and data related to thelawsuits, information pulled from one or more databases of intellectualproperty related to the intellectual property, and any other suitableproperty of the tokenized intellectual property or any other token orsuitable instance of the digital knowledge 16804. Metrics related totokenized intellectual property may include, for example, value, age,strength, efficacy, or any other suitable metric related to thetokenized intellectual property or any other knowledge token 17038 orsuitable instance of the digital knowledge 16804.

In some embodiments, the knowledge distribution system 16802 may beconfigured to perform an aggregation operation that aggregates a set ofoperations and/or instructions included in one or more instances of thedigital knowledge 16804. Aggregation may be employed where componentinstances of digital knowledge 16804 are aggregated to form largerinstances of digital knowledge. In embodiments, aggregation occurs wherecomponent instances are concatenated to form larger instances, such asby adding component instances as additional (optionally tokenized)blocks to a chain, or by adding references or links to componentknowledge blocks. Examples of concatenation aggregation include wherechapter instances are concatenated to form book instances, wheresentence instances are concatenated to form paragraphs, where wordinstances are concatenated to form sentence instances, and the like. Inembodiments, aggregation occurs where component instances are linkedschematically to form a system, such as where knowledge representingphysical component parts of a machine are linked to form the machine,where component steps in a process are linked to form the completeprocess, or the like. In embodiments, aggregation of knowledge involveslinking elements in a flow, such as where instances are linkeddiagrammatically to generate a workflow, such as where ingredients andprocess steps are linked to generate a recipe or formulation process,where workflow steps and materials are linked to describe a work process(such as one involving expertise or know how) or the like. Inembodiments, aggregation of knowledge involves coupling of partialinstances to form a whole instance, such as where two or more sub-partsof a formula are joined to form a complete formula (e.g., a chemical,pharmaceutical, biological, materials science, physical, or otherformula), where two or more sub-parts of an instruction set (e.g.,computer code) are joined to form the entire instruction set, or thelike. In embodiments, aggregation may involve linking related instancesof knowledge in a cluster, such as where instances of knowledge aretopically linked to form a cluster, such as a cluster of relatedsubjects in a knowledge domain (such as a scientific domain, a domain ofthe humanities, a social science domain, a commercial domain, a businessdomain, or many others). In embodiments, aggregation may involvehierarchical aggregation of knowledge, such as by representing knowledgeaccording to one or more defined hierarchies, such as an organizationalhierarchy (such as an organizational chart or reporting structure), anindustry hierarchy, a topical hierarchy, a physical hierarchy, or thelike. Many other examples of aggregation may be envisioned. The ledgermanagement system 16910 may be configured to perform the aggregationoperation. The aggregation operation adds at least one instruction to apreexisting set of instructions, thereby yielding a modified set ofinstructions. The modified set of instructions may then be stored in thedistributed ledger 16808 and may be tokenized, manipulated, and/ormanaged similarly to any instance of the digital knowledge 16804. Insome embodiments, the aggregation operation may be performed by thesmart contract 16840, according to one or more terms of the smartcontract 16840, and/or in reaction to triggering of one or moretriggering events of the smart contract 16840. In some embodiments, theaggregation operation may be performed at request of a user of theknowledge distribution system 16802.

In some embodiments, the ledger network 16970 is a federated network,such that the ledger management system 16910 of the knowledgedistribution system 16802 may act as an arbiter to simplify theconsensus mechanism. Some or all of the nodes 16916 may be preselectedor preapproved to act as nodes 16916 with respect to the management ofblocks of the distributed ledger 16808 and/or data contained therein,such as one or more instances of the digital knowledge 16804. The ledgermanagement system 16910 may ease computational burdens on the othernodes 16916 in the ledger network 16970. In some embodiments, thedistributed ledger 16808 is distributed, such that the participatingnodes 16916 may each store a respective local copy 16808-L of thedistributed ledger 16808, where each local copy 16808-L may include theentire distributed ledger 16808 or a portion thereof.

In the illustrated example, the knowledge distribution system 16802stores a copy of the distributed ledger 16808, the copy of thedistributed ledger 16808 being local to the knowledge distributionsystem 16802, and each node 16916 stores a distributed local copy16808-L of the distributed ledger 16808. In some embodiments, however,the knowledge distribution system 16802 does not store a local copy ofthe distributed ledger 16808 such that the distributed ledger 16808 ismaintained wholly by participant nodes 16916. A distributed copy (e.g.,copy 16808-L) of the distributed ledger 16808 may contain the entiredistributed ledger 16808 or only a portion of the distributed ledger16808. In general, each copy of the distributed ledger 16808 stores aset of blocks 16922. In some embodiments, each respective block maystore information relating to a respective state change event as a hashvalue and may further store a block identifier of a “parent” block thatwas added to the distributed ledger 16808 prior to the respective block.In some embodiments, the ledger management system 16910 may select theblock that was most recently added to the ledger to act as the parentblock, whereby the ledger management system 16910 includes the blockidentifier of the most recently added block to the state change eventrecord.

A state change event may refer to any change of state relating to thedigital knowledge 16804 and/or management of one or more instances ofthe digital knowledge 16804. Non-exhaustive examples of state changeevents may include creating a new instance of the digital knowledge16804, registering a new user of the knowledge distribution system16802, such as a new knowledge provider 16806 (and/or registering a newknowledge provider device 16890) or a new knowledge recipient 16818(and/or registering a new knowledge recipient device 16894), grantingthe new user permission to perform a specific operation, modifyingcertification and/or validation of one or more instances of the digitalknowledge 16804 at the request of a user, transmitting one or moreinstances of the digital knowledge 16804 to one or more knowledgerecipients 16816 (e.g., knowledge recipient devices 16894), recordationof a use of an instance of digital knowledge 16804 by a knowledgerecipient, and the like. In some embodiments, the ledger managementsystem 16910 may create a state change event record that indicates thestate change event, e.g., the operation that was performed, for eachstate change event that occurs. A state change event record may furtherbe information/metadata relating to the event, such as one or more useridentifiers of one or more respective users associated with the statechange event, a timestamp corresponding to the state change event, adevice identifier of the device that requested or performed anoperation, an IP address corresponding to the device that requested orperformed the operation, and/or any other relevant data. In someembodiments, the ledger management system 16910 may include a blockidentifier of a previous block that was previously stored on the ledger16808 in the state change event record, such that the previous block maybe a “parent” to a new block that will be generated based on the statechange event record, as the state change event record references thepreviously stored block, but the previously stored block will notreference the new block. In some embodiments, the block identifier maybe the hash value of a previously generated block.

In some embodiments, the ledger management system 16910 and/or one ormore of the nodes 16916 may be configured to generate a state changeevent record for each event occurring with respect to management ofdigital knowledge 16804 via the knowledge distribution system 16802. Inembodiments, the ledger management system 16910 and/or one or more ofthe nodes 16916 may generate a new block 16922 corresponding to thestate change event record by generating a cryptographic hash (or “hashvalue”) of the state change event record by inputting the state changeevent record into a hashing function to obtain the cryptographic hash.The resultant hash value is a unique value (or substantially uniquevalue with a very low likelihood of collisions) that represents thecontents of the state change event record, such that the resultant hashvalue is a unique identifier that identifies the new block and that alsoencodes the contents thereof, including a block identifier of the parentblock. Thus, when the new block is “solved” (solving a block in thiscontext may refer to the process of determining the original contents ofthe state change event record encoded in the hash value), the solutionof the new block indicates the contents of the state change eventrecord, including the block identifier of the parent block. As such, thehash value of the preceding state change event record may be used toverify the authenticity of the current state change event record by wayof verification. While the above description describes blocks that storeonly one state change event record, in some embodiments, the ledgermanagement system 16910 and/or one or more of the nodes 16916 may encodetwo or more state change event records in a single block. The ledgermanagement system 16910 and/or one or more of the nodes 16916 mayinclude the two or more state change event records in the body of a newblock data structure and may include the block identifier of theprevious block in a block header of the new block data structure. Theledger management system 16910 and/or one or more of the nodes 16916 maythen input the new block data structure into the hashing function, whichoutputs the new block 16922. In these embodiments, the new block 16922may be a cryptographic hash that represents the two or more state changeevent records and the block identifier of the previous block (i.e., theparent block to the new block). In this way, when the new block issolved, the solution to the block is the two or more state change eventrecords and the block identifier of the parent block, where the blockidentifier of the parent block can be used to validate the authenticityand accuracy of the new block.

In embodiments, the ledger management system 16910 and/or one or more ofthe nodes 16916 may request verification of a block 16922. In someembodiments, verification of a block 16922 may include broadcasting arequest 16924 to verify a block 16922 (referred to as “the block 16922to be verified”) to the other nodes 16916 in the ledger network 16970(which may include the ledger management system 16910 if the ledgermanagement system 16910 is not issuing the request 16924). In someembodiments, the request 16924 may include or be broadcasted with theblock 16922 to be verified. Verification may further include one of theother nodes 16916 that received the request 16924 (or potentially theledger management system 16910) solving the block 16922 to be verified.A node 16916 may determine it has solved the block 16922, when thesolution to the block contains a valid block identifier—that is, a blockidentifier that references one of the other blocks 16922 stored on thedistributed ledger 16808. Once the solver has determined the solution tothe block 16922, the solver broadcasts a “proof of work” 16928 to theother nodes 16916. In some embodiments, the proof of work 16928 may bethe block identifier of the previous block 16922. In some embodiments,each of the non-solving nodes 16916 (potentially including the ledgermanagement system 16910), may receive the proof of work and may validatethe proof of work based on the copy of the distributed ledger 16808 thatis stored at the node 16916. In these embodiments, each node 16916 maydetermine whether the block identifier contained in the proof of workcorresponds to (e.g., matches) a block identifier of a block stored onthe local copy of the distributed ledger 16808.

In some embodiments, the ledger management system 16910, in conjunctionwith the other nodes 16916 in the ledger network 16970, maintains animmutable record of any operations performed with respect to amanagement of digital knowledge 16804 via the knowledge distributionsystem 16802 using the knowledge distribution system 16802. In theseembodiments, any time a user performs an operation with respect tomanagement of digital knowledge 16804 via the knowledge distributionsystem 16802 hosted on the knowledge distribution system 16802, theledger management system 16910 may: generate a new event state recordcorresponding to the operation; encode the new event state record into anew block data structure and a block identifier of a previous block(e.g., the most recently added block) into a block header of the newblock data structure; and hash the new block data structure using ahashing function to obtain the new block. Furthermore, in someembodiments, the ledger management system 16910 may transmit a request169240 to verify the new block 16922 to the other nodes 16912 in thenetwork 16814. In some embodiments, one of the nodes 16916 may attemptto determine a solution to the new block 16922. If a valid solution isdetermined, the solver node 16916 may transmit a proof of work 16928 tothe other nodes 16916 in the ledger network 16970, and the other nodes16916 may attempt to validate the proof of work 16928.

In some embodiments, the ledger management system 16910 utilizes adistributed ledger 16808 to manage permissions of different users of theknowledge distribution system 16802, such as one or more of theknowledge providers 16806 (and/or one or more knowledge provider devices16890) and/or one or more of the knowledge recipients 16818 (and/or oneor more knowledge recipient devices 16894). In some embodiments,permissions may be granted with respect to an instance of the digitalknowledge 16804 or set of instances of the digital knowledge 16804. Forexample, permissions may include permission for a user to upload aninstance of the digital knowledge 16804 or set of instances of thedigital knowledge 16804, permission for a user to view an instance ofthe digital knowledge 16804 or set of instances of the digital knowledge16804, permission for a user to edit an instance of the digitalknowledge 16804 or set of instances of the digital knowledge 16804,permission for a user to delete an instance of the digital knowledge16804 or set of instances of the digital knowledge 16804, permission fora user to download an instance of the digital knowledge 16804 or set ofinstances of the digital knowledge 16804, permission for a user to print(e.g., print-to-paper or 3D print) an instance of the digital knowledge16804 or set of instances of the digital knowledge 16804, and the like.Permissions may additionally or alternatively relate to services 16930that are offered by the knowledge distribution system 16802. Forexample, permissions may include permission for a user to access afull-text search functionality on the knowledge distribution system16802, permission for the user to use a virus scanner offered by theknowledge distribution system 16802, permission for the user to have theknowledge distribution system 16802 generate a machine-generatedinstance of the digital knowledge 16804 or set of instances of thedigital knowledge 16804, and the like. Permissions may also include thepermission to perform an operation granted to one or more other users.Permissions may be applied to one or more users by default, applied toone or more classes of users, such as users holding one or more of thepermission keys 16932, automatically be applied to one or morecategories of users such as knowledge provider 16806, knowledgerecipient 16818, and/or crowdsourcer 16836, and/or may be manually givento one or more users by administrators and/or managers of the knowledgedistribution system.

In some embodiments, the ledger management system 16910 may manageindividual participant's access to respective services 16930 bygenerating one or more unique service-specific permission keys 16932 fora respective service 16930 and issuing each respective uniqueservice-specific permission key 16932 to a respective participant thathas been granted access to the respective service 16930. In some ofthese embodiments, the ledger management system 16910 may utilize thedistributed ledger 16808 to store proof of the service-specificpermission keys 16932 and to manage permissions to the services 16930.In these embodiments, the ledger management system 16910 may: receive aninstruction to grant a user permission to access a particular service;generate a service-specific permission key 16932 corresponding to theparticular service and assign the key 16932 to the user; encode a userID of the user and the service-specific permission key 16932 in a statechange event record; generate a new block based on the state changeevent record and a block identifier of a previously stored block; storethe new block on its local copy of the distributed ledger 16808, andbroadcast the new block to other nodes 16916 in the network for storageon the respective copies of the distributed ledger 16808 stored at thenodes. In some embodiments, the ledger management system 16910 mayvalidate the new block prior to storing and transmitting the block forstorage at the other nodes 16916. The ledger management system 16910 mayfurther transmit the new block to a computing device associated with theuser (which may or may not be a participating node), whereby an agent16920 on the computing device may store the new block. In this way, theagent 16920 may use the new block to obtain access to the particularservice, when the user attempts to access the particular service 16930from the computing device. When the user attempts to access theparticular service 16930, the agent may communicate the block containingthe permission key 16932 corresponding to the particular service 16930to the ledger management system 16910. The ledger management system16910 may solve the received block and/or validate the received block,in the manner described above. If the ledger management system 16910 isable to validate the received block, the ledger management system 16910grants the computing device of the user access to the service 16930,whereby the user may begin using the service 16930. In some embodiments,permissions and access to an instance of the digital knowledge 16804 orset of instances of the digital knowledge 16804 that are stored in anorganizational structure may be managed in a similar way, where usersare granted permission keys 16932, where these permission keys 16932correspond to specific operations that a user may perform on theinstance of the digital knowledge 16804 or set of instances of thedigital knowledge 16804 with which the permission keys 16932 areassociated.

In some embodiments, the ledger management system 16910 and/or one ormore computing node computing devices 16916 having requisite processingresources may generate an immutable log of a transaction based on thedistributed ledger 16808. In these embodiments, the ledger managementsystem 16910 and/or the nodes 16916 (referred collectively as thesolving nodes 16916) may begin solving the most recent blocks 16922 inthe distributed ledger 16808. Each time a block is solved, the solvingnode 16916 may transmit the proof of work 16928 to other nodes 16916,which may then verify the accuracy of the solution. The solving nodes16916 may iteratively solve each of the blocks 16922 in the distributedledger 16808 in this manner until the entire distributed ledger 16808 issolved, thereby resulting in an operation log of the management ofdigital knowledge 16804 via the knowledge distribution system 16802. Theoperational log may define the actions or operations that were performedusing the knowledge distribution system 16802. By creating, validating,and solving the blocks 16922 of the distributed ledger 16808 in themanners described above, the distributed ledger 16808 is generated in atransparent and secure manner. The resultant operational log is storedin an encrypted manner until it is solved, and once solved, theoperational log is auditable and immutable. The operational log mayindicate each time a user was allowed to access the distributed ledger16808 and/or management of digital knowledge 16804 via the knowledgedistribution system 16802, the permissions that each user was granted,the requests to perform operations or use services 16930 that each userinitiated, the operations that were performed, the user that performedthe operation, and the like.

In some embodiments, the solving nodes 16916 may optimize the solving ofthe ledger 16808 by solving different blocks 16922 in a distributedmanner. For example, if the distributed ledger 16808 includes one ormore forks (e.g., when more than one child block points 16922 to thesame parent block 16922), the distributed ledger 16808 may be said tofork at the parent block 16922. In this example, each chain originatingat the fork may have a final block 16922 (or leaf block 16922). In thisscenario, different solving nodes 16916 may begin solving the ledger16808 at different leaf blocks 16922 in a breadth-first or depth-firstmanner, thereby increasing the speed at which the ledger 16808 issolved.

In some embodiments, the ledger management system 16910 may beconfigured to facilitate collaboration between one or more of theknowledge providers 16806, one or more of the knowledge recipients16818, or a combination thereof, by assisting in the execution ofmanagement of digital knowledge 16804 via the knowledge distributionsystem 16802 using, for example, smart contracts. In these embodiments,the ledger management system 16910 may provide management of digitalknowledge 16804 via the knowledge distribution system 16802 that isdefined to facilitate a respective type of management of digitalknowledge 16804. For example, for a transfer of one or more instances ofdigital knowledge to a knowledge recipient 16818 (e.g., using knowledgerecipient device 16894) in exchange for funds transmitted to a knowledgeprovider 16806 (e.g., knowledge provider device 16890) in accordancewith a sale, license, or rental agreement and/or a smart contract, theledger management system 16910 define various tasks that must becompleted before a next step can be performed in sale, license, orrental of the digital knowledge 16804. In this example, the knowledgedistribution system 16802 may require that an instance of the digitalknowledge 16804 or a link and/or reference thereto must be uploaded tothe distributed ledger 16808 prior to a transfer of funds from theknowledge recipient 16818 to the knowledge provider 16806. Anothercondition may be that one or more parties having adequate permission tosign a document must electronically execute a document before engagingin a transfer of one or more instances of the digital knowledge 16804.The knowledge distribution system 16802, the ledger management system16910, and/or the distributed ledger 16808 may be preconfigured based onthe type of management of digital knowledge 16804 to be executed via theknowledge distribution system 16802 and/or may be customized by one ormore parties associated with the management of digital knowledge 16804via the knowledge distribution system 16802. In some embodiments, eachoperation and/or management of digital knowledge 16804 via the knowledgedistribution system 16802 may be encoded in a smart contract, wherebythe smart contract may manage the phases of the workflow when the smartcontract determines that one or more required conditions are met. Insome embodiments, copies of a smart contract are stored and executed bythe agents 16920 of one or more respective nodes 16916. The agent 16920may facilitate the performance of operations that are defined in thesmart contract (including validating permissions to perform theoperations using the distributed ledger 16808), the reporting andrecording of the performance of the operations (e.g., by generatingblocks or requesting generation of blocks from the ledger managementsystem 16910), and/or verifying that one or more conditions defined inthe smart contract are met. Once a consensus is achieved with respect toone or more required conditions, the management of digital knowledge16804 via the knowledge distribution system 16802 may progress to a nextphase in the workflow. In this way, the ledger network 16970 (e.g., theledger management system 16910 and the participating nodes 16916) mayfacilitate collaboration between parties in the management of digitalknowledge 16804 via the knowledge distribution system 16802 by assistingin the execution of the workflow associated with the management ofdigital knowledge 16804 via the knowledge distribution system 16802 byvalidating pre-closing and closing work and/or providing a framework forthe management of digital knowledge 16804 via the knowledge distributionsystem 16802 by way of smart contracts.

FIG. 172 illustrates a method 17200 of deploying a knowledge token 17038and related smart contract 16840 via the knowledge distribution system16802.

At 17210, the knowledge distribution system 16802 receives an instanceof the digital knowledge 16804, such as from a user. In embodiments, theuser may be affiliated with an organization (e.g., an organization thatowns the digital knowledge) or an unaffiliated individual (e.g., aperson who created the digital knowledge on their own or incollaboration with other unaffiliated individuals). The user may providethe instance of the digital knowledge 16804 via a graphical userinterface. For example, the user may upload the digital knowledge viathe graphical user interface. In embodiments, the digital knowledge maybe an instruction set that may be performed by a device or set ofdevices. The user may upload the digital knowledge by providing theknowledge itself or a reference to the digital knowledge (e.g., anaddress from which the digital knowledge may be accessed/retrievedelectronically).

In embodiments, the user may provide additional information, such as atype of the digital knowledge, a description of the digital knowledge, aprice to be charged to access the digital knowledge, and the like. Insome embodiments, the user may provide licensing data, such as anypatent, trademark, copyright rights that are licensed or otherwiseconveyed to a knowledge recipient, a length of the license(s) (e.g.,when each license expires), a scope of the license(s) (e.g., limitationson use/sale/transferability or geographical limitations), and the like.In embodiments, the user may define validation information, such ascertifications/validations of the digital knowledge. In embodiments, theuser may also define limitations on the distribution of the digitalknowledge (e.g., a total number of knowledge tokens that may begenerated).

In embodiments, the user may define a set of conditions and/or actionsthat are used to generate a smart contract governing transactions forthe digital knowledge. Examples of conditions may include a time periodwhen the smart contract is valid, requirements for a recipient device(e.g., certain specifications on a device, such as a type of 3D printer,a minimum amount of processing power, required machinery to performcertain processes, or the like) that must be verified before release ofthe digital knowledge, requirements for a knowledge recipient (e.g.,definitions of certain types of data that must be provided to ensure theknowledge recipient is eligible to receive the digital knowledge), orany other suitable conditions. In some embodiments, the user may definea set of actions that may be performed in response to certain conditionsbeing triggered. Some of the actions that are performed by a smartcontract may be default conditions, such as writing a record of thetransaction to the distributed ledger or releasing the digitalknowledge. In some embodiments, a user may define custom actions, suchas defining allocations of funds to third-party rights holders,generating a serial number for a product that is produced by the digitalknowledge, digitally signing a product that is produced by the digitalknowledge, exposing an API to a knowledge recipient, or the like.

At 17212, the knowledge distribution system tokenizes the digitalknowledge 16804, thereby creating a knowledge token 17038. Inembodiments, the ledger management system may tokenize the digitalknowledge by wrapping a smart contract around the digital knowledge toobtain the knowledge token. In some embodiments, the ledger managementsystem may retrieve a smart contract template from the smart contractdatastore, such that the smart contract template corresponds to a typeof the digital knowledge that is to be tokenized. In some of theseembodiments, the ledger management system may parameterize the smartcontract template based on the information provided by the user and anyconditions and/or actions defined by the user. For example, the smartcontract may be parameterized for the instance of digital knowledge (ora reference thereto), any licenses that are granted, the price to bepaid, any conditions that are to be met, and any actions to beperformed. In some embodiments, the ledger management system may includeany libraries in the smart contract that are needed to support any ofthe functions defined in the smart contract. In some embodiments, theledger management system may configure one or more event listeners thatallow the smart contract to monitor one or more data sources. In theseembodiments, the ledger management system may define the data source(s)to be monitored, whereby the event listener obtains and/or processes thedata from the data source(s) which is then used to determine whether acertain condition or set of conditions is met. Additional examples oftokenization may be found in the Ethereum specification, which may beaccessed at https://github.com/ethereum. In embodiments, the knowledgedistribution system may generate a set number of knowledge tokens,whereby each knowledge token may be used to facilitate a differenttransaction for the instance of digital knowledge.

At 17214, the knowledge distribution system stores the knowledgetoken(s) 17038. In embodiments, the ledger management system may storethe knowledge token(s) by deploying the knowledge token(s) to adistributed ledger 16808. In embodiments, the ledger management systemmay initially assign the ownership of the knowledge token(s) to theknowledge provider. In embodiments, the knowledge distribution systemmay also store information relating to the instance of digital knowledgein the knowledge datastore, which may be used to populate a marketplacesite where potential knowledge recipients can view information relatingto the digital knowledge.

FIG. 173 illustrates a method 17300 of performing high level processflow of a smart contract that distributes digital knowledge. Inembodiments, the smart contract may be a knowledge token that is storedon the distributed ledger and that is executed by one or more nodes thathost the distributed ledger. In some of these embodiments, the smartcontract may be executed on a virtual machine or in a container.

At 17310, the smart contract monitors one or more of the conditionsdefined in the smart contract. In some embodiments, an event listenerobtains data (either passively or actively) from one or more datasources defined in the smart contract 16840. As the event listenerobtains data from the one or more data sources, the smart contract maydetermine whether certain conditions are met, and if so, may perform anaction that is triggered by the met conditions.

At 17312, the smart contract verifies conditions for transaction ofdigital knowledge and at 17314, the smart contract initiates transfer ofthe digital knowledge 16804. In embodiments, the smart contract mayinclude an event listener that determines whether a requisite amount offunds have been deposited to the smart contract. Once a party hasdeposited the requisite amount of funds (e.g., a predefined amount ofcryptocurrency or fiat currency), the smart contract may initiate thetransfer of the digital knowledge to the knowledge recipient (e.g., theparty that deposited the requisite amount of funds). In embodiments,this may include updating the distributed ledger with a block thatindicates the change in ownership of the token to the knowledgerecipient and providing any required keys to the knowledge recipient.Once the ownership of the knowledge token has been changed, theknowledge recipient may access the digital knowledge contained therein(and in accordance with any restrictions defined in the smart contract,such as using a particular type of device).

As discussed, the techniques described herein may be applied tofacilitate transactions for different types of instruction sets. In someembodiments, the knowledge distribution system may be used to distributeinstruction sets for 3D-printing specific products (e.g., replacementparts, medical devices, custom products, manufacturing parts, and thelike). In operation, the knowledge distribution system 16802 may presenta graphical user interface to a user, whereby the user may provide aninstance of digital knowledge, as well as a user provider (e.g., aknowledge provider) may upload an instruction set for printing a 3D itemto the knowledge distribution system 16802. In embodiments, the 3Dprinting instruction set may include a file (e.g., a CAD file and/or anSTL file) and any accompanying instructions for printing the productdefined in the file. In some embodiments, the user may also define atransaction price that defines an amount of currency (fiat currencyand/or cryptocurrency) that must be paid to purchase a knowledge tokencontaining the 3D printing instruction set. Additionally, the user mayprovide a description of the product and any requirements for printing(e.g., required materials and/or device types or minimum specificationsneeded to 3D print the product). The user may also provide additionalinformation, such as photographs of a printed product, certificationsmade with respect to the product, and the like.

In embodiments, the user may define any intellectual property rightsthat are being licensed or otherwise conveyed to a knowledge recipientwith the digital knowledge (also referred to as an intellectual propertystack) with the transaction for the 3D printing instruction set. In someembodiments, the user may define an allocation schedule that defines howroyalties are divided amongst one or more licensors. For example, if theproduct that is printed from the instance of digital knowledge islicensed under one or more patents, design patents, copyrights, and/ortrademarks, a portion of the transaction price for each printed productmay be allocated to the licensors as royalty payments. In this example,the user may identify the licensor(s) that collect the royalties and mayassign a percentage or amount of the royalties that go to eachrespective licensor. In embodiments, the user may define anygeographical limitations on the digital knowledge. For example, the usermay define countries, regions, jurisdictions, or other geographicalareas to which the digital knowledge may or may not be distributed. Inembodiments, the user may further define other types of permissions orrestrictions, including 3D printer requirements (e.g., a set of 3Dprinter types, makes and models that can print the product, serialnumbers of 3D printers that can print the product, material types thatmust be used to print the 3D product, and the like), a time periodduring which the item can be 3D printed, whether the digital token maybe transferred to a downstream recipient, or the like. In embodiments,the user may define actions that are performed in connection with 3Dprinting an object, such as assigning a serial number to the product(which may or may not be printed to the object), and/or the like. Inembodiments, the user may further define any warranties, disclaimers,indemnifications, and/or the like associated with the 3D-printedproduct.

In embodiments, the smart contract system 17068 may generate knowledgetokens 17038 that contain the digital knowledge (or a referencethereto). In some embodiments, the smart contract system 17068 maytokenize the digital knowledge by wrapping the digital knowledge (e.g.,the 3D printing instruction set or a reference to the instruction set)with a smart contract wrapper. In some embodiments, the smart contractsystem 17068 may obtain a smart contract template and may parameterizethe smart contract using some of the information entered in by the user,such as price, license fee allocations, geographic restrictions, otherrestrictions, custom actions (e.g., assigning serial numbers), if/whenthe token expires, 3D printer requirements, and the like. In someembodiments, each knowledge token that is generated for the 3D printerinstruction set may be assigned a different serial number, such thateach 3D-printed product may be identified by its serial number andassociated with the token from which it was printed. In this way, theproduct may be verified and tied to a particular record in thedistributed ledger. In embodiments, the smart contract system 17068 mayoutput the generated knowledge tokens to the ledger management system16910.

In embodiments, the ledger management system 16910 may upload theknowledge tokens on the distributed ledger. In some embodiments, theledger management system 16910 may generate a block containing theknowledge tokens and may broadcast the block to the distributed ledger16808, whereby a knowledge recipient may then transact for one or moreof the knowledge tokens (e.g., to print one or more respective productsusing the 3D printing instruction set). In some embodiments, one or moreof the recipient nodes may execute the smart contracts that wrap thedigital tokens, whereby the smart contract listens for one or moretriggering conditions (e.g., receiving an amount of currency equal tothe transaction price of the knowledge token). Additionally oralternatively, the ledger management system 16910 may execute the smartcontracts (e.g., in containers) and may record the transaction for theknowledge token to the distributed ledger.

In embodiments, the knowledge distribution system 16802 may provide orconnect to a digital knowledge marketplace, whereby potential recipientsmay purchase knowledge tokens corresponding to respective 3D-printinginstruction sets. For example, the marketplace may display items thatmay be 3D printed, such as airplane parts, car parts, machinery parts,other types of replacement parts, toys, medical devices, and/or thelike. A potential recipient may enter into a transaction for aparticular 3D printing instruction set. In embodiments, the potentialrecipient may select one of the items. In response, the knowledgedistribution system may present the price of each token, therestrictions associated with the knowledge token (e.g., any devicerequirements, geographical restrictions, use limitations, and/or thelike), warranties, disclaimers, indemnifications, certifications, and/orthe like to the potential recipient. The potential recipient may thenchoose to accept the terms of the transaction (e.g., agree to buy thetoken). The potential recipient may then commit a defined amount ofcurrency to the transaction. In response, the smart contract may listenfor additional conditions (if so defined) before completing thetransaction and/or releasing the digital knowledge. For example, thesmart contract may request the potential recipient to verify that theprinter requirements are met or may connect to the 3D printer to verifythe requirements. If all the conditions required to complete thetransaction are met, the smart contract may provide the currency to theknowledge provider (and any other licensors) and may perform any otheractions, such as releasing the digital knowledge to the 3D printer (oranother device), broadcasting a block to the distributed ledgerverifying the transaction and/or recording the serial number in thedistributed ledger. The 3D printer may receive the 3D printinginstructions and may print the product in accordance with the 3Dprinting instruction set.

In embodiments, the knowledge distribution system 16802 may be deployedon or integrated with or within a set of infrastructure capabilities,such as cloud computing infrastructure, platform-as-a-serviceinfrastructure, Internet of Things platform capabilities, distributeddatabase capabilities, data management platform infrastructure,enterprise database resources (including cloud and on premisesresources), and the like. In embodiments, the knowledge distributionsystem 16802 may use or integrate with or within various services, suchas identity management services, information management services,digital rights management services, information rights managementservices, cryptographic services, key management services, distributeddatabase services, and many others.

Referring to FIG. 174 , in embodiments, the knowledge distributionsystem 16802 may provide one or more collaboration APIs 17474 forfacilitating collaboration between users. The collaboration APIs may beconfigured to allow users to provide and share information to establisha shared set of data resources for collaboration, such as to provide ashared “ground truth” as to underlying facts, to establish a set ofalternative views regarding the underlying facts (e.g., to identifywhere there may be disagreement as to the ground truth or the absence ofinformation that is needed to establish shared understanding), tofacilitate management of a set of scenarios with respect to whichcollaboration is desired, to facilitate a set of simulations relating totopics of interest for collaborators, to facilitate controlled access toshared and non-shared knowledge elements, and/or to allow users toprovide, verify, and/or share information outside of enterprisefirewalls. The collaboration API 17474 may be configured to allow usersand/or parties to provide, receive, share, and/or verify information,such as the digital knowledge 16804, information related to the digitalknowledge 16804, information related to transactions performed via thedistributed ledger 16808, via one or more smart contracts 16840, via themarketplace system 17454, and the like. The APIs may be configured toallow for sharing of information privately, publicly, or a combinationthereof. Information shared via the APIs, or events or transactionsrelating thereto, may be stored on the distributed ledger 16808 andthereby be distributed across the nodes 16916 of the distributed ledger.The users may include the knowledge providers 16806, the knowledgerecipients 16818, the crowdsources 16836, the users and/or parties tothe distributed ledger 16808 and/or the digital marketplace 17456, andthe like.

In some embodiments, the collaboration API 17474 may include operationaland/or situational knowledge that may be captured by the knowledgedistribution system 16802. The collaboration API 17474 may be configuredto process the situational knowledge and transmit the situationalknowledge and/or interpretations of the situational knowledge to theledger management system 16910. The ledger management system 16910 maybe configured to store the situational knowledge and/or interpretationsof the situational knowledge on the distributed ledger 16808. An exampleof situational knowledge is data regarding current condition, state,and/or location of a piece of collateral related to an instance of thedigital knowledge 16804 and/or related to a smart contract 16840.Another example of situational knowledge is a state of completion of awork-in-progress that is subject to a transaction, a term of paymentand/or lending triggered by completion of an item (e.g., an instance ofthe digital knowledge 16804) to a certain stage of completion.

In embodiments, the smart contract system 16868 may include one or moretransaction frameworks 17476 configured to facilitate managingtransactions via the smart contracts 16840. The transaction frameworks17476 may include one or more data structures, routines, subroutines,and the like configured to assist in management of transactions, such asby automatically importing, exporting, sorting, configuring, handling,or otherwise processing data related to transactions handled via theknowledge distribution system 16802. The smart contract system 17068 maybe configured to include one or more transaction frameworks 17476related to billing, payments, reporting, auditing, reconciliation,and/or the like.

In embodiments, each of the transaction frameworks 17476 may beconfigured to facilitate management of one or more particular types oftransactions. Examples of types of transactions and related data ofwhich one or more of the transaction frameworks 17476 may be configuredto facilitate management include purchase/sale, lending/leasing, rental,licensing, resource/time sharing, service contracts, maintenance/repair,warranty, guaranty, insurance, profit/revenue sharing, manufacturing(optionally tiered), resale/distribution (optionally tiered), demandaggregation, forward market/futures transactions, andconditional/contingent contracts, among others, including any of themany types describe in this disclosure and the documents incorporatedherein by reference. For example, in a tiered distribution contractframework, the transaction framework 17476 may be configured to use thedistributed ledger 16808 and the smart contract 16840 to allocate one ormore of payments, commissions, and costs in a granular manner. Inanother example, in a contingent contract framework, the transactionframework 17476 may be configured to use the distributed ledger 16808and the smart contract 16840 to manage one or more of options, futures,emergent events, and the like. Other examples of smart contractframeworks 17476 include those configured to manage commissions,incentive payments, payments for milestones (e.g., partial work,delivery partway through a supply chain, etc.), and escrows.

In embodiments, one or more of the transaction frameworks 17476 may beconfigured to facilitate management of the smart contracts 16840 insituations in which there are issues with performance by one or moreparties to an agreement. Issues with performance may include, forexample, breach of contract, failure to pay, late payment, poorperformance, poor quality goods, failure to perform services, and thelike. Remedies for issues that may be encoded in the transactionalframeworks 17476 may include pulling functionality, loss of license,ramping down of performance, financial penalties (e.g., loss of tokensor currency) and the like.

In embodiments, the transaction framework 17476 may facilitate using thedistributed ledger 16808 and the smart contract 16840 to allocate riskand liability in a granular manner. The knowledge distribution system16802 may be configured to import sensor data from one or more sensors,such as IoT sensors. The sensor data may include single sensor data,multiple sensor data, fused sensor data (e.g., where results from two ormore sensors are joined, such as by multiplexing, by computation, or thelike), raw sensor data, normalized sensor data (such as to allowcomparison to a scale, such as a quality scale, a condition scale, orthe like), calibrated sensor data (such as to allow comparison to othersensors on an accurate basis), and others. In embodiments, the sensordata may indicate the state or condition of a physical item or itsenvironment at a point in time or over a period of time, such as itstemperature, the ambient temperature of the environment in which it islocated, environmental humidity, movements of the item (such asresulting from impacts, vibration, transport, or the like), exposure toheat, exposure to radiation, exposure to chemicals (includingparticulates, toxins, and the like), bearing of loads, bearing ofweight, exposure to stress, exposure to strain, exposure to impacts,damage (such as dents, deformations, deflections, disconnects, breaks,cracks, shatters, tears and many others), exposure to biological factors(including pathogens), extent of progressive damage (such as rust), andother factors. In embodiments, the sensor data may indicate the presenceor absence of activities or workflows related to an item, such as wheresensor data indicates fluid levels (e.g., oil or other lubrication,fuels, antifreeze, and other fluids, which indicate whether requiredmaintenance, such as an oil change, has been timely performed), levelsof particulates or other matter (such as dirt, grime, sand particles,and many others, which may indicate whether required cleaning hasoccurred), levels of rust, and many others. In various embodiments, theimported sensor data may allow the smart contract system 17068 toallocate related to performance, lack of performance, utilization,deterioration, wear-and-tear, damage, maintenance activity, or otherrelevant factors that may be attributed to individual parts of a tieredmanufacturing system (including individual machines, equipment items,devices, component parts, or the like) to particular related parties viathe transaction framework 17476. As one example among many, a series ofparties in possession of an item may be allocated responsibility fordepreciation, deterioration, or other reduction in its value based ontheir measured activities with respect to its caretaking, such as theenvironmental conditions in which it was stored and the presence orabsence of required maintenance activities, such as fluid changes,cleaning, and the like. For example, a party that stored the item inpristine conditions at specified temperatures and replaced fluidsaccording to a defined schedule might be assigned a moderate number ofpoints (or other metrics), while a party that stored the item outdoorsin poor conditions might be assigned a much higher number of points, inautomatically allocating responsibility for the replacement of the itemby a smart contract. Similarly, a party whose actions or lack of actionscan be directly measured as causing damage (e.g., the item was droppedand dented while in the party's possession), may be automaticallyallocated responsibility for the damage. Thus, a sensor-enabled smartcontract may track and allocate responsibility for conditions andactivities involving a physical item across its lifetime, includingamong parties that share or transfer possession of the item, share useof the item, or the like. Shared use or possession over the lifetime mayinclude situations of tiered manufacturing, such as where componentparts are progressively configured into an overall system by a set ofparties. In such a situation, a smart contract may use sensor datacollected throughout manufacturing to determine responsibility for afailure of an item (e.g., a manufacturing defect) based on what part ofthe item failed and/or why an item failed (such as due to a problem inthe manufacturing chain). Shared use of possession may also include“shared economy” situations, such as shared use of property (includingrooms, office space, homes, apartments, real estate, vehicles,electronic devices, and many others), where a smart contract mayallocate responsibility for damage, maintenance (or lack thereof),cleaning, and other factors based on sensor data collected over thelifetime of the shared item. In embodiments, the smart contractframework 17476 may also provide for inclusion of indemnity clauses andmore complex causes allocating liability and/or limitation thereof(including exceptions to the same) which may include factors related tothe sensor data collected over time as noted above. For example, a smartcontract may limit a manufacturer's liability for defects to a period(e.g., ninety days, one year, or the like) but the smart contract mayembody an exception for hidden defects (e.g., ones that were present butdid not manifest during the warranty period). Sensor data may indicatewhether a defect was manifested or not during the base warranty periodand automatically determine whether a warranty claim asserted after theperiod is valid. In embodiments, such a smart contract may furtherallocate, and optionally execute a transfer of value, such as currency,upon determination of the ultimate responsibilities among parties, suchas where one party has indemnified another for a type of liability. Inembodiments, a smart contract may be configured to perform a computationand allocation of net liability among multiple parties to a contractthat involves indemnification by one or more parties of another. Inembodiments, such a smart contract may consume sensor data that is usedto determine the extent of liability to be allocated to each party(e.g., where a party's actions or inactions may result in sensed changesof the condition of an item that may trigger greater responsibility forindemnification of others). In embodiments, such a smart contract mayautomatically credit or debit an account, trigger a transfer of value,or the like.

In embodiments, the transaction framework 17476 may facilitate using thedistributed ledger 16808 and the smart contract 16840 to allocatepayments, commissions, costs, and the like in a granular manner. Thetransaction framework 17476 may facilitate inclusion in the smartcontract 16840 and management by the smart contract 16840 of one or moreparties to a set of distribution agreements, value added reselleragreements, manufacturer agreements, sub-distributor agreements,sub-licensee agreements, payment agreements, servicing agreements,maintenance agreements, update agreements, upgrade agreements, rentalagreements, resource sharing agreements, item-sharing agreements,warranty agreements, insurance agreements, lending agreements,indemnification agreements, guarantee agreements, and the like.

In embodiments, the transaction framework 17476 may facilitatemanagement and/or execution of contingent contracts via the distributedledger 16808 and the smart contract 16840. The contingent contracts mayinclude clauses whereby a provision of a good, service, payment, and thelike is contingent on one or more triggering events taking place. Forexample, admission tickets for a sporting event may be sold to a fans ofa plurality of sports teams, with validity of the ticket and/or therelated transaction being contingent on the team of which the fan is afan of being eligible to participate in the sporting event. Inembodiments, the transaction framework 17476 may have one or more datacollection facilities, such as web crawlers, spiders, clusteringsystems, sensor data collection systems, services, APIs, or the likethat collect data indicating the presence or absence of a triggeringcondition for the contingent contract, such as, in the example of anevent-triggered contract, a system that searches for the existence of anevent involving a particular performer, player, or team (among others)in an event, and the smart contract may automatically handle theallocation of rights that are triggered by the occurrence of the event.For example, where the right to attend the Super Bowl (or other game) istriggered by a particular team's presence in the game (or in similarexamples where an attendance right is triggered by emergence orrealization of a desired instance of a type of event), the smartcontract transaction framework 17476 may automatically determine (suchas via a search engine or other capability operating on news datasources) the triggering event (e.g., that a given team won a conferencechampionship game resulting in becoming a participant in the leaguechampionship, or other similar example, or the a particular performer orgroup has announced a date and place for a concert tour or otherperformance). Further, the smart contract transaction framework 17476may trigger a set of actions upon the automatic determination of theinstance of the triggering event, such as transferring ticket rights toparties for whom the rights vest upon the event, informing other partiesthat their contracts are closed (i.e., that there remains no possibilityof the event occurring under the defined conditions for those parties,such as holders of rights related to teams that did not make it to thechampionship games), allocating consolation prizes to losing parties,triggering other smart contracts (such as smart contracts that allocateprovision of related goods and services, such as travel andtransportation services (e.g., automatically securing airline ticketsbased on the location of the ticket holder and the location of the game,automatically securing a rental car, and the like), hospitality services(e.g., automatically securing a hotel reservation in the city of thegame for a fan that does not live in the city, automatically securingreservations for meals, and the like).

In embodiments, a smart contract for an event embodying automaticdetection of triggers for contingencies related to the emergence orrealization of an instance of an event, and embodying automaticallocation of rights (such as attendance rights, travel and hospitalityrights, and the like) based on the triggers may include, take inputfrom, use, connect with, link to, and/or integrate with a set ofintelligent agents, such as using any of the artificial intelligence,machine learning, deep learning, and other techniques described hereinor in the documents incorporated by reference herein, including roboticprocess automation trained and/or supervised by human experts. The setof intelligent agents may include ones that are trained, for example,(a) to determine and manage a set of possible events (such as what teamscould be involved in what games at what locations and what points intime across a set of leagues, sports, locations and the like), includingexpanding or pruning the list based on game results and other factors(e.g., where teams fall out of contention for playoff spots, renderingpreviously possible games impossible); (b) to forecast probabilities oflikelihood of instances of event based on current and historical data(e.g., the likelihood that a game will occur between two particularteams, the likelihood that a performer will hold a concert at a givenlocation (or within a geofence) during a date range, or the like); (c)to generate and configure a smart contract that governs allocation ofrights subject to contingencies, including setting parameters for thelaunch (e.g., by auction, by lottery, by “drop” or the like) of amarketplace or other venue by which parties may enter into the smartcontract for the contingent event; (d) to forecast demand for aninstance of a contract (e.g., demand for Final Four tickets in NewOrleans if University of Kentucky is playing; or demand for Elton Johntickets in Paris during Q3 of a given year; among many others) based ona given type of contingent event, such as based on historical demand forsimilar events (optionally using various clustering and similaritytechniques operating on historical attendance data, secondary marketticket data and other data sets), expressed demand (including demandexpressed in demand aggregation contracts, such as where some users havepurchased options for the event or similar events), historical data oncontingent smart contracts for similar items or services, secondaryindicators of demand (such as search engine metrics, social mediametrics and the like) and many others; (e) to set initial pricing forevents, including based on the forecast demand and historical pricingdata for underlying events (e.g., ticket prices, secondary market pricesand activity levels, time required to sell out tickets and the like) andfor other contingent event contracts; (f) to manage allocation of smartcontracts, such as in tranches of release; (g) to collect and manageparty-specific factors and user profiles, such as understanding locationfactors (e.g., place of residence, place of work), affinity and loyaltyfactors (favorite teams, favorite restaurants, favorite airlines,favorite hotel chains, favorite types of food, and others), and others;(h) to manage matching of party-specific factors and user profiles tocontingent events (such as to find and present smart contractopportunities that fit a user's profile, such as ones involvingpossibilities of attending events with a favorite team, player orperformer involved); and (i) to manage discovery and presentation of,and configuration of parameters for, smart contracts that embody othergoods and services that may be paired with a contingent event smartcontract (such as automatically finding and matching an appropriateairline flight, train reservation, bus ticket or the like andconfiguring a contingent smart contract for the same between thetransportation provider and the prospective event ticket holder;automatically finding and matching an appropriate hotel reservation andconfiguration a smart contract hotel reservation between the prospectiveticket holder and the hotel provider, or creating similar contingentsmart contracts among providers and prospective ticket holders for othertravel, accommodations and hospitality packages, such as restaurantreservations, rental cars, and many others); among others. Thus, the setof AI-enabled intelligent agents may provide automation of variouscapabilities for enabling the creation, hosting, provisioning, andresolution of a marketplace for contingent event smart contracts.

In embodiments, the transaction framework 17476 may facilitate demandaggregation via the distributed ledger 16808 and the smart contract16840. The transaction framework 17476 may aggregate demand for one ormore products and/or services accumulated via analytics, commitments,options, or any other suitable source. Upon accumulation of demand, suchas by a demand metric meeting a demand threshold, the smart contract16840 may trigger to begin design, manufacturing, distribution, and/orthe like of the related product and/or service. In embodiments, a set ofintelligent agents, using various AI capabilities noted above, may beconfigured facilitate demand aggregation, including agents that may (a)forecast demand for an instance or type of product, such as based onvarious secondary indicators of demand, such as search engine metrics,chat activity (e.g., in relevant forums), event information (such asattendance at relevant industry events), social media information (suchas numbers of posts), product sales, historical selling times (e.g.,time from product launch to selling out of a product), and many others;(b) aggregate demand, such as by configuring a set of smart contracts bywhich parties commit to purchasing an item upon its instantiation, suchas during a given time window within a given price range and determininga total aggregate demand; (c) projects the cost a demand-aggregatedoffering, such as based on a model (optionally itself managed and/orcreated by an intelligent agent) that indicates the projected cost of anitem at various volumes of production, optionally based on a projectionor model of the likely component parts and cost thereof, as well asother costs, such as assembly, transportation, financing, warranty andthe like; (d) projects the price of the demand-aggregated item atvarious volumes of offering, such as based on the forecast demand andhistorical pricing; (e) forecasts the profit likely to be associatedwith offering the demand-aggregated item at various volumes ofproduction and/or at various points in time, such as using theforecasted demand, cost and pricing information; and the others. Thus ademand aggregation marketplace may be enabled and/or supported byautomation capabilities provided by the set of intelligent agents.

In embodiments, the smart contract system 16868 may be configured toimport patterns of implementation and/or systems building knowledge intoone or more of the transaction frameworks 17476. The patterns ofimplementation and/or systems building knowledge may include, forexample, knowledge systems, workflow, product management, support calls,human interaction, social media, redundant systems, data storage, andimplementation patterns at scale.

The smart contract system 16868 may automatically configure the smartcontracts 16840 to implement the imported patterns of implementationand/or systems building knowledge. The imported patterns ofimplementation and/or systems building knowledge may be stored in thedatastore 16858.

In some embodiments, the knowledge distribution system 16802 may includean artificial intelligence (AI) system 17480 in communication with theledger management system 16910 and/or the smart contract system 17468and configured to perform AI-related tasks according to a machinelearned model. The AI system 17480 may be configured to perform actionswith respect to the knowledge distribution system 16802 to manage thedigital knowledge 16804. The AI system 17480 may have permission andaccess rights to manage, use, and interact with systems of the knowledgedistribution system 16802 similarly to a user.

In embodiments, the AI system 17480 may be trained by one or moretransaction experts to develop the machine learned model by which the AIsystem 17480 operates to perform AI-related functions. Examples oftransaction experts that may at least partially train the AI system17480 include agents, brokers, traders, attorneys, financial advisors,auditors, accountants, bankers, marketers, advertisers, exchangeoperators, buyers, sellers, distributors, and manufacturers/developers.The AI system 17480 may be trained by any suitable machine learningalgorithm, and by any suitable training data set. Examples of machinelearning algorithms include supervised learning, unsupervised learning,semi-supervised learning, reinforcement learning, self-learning, featurelearning, sparse dictionary learning, anomaly detection, robot learning,and association rules. The machine learned model may be any suitabletype of model, such as an artificial neural network, a decision tree, asupport vector machine, a regression analysis model, a Bayesian network,or a genetic algorithm.

In embodiments, the AI system 17480 may be trained to identifyopportunities for smart contracts. Examples of opportunities includeexchange opportunities and arbitrage opportunities.

In embodiments, the AI system 17480 may be trained to configure marketcontract terms and conditions.

In embodiments, the AI system 17480 may be trained to monitor marketconditions.

In embodiments, the AI system 17480 may be trained to monitor and managecontract terms and conditions. Monitoring and managing contract termsand conditions may include monitoring goods and/or observing services.

In embodiments, the AI system 17480 may be trained to monitor and managetransaction processes. For example, the AI system 17480 may be trainedto recognize release of funds from an escrow account.

In embodiments, the AI system 17480 may be trained to monitorcounter-party information. Examples of counter-party information includesolvency, and status of performance, and quality of performance.

In embodiments, the AI system 17480 may be trained to identifytransaction opportunities. Examples of transaction opportunities includeinstances of exchange and arbitration opportunities.

In embodiments, the AI system 17480 may be trained to negotiate onbehalf of parties to transactions involving digital knowledge.

In embodiments, the AI system 17480 may be configured to configure andexecute auctions. The AI system 17480 may perform auction-relatedactions such as selecting a type of auction suitable for a transactionand/or settings rules and parameters for an auction to be at leastpartially carried out on the distributed ledger 16808. The auctionsselected may be any suitable type of auction for being at leastpartially carried out on the distributed ledger 16808, such as a Dutchauction or a reverse auction.

In embodiments, the AI system 17480 may be configured to distributecurrency tokens and/or tokenized digital knowledge 16804.

In embodiments, the AI system 17480 may be configured to configure andmanage exchange of the digital knowledge 16804 across differentmarketplaces and exchanges. The AI system 17480 may set exchange ratesbetween native currencies of exchanges and/or may tokenize the digitalknowledge 16804 and set exchange rates between instances of thetokenized digital knowledge 16804.

In embodiments, the AI system 17480 may be configured to establish,monitor, and/or negotiate payment, leasing, and/or lending optionsrelated to management of the digital knowledge 16804. The lendingoptions may include payment plans, trust-less scenarios, and/ornon-trust-less scenarios.

In embodiments, the knowledge distribution system 16802 may include arobotic process automation (RPA) system 17482 in communication with theAI system 17480 and configured to improve one or more functions of theAI system 17480. The RPA system 17482 may use robotic process automationtechniques to allow the AI system 17480 to interface with one or more ofsystems of the knowledge distribution system 16802, the distributedledger 16808, and systems, marketplaces, and/or exchanges and the likeexternal to the knowledge distribution system 16802 by performingactions in one or more graphical user interfaces of the knowledgedistribution system 16802, the distributed ledger 16808, and systems,marketplaces, and/or exchanges and the like external to the knowledgedistribution system 16802.

In embodiments, the knowledge distribution system 16802 may include arights management system 17484 configured to manage rights of usersapart from an exchange or marketplace.

In embodiments, the knowledge distribution system 16802 may include amarket management system 17486 configured to establish a market forselling and/or reselling currency tokens and/or instances of the digitalknowledge 16804. The market may be configured such that wrapped and/ortokenized instances of the digital knowledge 16804 may be resold withoutbeing unwrapped. The market may be established and configured as a spotmarket, a secondary market, and/or a futures/derivatives market. Futuresand derivatives resold on the futures/derivatives market may includeoptions, futures, and other derivatives. The knowledge distributionsystem 16802 may establish and/or monitor secondary markets, ancillarymarkets, forward markets, and the like in addition to resale markets fordigital currency and instances of the digital knowledge 16804.

In embodiments, the market management system 17486 may be configured tomonitor metrics of users, buyers, and/or sellers participating in one ormore markets established by the market management system 17486. Themetrics may include, for example, metrics indicating how, where, or howoften an instance of the digital knowledge 16804 is used. The metricsmay alternatively or additionally include metrics regarding creation ofthe digital knowledge 16804, duration during which a given type ofdigital knowledge 16804 remains relevant or valuable, and/or metricsregarding transaction patterns. Examples of transaction patterns includesize of transaction, transaction pricing and trends thereof, profileinformation of buyers, sellers, consumers, users, and/or creators of thedigital knowledge 16804, and the like. Another metric additionally oralternatively monitored by the markets system may include metricsindicative of gaming and/or misconduct by users of the market.

In embodiments, the digital knowledge 16804 may include instruction setssuch as: process steps in food production or food preparationinstructions (e.g., for industrial food preparation), additivemanufacturing/3D printing instructions, instruction sets for surgicalrobots and human/robot interfaces generally, crystal fabrication systeminstructions, crystal fabrication process instructions, polymerproduction process instructions, chemical synthesis processinstructions, coating process instructions, semiconductor fabricationprocess instructions, silicon etching instructions, doping instructions,chemical vapor deposition instructions, biological production processinstructions, smart contract instructions, and/or instructions forestablishing, updating, and/or verifying a chain of work, possession,title, and the like.

In embodiments, the digital knowledge 16804 may include code, software,and/or logic, such as: algorithmic logic, instruction sets for use in anapplication, executable algorithmic logic, computer programs, firmwareprograms, instruction sets for field-programmable gate arrays,instruction sets for complex programmable logic devices, serverless codelogic, cryptography logic, AI logic, AI definitions, machine learninglogic and/or definitions, and/or quantum algorithms.

In embodiments, the digital knowledge 16804 may include digitaldocuments, such as digital documents relating to: part schematics,production records (e.g., for aircraft parts, spaceship parts, nuclearengine parts, and the any/or any other suitable part), automobile parts,airplane parts, pieces of furniture or components thereof, replacementparts for industrial robots or machines, trade secrets, and/or otherintellectual property such as know-how, patented material, and/or worksof authorship.

In embodiments, the digital knowledge 16804 may include 3D printingschematics, such as schematics for printing medical devices, automobileparts, airplane parts, furniture, furniture components, and/orreplacement parts for industrial robots or machines.

In embodiments, the digital knowledge 16804 may include personal and/orprofessional knowledge relating to one or more organizations and/orindividuals. The personal and/or professional knowledge may include:professions resumes, professional history tracking information, recordsof professional credentials, academic degrees, professionalcertificates, verifications of professional positions held by one ormore individuals, professional feedback, verification of work performedby one or more individuals and/or parties, personal financial history,business financial history, and/or personal life achievements asverified by one or more third parties.

In some embodiments, the digital knowledge 16804 may include data setsand/or sensor information defining and/or population a set of digitaltwins. The digital twins may embody one or more instances of the digitalknowledge 16804 relating to one or more physical entities. The one ormore instances of the digital knowledge 16804 may include knowledgerelated to one or more of configurations, operating modes, instructionssets, capabilities, defects, performance parameters, and the like.

In embodiments, the knowledge distribution system 16802 may beconfigured to transmit instances of the digital knowledge 16804 toand/or receive instances of the digital knowledge 16804 from one or moreexternal knowledge exchanges and/or knowledge databases. The externalknowledge exchanges and/or knowledge databases include domain-specificexchanges, geography-specific exchanges, and the like. The knowledgedistribution system 16802 may be configured to facilitate exchange ofvaluable or sensitive instances of the digital knowledge 16804 relatedto the subject matter of the external knowledge exchange and/orknowledge database. Additional or alternative examples of externalknowledge exchanges and/or databases may include stock exchanges,commodities exchanges, derivative exchanges, futures exchanges,advertising exchanges, energy exchanges, renewable energy creditsexchanges, cryptocurrency exchanges, bonds exchanges, currencyexchanges, precious metals exchanges, petroleum exchanges, goodsexchanges, services exchanges, or any other suitable type of exchangeand/or database. The knowledge distribution system 16802 may integrateand/or communicate with interfaces of external knowledge exchangesand/or databases, such as APIs connectors, ports, brokers. Theintegration and/or communication may be facilitated via one or more ofextraction, transformation, and loading (ETL) technologies, smartcontracts, wrappers, tokens, containers, and the like.

In embodiments, the knowledge distribution system 16802 may be deployedon and/or integrated with or within a set of infrastructurecapabilities. Examples of infrastructure capabilities include cloudcomputing infrastructure, platform-as-a-service infrastructure, Internetof Things platform capabilities, distributed database capabilities, datamanagement platform infrastructure, enterprise database resources(including cloud and on premises resources), and the like.

In embodiments, the knowledge distribution system 16802 may use and/orintegrate with or within various services. Examples of services withwhich or within which the knowledge distribution system 16802 mayintegrate include identify management services, information managementservices, digital rights management services, information rightsmanagement services, cryptographic services, key management services,distributed databased services, and the like.

In some embodiments, the knowledge distribution system 16802 may beconfigured to facilitate creation, management, and execution ofcontingent event contracts based on demand aggregation. The contingentevent contracts may be, for example, smart contracts 16840 havingcontingent events based on demand aggregation. The knowledgedistribution system 16802 may collect demand aggregation by havingcrowdsources 16836 indicate demand for an instance of digital knowledge16804. The crowdsources 16836 may indicate demand for an instance ofdigital knowledge 16804 by, for example, contributing currency towarddevelopment or generation of the digital knowledge 16804. The knowledgedistribution system 16802 may facilitate indication of demand, such asby establishing a website, app, or other service that collectsindications of demand. The knowledge distribution system 16802 mayadditionally or alternatively aggregate demand indicated by third partysources, such as by websites, apps, or other systems or servicesexternal to the knowledge distribution system 16802.

In some embodiments, the knowledge distribution system 16802 may beconfigured to create a point in time or reference knowledge dataset viatokenization of one or more instances of the digital knowledge 16804.For example, one or more event contracts may be triggered by a set ofevents based on one or more instances of digital knowledge 16804. Theknowledge distribution system 16802 may input a tokenized instance ofthe digital knowledge 16804 as a trigger to the event contract. Theknowledge distribution system 16802 stores the tokenized instance ofdigital knowledge on the distributed ledger 16808 as a historicallytrackable digital asset, thereby creating a fungible record of tokenizedknowledge.

In some embodiments, the knowledge distribution system 16802 facilitatetimestamped input and output control of instances of the digitalknowledge 16804. The knowledge distribution system 16802 may beconfigured to facilitate sale of instances of the digital knowledge16804 as well as tokenized instances of the digital knowledge indicativeof times and/or references related to the digital knowledge 16804. Forexample, as a plurality of instances of digital knowledge 16804 aredeveloped, created, and/or stored. Tokenized instances of the digitalknowledge 16804 may be stored on the distributed ledger 16808 withimmutable time references. The knowledge distribution system 16802 mayfacilitate sale, lease, or other transactions of the timestamped tokensin addition to or alongside execution of one or more smart contracts forthe sale, lease, or other transaction related to sale of the instancesof digital knowledge 16804 around which the timestamped tokens weregenerated and stored.

In some embodiments, knowledge token 17038 is a non-fungible token.Non-fungible token or NFT represents a digital knowledge asset that isunique, or one-of-a-kind and has at least a unique identifier, and/orother distinguishable asset-specific information. The instances ofdigital knowledge may be referred to as “digital knowledge assets.” TheNFT may be used, at least in part to signify an ownership of the asset.In embodiments, the NFT can represent any percentage of the ownershiprights including complete ownership rights to a small fractionalownership of a digital knowledge asset. The extent of the rightsincluding access, licensing, ownership and/or other suitable rightsassociated with the NFT may be specified by the knowledge provider inthe smart contract. For example, a patent assignee may mint a patent NFTwith complete ownership rights including the right to sue forinfringement. Further, NFTs may be coded to contain exhaustive, publiclyverifiable metadata and data about transaction history.

In embodiments, the knowledge token 17038 may be implemented as anon-fungible token (NFT) on Ethereum blockchain using technical standardERC-721 (where “ERC” stands for Ethereum Request for Comment). ERC-721is a standard interface used to create, track, and manage non-fungibletokens in the Ethereum blockchain. In ERC-721, each token is completelyunique and non-interchangeable with other tokens, and thus non-fungible.The ERC-721 standard outlines a set of common rules that all tokens mayfollow on the Ethereum network to produce expected results. Further, thestandard may stipulate characteristics about a how token ownership isdecided, how are tokens created, how are tokens transferred, and how aretokens burned.

In embodiments, the NFT representing digital knowledge asset may betraded, licensed, sold, auctioned, or otherwise monetized at a NFTmarketplace or exchange. Some examples of such marketplaces includeOpensea, Rarible, Foundation, Atomic hub, and the like. An owner of anNFT representing a digital knowledge asset may upload the NFT and/or themetadata associated with the asset on the NFT marketplace.

As one example, a patent may be tokenized as a dynamic NFT where thevalue/price of the NFT is dynamic during the lifetime of the patent andcan change based on real-world events like legal or transactionalevents. Some examples of events that may affect the NFT value/priceinclude prosecution events, invalidity or litigation events, andlicensing or sale events. An owner or assignee of a patent (knowledgeprovider) may choose to monetize the patent by offering one or more NFTsto be traded, licensed, sold, or auctioned at an NFT marketplace orexchange. The assignee may upload a digital representation of the NFTand/or the metadata associated with the patent. For example, if thepatent is an issued patent, an image of the front page of the patentincluding the patent number, title, abstract, one or more figures, etc.may be uploaded. Additionally, price, ownership, and trading historyalong with smart contract terms may be provided. The following is anexample template for a smart contract for a patent NFT:

Whereas, ABC LLC, located at 123 XYZ (“Seller”) owns the interest inU.S. Pat. No. ______ (the “Patent”) assigned to it through the inventorassignment recorded with the US Patent and Trademark Office at Frame______ (USPTO Patent Assignment Search).

Whereas, ______ located at ______ (“Buyer”) wishes to acquire allSeller's right, title, and interest in and to the Patent.

Whereas, Seller has received from Buyer proceeds from the winning bid inthe NFT Auction (the “Auction Consideration”).

ASSIGNMENT. For receipt of the Auction Consideration, Seller herebysells, assigns, transfers, and sets over to Buyer Seller's entire right,title, and interest in and to the Patent, including Seller's right tosue for, settle and release past, present, and future infringement ofthe Patent.

Signature: ______

Smart contract 16840, ensure that the ownership of the patent NFT may bechanged quickly, efficiently, securely, and transparently. Patent NFTs,an example for which is provided above, enable efficient first-salepurchases, secondary market trading, collateralized borrowing/lending,and insurance markets for the patents, thereby adding liquidity,efficiency, and transparency to an otherwise illiquid and opaque market.

FIG. 197 is a diagrammatic view illustrating an example implementationof the knowledge distribution system including a trust network 19702 foridentifying the likelihood of fraudulent transactions using a consensustrust score and preventing such fraudulent transactions according tosome embodiments of the present disclosure.

The trust network 19702 is configured to identify the likelihood offraudulent transactions on the ledger network 16970 by generating andtracking a consensus trust score for the nodes of the network. Theconsensus trust scores can offer transactors in the ledger network 16970a safeguard against fraud while preserving user anonymity and autonomy.The consensus trust score may be generated based on data retrieved fromvarious data sources (e.g., fraud/custody data) along with dataretrieved from the nodes of the ledger network 16970. The consensustrust score may be a number (e.g., a decimal or integer) that indicatesa likelihood that the blockchain address is involved in fraudulentactivity. Put another way, a trust score can represent the propensity ofa blockchain address to be involved with fraudulent activity.

Any party to a transaction (e.g., knowledge provider 16806) may requestthe consensus trust score from the trust network 19702 before engagingin a transaction in which funds (e.g., tokens) are transacted on theblockchain. In general, a party to a transaction can use a consensustrust score to determine whether the blockchain address with which theyare transacting is trustworthy. The transacting parties may use theconsensus trust scores to take a variety of actions. For example,parties may use consensus trust scores to determine whether to proceedwith or cancel a transaction. As another example, parties (e.g., digitalexchanges) can use consensus trust scores to determine whether to insurea transaction. As such, the consensus trust scores described herein canhelp protect parties from falling victim to fraud or from receivingfraudulent funds. Note that the consensus trust scores inform thetransactors of the degree to which any cryptocurrency address may betrusted without requiring the transactor to know the identity of theparty behind the address.

The trust network 19704 may include a plurality of trust nodes 19704-1,19704-2, . . . , 19704-N (referred to herein as “nodes”). Each of thenodes may include one or more node computing devices (e.g., one or moreserver computing devices) that implement a variety of protocolsdescribed herein. The nodes 19704-x may acquire data associated withblockchain addresses and determine a variety of trust scores based onthe acquired data. A trust score determined locally at a node based onthe acquired data may be referred to as a “local node trust score” or a“local trust score.” The nodes 19704-x may be configured to communicatetheir local trust scores among one another such that each node may haveknowledge of local trust scores associated with other nodes. After anode acquires a plurality of local trust scores, the node may determinea candidate consensus trust score (hereinafter “candidate trust score”)based on the plurality of local trust scores. One or more nodes maydetermine a consensus trust score based on the plurality of candidatetrust scores. The consensus trust score may indicate a consensus valuefor a local trust score among a plurality of nodes. The consensus trustscore for a cryptocurrency address can be written to a distributedconsensus ledger and later retrieved from the trust network 19704 (e.g.,in response to a trust request).

In some implementations, the consensus trust score may be determinedbased on an average (e.g., a blended average) of the candidate trustscores. For example, the consensus trust score may be determined byusing a statistically weighted average of candidate trust scores basedon count.

The trust scores described herein (e.g., local, candidate, or consensus)can be calculated/provided in a variety of formats. In someimplementations, a trust score may be an integer value with a minimumand maximum value. For example, a trust score may range from 1-7, wherea trust score of ‘1’ indicates that the blockchain address is likelyfraudulent. In this example, a trust score of ‘7’ may indicate that theblockchain address is not likely fraudulent (i.e., very trustworthy). Insome implementations, a trust score may be a decimal value. For example,the trust score may be a decimal value that indicates a likelihood offraud (e.g., a percentage value from 0-100%). In some implementations, atrust score may range from a maximum negative value to a maximumpositive value (e.g., −1.00 to 1.00), where a larger negative valueindicates that the address is more likely fraudulent. In this example, alarger positive value may indicate that the address is more likelytrustworthy. The customer may select the trust score format they prefer.

The trust network 19704 described herein distributes the trust scorecomputational workload across a plurality of nodes to produce aresilient network that is resistant to failure/outage and attack. Insome implementations, the trust network 19704 may include a built-intransactional autonomy moderated by a token that allows the trustnetwork 197304 to distribute the computational workload. Additionally,distributing trust calculations throughout the network may provide aresistance to fraud/conspiracy intended to corrupt the network.

The transactor device 19706 is any computing device can interact withthe trust network 19704. Example transactor devices may includesmartphones, tablets, laptop computers, desktop computers, or othercomputing devices. The transactor device 19706 may include an operatingsystem and a plurality of applications, such as a mobile application, aweb browser application, a decentralized application, and additionalapplications.

The transactor device 19706 may include an interface for an intermediatetransaction system 19708 (e.g., a web-based interface and/or aninstalled transaction application 19710). In certain implementations,the intermediate transaction system 19708 implemented on one or moreserver computing devices, may communicate with the ledger network 16970,transactor devices 19706, and the trust network 19704 and performcryptocurrency transactions on behalf of the transactor devices 19706.The intermediate transaction system 19708 may also acquire consensustrust scores from the trust network 19704 on behalf of the transactordevices 19706. An example intermediate transaction system 19708 mayinclude a digital currency exchange (e.g., Coinbase, Inc). In someimplementations, exchanges may be decentralized.

The transaction application 19710 on transactor device 19706 may alsotransact with the ledger network 16970 to perform blockchaintransactions. The transaction application 19710 may also requestconsensus trust scores from the trust network 19704. Some exampletransaction applications may be referred to as “wallet applications.”

The transactor device 19706 can send trust requests to the trust network19704 and receive trust responses from the trust network 19704. Thetrust request may indicate one or more cryptocurrency blockchainaddresses for which the transactor device 19706 would like a trustreport (e.g., one or more consensus trust scores). In someimplementations, the trust request can include a request payment, suchas a blockchain token and/or fiat currency (e.g., United StatesDollars). The request payment may be distributed to nodes in the trustnetwork 19704 as payment for providing the consensus trust score(s).

In one example, transactor device 19706 can send a trust request to thetrust network 19704 and receive a trust response (e.g., trust report)from the trust network. The transactor device 19706 and the trustnetwork 19704 may communicate via an application programming interface(API). The trust request may include a cryptocurrency blockchain addressfor the transactor on the other side of the transaction. For example, atrust request from a knowledge provider 16806 may request a trust reportfor the knowledge recipient's 21918 blockchain address. The knowledgeprovider 16806 may make a decision based on the received trust report,such as whether to engage in the cryptocurrency blockchain transactionwith the knowledge recipient 16818.

In some implementations, the trust network 19704 may implement a fraudalert protocol that can automatically notify network participants (e.g.,fraud alert requesting devices) of potentially fraudulent cryptocurrencyblockchain addresses. For example, a node may include a fraud alertmodule configured to provide fraud alerts under a set of fraud alertcriteria that may be configured by a user. In one example, the fraudalert module may monitor one or more cryptocurrency addresses andprovide a fraud alert if the consensus trust score for any address dropsbelow a threshold level of trustworthiness (e.g., as set by a user). Inanother example, a fraud alert may be sent if a monitored trust scorechanges by more than a threshold percentage. In some implementations, afraud alert protocol may be implemented using a smart contract thatmonitors a consensus trust score and provides alerts according to a setof business rules that may be defined by a user.

In some implementations, the trust network 19704 may implement areputation protocol that calculates and stores reputation values. Thereputation values for a node may indicate a variety of parametersassociated with the node, such as an amount of work the node performedduring trust score calculations and distribution, the quality of thework performed (e.g., the accuracy), and the consistency of nodeoperation (e.g., node uptime). The reputation values may be used byother protocols in the trust network 19704. For example, nodes maydetermine candidate and/or consensus trust scores based on thereputation values associated with one or more nodes. As another example,the nodes may be awarded and/or punished according to their reputationvalues.

In some implementations, the reputation value may be a function of theamount of work a node performs with respect to calculating trust scores.For example, the reputation value for a node may be based on the numberof local trust scores calculated, the number of candidate trust scorescalculated, and the amount of work related to calculating consensustrust scores. In some implementations, the reputation value may be afunction of the quality of the calculations performed by the nodes. Forexample, the quality reputation values may be based on a number of trustscore outliers produced by the nodes and how fast trust scores wereproduced. In some implementations, the reputation value may be afunction of node parameters, such as node bandwidth, node processingpower, node throughput, and node availability. Example reputation valuesassociated with node availability may be based on uptime values, meantime between failure (MTBF) values, and/or mean time to repair (MTTR)values. In some implementations, the reputation value may be a functionof the amount of data (e.g., historic data) stored at a node and theamount of time for which the data is stored. In some implementations,the reputation value may be a function of the amount staked by a node.In some implementations, the reputation value may be a compositereputation value, which may be a function of any individual reputationvalues described herein. For example, a composite reputation value maybe a weighted calculation of one or more component reputation values.

FIG. 198 illustrates an example method that describes operation of anexample trust network illustrated in FIG. 197 . For example, the methodof FIG. 198 illustrates the determination of local trust scores,candidate trust scores, and a consensus trust score for a singlecryptocurrency blockchain address. The method of FIG. 198 may beperformed multiple times to determine local trust scores, candidatetrust scores, and consensus trust scores for multiple cryptocurrencyblockchain addresses.

At step 19800, the nodes in the trust network 19704 acquire and processfraud and custody data associated with a cryptocurrency address. Examplefraud and custody data may include data that provides evidence of fraudwith respect to a cryptocurrency address and/or indicates the party thatowns/controls the cryptocurrency address. At step 19802, the nodesacquire and process cryptocurrency blockchain data associated with thecryptocurrency address. Example cryptocurrency blockchain data mayinclude data for a plurality of blockchain transactions between aplurality of different cryptocurrency addresses. At step 19804, thenodes each determine local trust scores for the cryptocurrency addressbased on the data acquired in blocks 19800-19802.

Different nodes may compute the same/similar local trust scores in caseswhere the different nodes have access to the same/similar cryptocurrencyblockchain data and fraud/custody data. In some cases, the local trustscores may differ among nodes. For example, the local trust scores maydiffer when nodes have access to different fraud and custody data. In aspecific example, nodes located in different jurisdictions (e.g.,countries) may have access to data sources that are blocked in otherjurisdictions. In another specific example, some nodes may accessinformation at different rates.

At step 19806, the nodes communicate the local trust scores for thecryptocurrency address with one another. After communication of thelocal trust scores, each of the nodes may include a plurality of localtrust scores calculated by other nodes. At step 19808, the nodes 100determine candidate trust scores for the cryptocurrency address based onthe local trust scores. The candidate trust score may for example, bedetermined by removing outlier local trust scores from the trust scorelist before determining the candidate trust score. In embodiments, thecandidate trust score may be determined based on an average (e.g., ablended average) of the remaining local trust scores in the trust scorelist 406. For example, the candidate trust score may be determined byusing a statistically weighted average of local trust scores based onnode count. At step 19810, the nodes in the trust network 19704determine a consensus trust score for the cryptocurrency address basedon the candidate trust scores for the cryptocurrency addresses. Theconsensus trust score may be determined in response to one or moreconsensus triggers associated with the candidate trust scores. Forexample, the consensus trust score may be determined if greater than athreshold number/fraction of candidate trust scores are in agreement(e.g., within a threshold variance). In some implementations, theconsensus trust score may be determined based on an average (e.g., ablended average) of the candidate trust scores. For example, theconsensus trust score may be determined by using a statisticallyweighted average of candidate trust scores based on count. At step19812, the nodes can update a distributed consensus trust score ledgerto include the calculated consensus trust score. At step 19814, 19816,the trust network 19704 receives a trust request for the cryptocurrencyaddress from a transactor device 19706 and sends a trust response,including the consensus trust score, to the transactor device 19706.

FIG. 199 is a diagrammatic view illustrating a transaction beingprocessed by the ledger network 16970 including a plurality of nodecomputing devices 16816. A knowledge provider 16806 desiring to send anydigital knowledge asset to a knowledge recipient 16818 via the knowledgedistribution system 16802 may send a transaction request 19902 to theknowledge distribution network 16802. In embodiments, the transactionrequest may include a request for determining the trustworthiness of theknowledge recipient 16818 before initiating the transaction. Theknowledge distribution system 16802 may send the trust request to thetrust network 19704 that determines the trust score 19904 for theaddress of the knowledge recipient 16818 and sends a trust response tothe knowledge distribution system. Assuming the trust score for theknowledge recipient 16818 is above a given threshold, the knowledgedistribution system 16802 provides the transaction request to the ledgernetwork 1990 and the transaction is broadcast 19906 throughout theledger network 16970 to the nodes 16816. In embodiments, each of theknowledge provider device 16890 and the knowledge recipient device 16894may have digital wallets (associated with the ledger network 16970) thatprovide user interface controls and a display of transaction parameters.Depending on the ledger network 16970 parameters the nodes verify thetransaction 19908 based on rules (which may be pre-defined ordynamically allocated). For example, this may include verifyingidentities of the parties involved, etc. The transaction may be verifiedimmediately, or it may be placed in a queue with other transactions andthe nodes 16816 determine if the transactions are valid based on a setof network rules.

In structure 19910, valid transactions are formed into a block andsealed with a hash. This process may be performed by validating nodesamong the participating nodes 16816. Validating nodes may utilizeadditional software specifically for validating and creating blocks forthe ledger network 16970. Each block may be identified by a hash (e.g.,256 bit number, etc.) created using an algorithm agreed upon by thenetwork. Each block may include a header, a pointer or reference to ahash of a previous block's header in the chain, and a group of validtransactions. The reference to the previous block's hash is associatedwith the creation of the secure independent chain of blocks.

Before blocks can be added to the distributed ledger, the blocks must bevalidated. Validation for the ledger network 16970 may include aproof-of-work (PoW) which is a solution to a puzzle derived from theblock's header. In embodiments, other protocols like proof-of-stake(PoS) or proof-of-authority (PoA) may be used for validating a block.Unlike the proof-of-work, where the algorithm rewards miners who solvemathematical problems, with the proof of stake, a creator of a new blockis chosen in a deterministic way, depending on the amount of digitaltoken assets held by the node, also defined as “stake.” (The more stakea node has, the more probability that the node will be chosen as a blockvalidator—for example, a node holding 1% of the tokens has a probabilityof 1% to validate a block). Then, a similar proof is performed by theselected/chosen node. If the validators are successful in validating andadding the block, proof-of-stake, in embodiments, will award successfulvalidators with a fee in proportion to their stake. Proof of Authority(PoA) on the other hand, leverages “authority” or “trust,” which meansthat block validators are not staking tokens/coins but their ownreputation instead. Therefore, PoA blockchains are secured by thevalidating nodes that are arbitrarily selected as “trustworthy”entities. PoA consensus does not require the nodes to spend vast amountof resources to compete with each other and has good energy efficiency,low network bandwidth usage and high throughput.

With validating 19912 with PoW, nodes try to solve the block by makingincremental changes to one variable until the solution satisfies anetwork-wide target. This creates the PoW thereby ensuring correctanswers. Here, the PoW process, alongside the chaining of blocks, makesmodifications of the blockchain extremely difficult, as an attacker mustmodify all subsequent blocks in order for the modifications of one blockto be accepted. Furthermore, as new blocks are mined, the difficulty ofmodifying a block increases, and the number of subsequent blocksincreases.

With validating 19912 with PoS, instead of investing in energy in a raceto solve or mine blocks, a ‘validator’ invests in tokens or coins of thesystem. This consensus is more energy efficient and, the fact thatminers do not have to solve a hard puzzle, allow higher throughputs.

With validating 19912 with PoA, a set of trusted nodes in the networksare chosen as validators. For example, a group of known and reputablenodes may be chosen or authorized by network administrators or voted bythe network. Only validators are entitled to validate the next block,and this validator is chosen randomly in a way that the same validatorcannot validate two blocks consecutively. A validator node that iscaught trying to forge the system may be removed from the validatorspool.

In some implementations, the “authority” of a node may be a function ofits consensus trust score and reputation as determined by the trustnetwork 19704.

While proof of work (PoW), proof of stake (PoS) and proof of authority(PoA) are described herein as consensus protocols for validating blocks,these are merely a few example consensus algorithms and are not intendedto be limiting. It will be understood that many different consensusprotocols may be implemented for the orchestration of transactions, andthe synchronization and validation of data in the ledger network 16970.The consensus protocol may be a protocol where nodes 16816 come to anagreement on data that may be written to the distributed ledger, so thatall nodes in the ledger network 16970 agree on the data- orstate-comprising the ledger. In other words, the consensus protocol mayoperate to keep all nodes on the ledger network 16970 synchronized witheach other, in terms of the state of the ledger network 16970. Someexamples of consensus protocols that may be utilized for validationinclude, without limitation, Delegated Proof of Stake (DPoS), Proof ofReputation (PoR), Proof of Burn (PoB), Proof of Elapsed Time (PoET),Proof of Space, Proof of Weight, Practical Byzantine Fault Tolerance(PBFT), Delegated Byzantine Fault Tolerance (DBFT), Federated ByzantineFault Tolerance (FBFT), Paxos, Raft, Tendermint, directed acyclic graph(DAG), or a hybrid of two or more of the aforementioned consensusprotocols.

With distribution 19914, the successfully validated block is distributedthrough the ledger network 16970 and all nodes 16816 add the block to amajority chain which is the ledger network's 16970 auditable ledger.Furthermore, the value in the transaction submitted by the knowledgeprovider device 16890 is confirmed with 19916 (deposited or otherwisetransferred to the digital wallet of the knowledge recipient device16894).

FIG. 200 is a diagrammatic view illustrating an example implementationof the knowledge distribution system including the digital marketplace16856 configured to provide an environment allowing knowledge providers16806 and knowledge recipients 16818 to engage in commerce relating tothe transfer of digital knowledge. A set of crowdsourcers 16836, 20002,20004, 20006, and 20018 with their respective devices 16892, 20008,20010, 20012 and 20014 may help facilitate such transactions andcommerce between the knowledge provider 16806 and the knowledgerecipient 16818.

The digital marketplace 16856 may provide an interface for all the usersof the knowledge distribution system including knowledge providers16806, knowledge recipients 16818, crowdsources 16836, 20002, 20004,20006 and third parties to perform one or more suitable marketplaceinteractions with the digital knowledge 16804. For example, the usersmay create a public profile for other users to see, interact with otherusers, transact for one or more pieces of the digital knowledge 16804(e.g., buy, sell, license, lease, bid on, and/or give away the digitalknowledge), verify source information and/or other information relatedto one or more pieces of the digital knowledge 16804, review or provideone or more services related to one or more pieces of the digitalknowledge 16804.

In embodiments, the digital knowledge 16804 may include intellectualproperty (e.g., patents, trade secrets, copyrights, trademarks, designs,know how, privacy rights, publicity rights, and others) and theknowledge distribution system 16802 helps perform and facilitate therecordation, collaboration, licensing and tracking of informationregarding such intellectual property. The knowledge distribution system16802 may thus provide a platform that can be utilized by all users forrecordings, buying, selling, or licensing transactions and payments,tracking and reputation management. In such embodiments, the knowledgeprovider 16806 may be the owner of the intellectual property (IP) andthe knowledge recipient 16818 may be the licensee of the intellectualproperty. The crowdsourcer 16836 may be an IP attorney, crowdsourcer20002 may be a domain expert (e.g. an expert in cognitive radio toassess IP or patents related to dynamic spectrum sharing), crowdsourcers20004 and 20006 may be experts in IP valuation. Some other examples ofcrowdsourcers may include inventors, technology developers, patent lawfirms, patent offices, IP service providers, writers, litigators, orother experts in technology, law, or finance. The parties to thetransaction i.e., the knowledge provider 16806 and the knowledgerecipient 16818 may employ the services of one or more the crowdsourcersto facilitate such transaction of IP.

In embodiments, the trust network 19704 provides trust scores to variousnodes including knowledge providers 16806, knowledge recipients 16818,crowdsources 16836, 20002, 20004 and 20006. The trust scores enablevarious parties to decide which parties to transact with. For example, aknowledge providers 16806 may choose one or more knowledge recipients16818 as well as one or more crowdsourcers based on their trust scores.

FIG. 201 is a diagrammatic view illustrating an example user interfaceof digital marketplace 16856 configured to enable transactions andcommerce between various users of the knowledge distribution system16802. User interface 20100 may be a web interface allowing an owner ofan intellectual property to offer the intellectual property forlicensing in the form of a non-fungible token (NFT) signifying somepercentage of ownership rights associated with the intellectualproperty. In the example, the intellectual property is a US patentapplication. The owner of the patent application ABC LLC may create aprofile 20102 and provide a wallet 20104 address to be able to offer thepatent application for licensing in the digital marketplace 16856. Thewallet 20104 may enable users of the knowledge distribution system 16802like owner ABC LLC transact with other users like potential licensees orcrowdsourcers. The owner may also upload an image of the front page ofthe patent application including the patent number, title, abstract, andone or more figures. The digital marketplace may provide additionaldetails about the NFT and the underlying IP on which the NFT is based.Such details may include a description 20106 of the IP, underlyingblockchain 20108, contract address 20110, price history 20112 of theNFT, trading history 20114 of the NFT, smart contract 16840 detailsincluding full license 20140 terms of the NFT licensing contract, andthe like. Additionally, any user of the digital marketplace may view theprofile of the owner to view the trust score that may help a user indeciding if they wish to transact with the owner to buy or license theIP NFT. In embodiments, potential licensees may be provided with a‘place bid’ button 20116, clicking on which they may place one or bidsfor an NFT. Once a potential licensee has provided a winning bid basedon the terms of the auction, and transferred the proceeds, the smartcontract 16840 may get triggered to transfer the ownership of the NFT tothe winning bidder and record the transaction on the blockchain. Thesmart contract 16840 may thus provide liquidity and efficiency to anotherwise illiquid and opaque market of digital knowledge assets byenabling quick and transparent transactions on the blockchain.

In embodiments, the digital marketplace 16856 may provide a searchinterface 20118 to enable a user to search for one or more pieces ofdigital knowledge 16804 (say tokenized as an NFT). A filter 20120 mayprovide users with a quick and easy way to navigate the digitalmarketplace 16856 and find products they are interested in. Inembodiments, the filter 20120 may provide the user with a dropdown menuwith various NFT categories available for licensing on the digitalmarketplace 16856. For example, the users may be able to look for NFTsfor various digital knowledge assets including intellectual property(e.g., patents, trade secrets, copyrights, trademarks, designs, knowhow, privacy rights, publicity rights, and others), instruction sets(e.g., 3D printing, semiconductor fabrication, process steps for crystalfabrication, polymer production, biological production chemicalsynthesis, food production, manufacturing, transportation) softwarecodes (e.g., executable algorithmic logic such as computer programs,firmware programs, serverless code logic, AI logic and/or definitions,machine learning logic and/or definitions, cryptography logic), datasets and the like.

Referring to FIG. 175 , knowledge distribution system 17500 forcontrolling rights related to digital knowledge is depicted. Theknowledge distribution system 17500 may include an input system 17802, atokenization system 17812, a ledger management system 17818, and a smartcontract system 17824. In some embodiments the knowledge distributionsystem 17500 may further include a smart contract generator 17858, anexecution system 17510, a reporting system 17514, and a crowdsourcingmodule 17516.

The input system 17802 receives digital knowledge 17808 from a user17502 and the tokenization system 17812 may tokenize the receiveddigital knowledge 17808 resulting in a token/tokenized digital knowledge17814 that is manipulable as a token.

The ledger management system 17818 creates and manages one or moredistributed ledgers 17820, where a distributed ledger may include aplurality of cryptographically linked blocks distributed over aplurality of nodes of a network 17848 as described elsewhere herein. Theledger management system 17818 may then store a smart contract(s) 17822and the tokenized digital knowledge 17814 in a distributed ledger 17820(FIG. 188 ).

A smart contract system 17824 may implement and manage a smart contract17822 which may include the tokenized digital knowledge 17814, atriggering event 17828, and a smart contract action 17830. Uponoccurrence of a triggering event 17828, the smart contract system 17824may perform the smart contract action 17830. The smart contract system17824 may process commitment(s) 17832 of parties 17532 to the smartcontract 17822. The smart contract system 17824 may manage rights 17540including control rights 17840 over the tokenized digital knowledge17814 and access rights 17842 regarding who can view, edit, access, oruse the tokenized digital knowledge 17814. The smart contract 17822further comprises a smart contract wrapper 17503. The knowledgedistribution system 17500 further comprises an account management system17505, a user interface system 17507, and a marketplace system 17509.

As depicted in FIG. 180 , the tokenized digital knowledge 17814 mayinclude executable algorithmic logic 18002, a 3D printer instruction set18004, an instruction set for a coating process 18008, an instructionsset for a semiconductor fabrication process 18010, a firmware program18012, an instruction set for a field-programmable gate array (FPGA)18014, serverless code logic 18018, an instructions set for a crystalfabrication system 18020, an instruction set for a food preparationprocess 18022, an instruction set for a polymer production process18024, an instruction set for a biological production process 18030, adata set for a digital twin 18032, an instruction set to perform a tradesecret 18034, intellectual property 18038, an instruction set 18040, orthe like. In embodiments, where the tokenized digital knowledge 17814includes intellectual property 18038, the smart contract system 17824may embed intellectual property licensing term(s) 18802 for theintellectual property 18038 in the distributed ledger and, in responseto a triggering event 17828, update the access rights 17842 to provideaccess to the intellectual property 18038 or process a commitment 17832of a party 17532 to the smart contract 17822 and the correspondingintellectual property licensing term(s) 18802.

In embodiments, the smart contract 17822 may include a smart contractwrapper 17503 which may add intellectual property 18038 to a stack ofintellectual property which may be on the distributed ledger 17820 andcommitment 17832 by one or more parties 17532 to: an apportionment ofroyalties for the added intellectual property 18804. The smart contractwrapper 17503 may record, in a distributed ledger, a commitment 17832 byone or more parties 17532 to: an apportionment of royalties for theaggregate stack of intellectual property 18804, or a contract term18810.

In embodiments, the ledger management system 17818 may include a loggingsystem 17512 to store logged data in the distributed ledger 17820. Inembodiments, the digital knowledge 17808 may be an instruction set andthe ledger management system 17818 may provide provable access to theinstruction set and execute the instruction set on a system. Providingprovable access may include logging or recording data in at least one ofthe plurality of cryptographically linked blocks. Provable access mayinclude: aggregating views of a trade secret into a chain that recordswhich knowledge recipients have viewed the trade secret 18814 on thedistributed ledger; recording a party who has contributed to the digitalknowledge, by logging data related to the party, logging accesstransactions to an instance of digital knowledge 18830; recording, asource of an instance of the digital information by storing data relatedto the source,

The knowledge distribution system 17500 may include a reporting system17514 to report analytic data or an analytic response(s) 18834 based ona plurality of operations performed on the distributed ledger, or on thetokenized digital knowledge. The reporting system 17514 may alsoanalyzed the tokenized digital knowledge 17814 and report the analyticresult 18832.

In an embodiment, the smart contract system 17824 may aggregate a set ofinstructions into an instructions set 18040, add an instruction topre-existing instructions set to provide a modified instruction set18040, manage allocation of instruction subsets 18042 to the distributedledger, manage access to the instruction to the instruction sets basedon access rights 17842, or the like.

In embodiments, the ledger management system 17818 may include acrowdsourcing module 17516 to obtain crowdsourced information 18602which may then be stored in the distributed ledger. crowdsourcedinformation 18602 may include: a review of an instance of the digitalknowledge 18824, a signature related to an instance of crowdsourcedinformation 18826, a verification of an instance of the digitalknowledge 18828, and the like.

In embodiments, the knowledge distribution system 17500 may include aprivate network system to enable a private network and allow authorizedparties to establish a cryptography-based consensus requirement forverification of new cryptographically linked blocks to be added to theplurality of cryptographically linked block. In embodiments, the ledgermanagement may establish cryptocurrency tokens designed to be tradeableamong users of the distributed ledger.

In embodiments, the knowledge distribution system 17500 may include anaccount management system 17505 to facilitate creation and management ofa plurality of user accounts 19094 corresponding to a plurality of users17502, 19004 of a knowledge distribution system 17500. The user accountdata may be stored on the distributed ledger.

In embodiments, the knowledge distribution system may include a userinterface system 17507, 19074 to present a user interface 19093 to auser(s) 17502, 19004 which enables the user to view data related to aninstance of the digital knowledge.

In embodiments, the knowledge distribution system may include amarketplace system 17509 to establish and maintain a digital marketplace19090 and visually present data corresponding to an instance of thedigital knowledge to a user of the knowledge distribution system.

In embodiments, the knowledge distribution system may include adatastore in communication with the distributed ledger where thedatastore may include a knowledge datastore configured to store datarelated to the digital knowledge, client datastore is configured tostore data related to a plurality of users of the knowledge distributionsystem, smart contract datastore is configured to store data related tothe smart contract, and the like.

In embodiments, the knowledge distribution system 17500 may include asmart contract generator 17858 to generate a smart contract using aparameterizable smart contract template. Smart contract parameters maybe based on a type of digital knowledge to be tokenized and may includea financial parameter, a royalty parameter, a usage parameter, an outputproduced parameter, an allocation of consideration parameter, anidentity parameter, an access condition parameter, or the like.

Referring to FIG. 176 , a computer-implemented method for controllingrights related to digital knowledge is depicted. In embodiments, adistributed ledger is created and managed (Step 17602) where thedistributed ledger may include a plurality of blocks linked viacryptography distributed over a plurality of nodes of a network as shownelsewhere herein. A smart contact may be implemented and managed (step17604). A smart contract may include a triggering event, a correspondingsmart contract action, and the like. The smart contract may be stored onthe distributed ledger. An instance of digital knowledge may be received(step 17608) The digital knowledge may be tokenized (step 17610) and theresulting tokenized digital knowledge stored via the distributed ledger(step 17612). Commitments of a plurality of parties to the smartcontract may be processed (step 17614) and rights of control or andaccess to the tokenized digital knowledge may be managed according tothe smart contract (step 17618). In response to an occurrence of thetriggering event, the corresponding smart contract action may beperformed with respect to the tokenized digital knowledge (step 17620).

Referring to FIG. 177 , an embodiment of a computer-implemented methodfor controlling rights related to digital knowledge is depicted. Thecomputer-implemented method may further include orchestrating, based onthe smart contract, an exchange of new digital knowledge for thetokenized digital knowledge (step 17702). The method may also includeintegrating the knowledge exchange with a separate exchange, wherein theknowledge exchange facilitates an exchange of at least one of valuableand sensitive knowledge related to a subject matter of the separateexchange (step 17704).

Referring to FIG. 178 , a knowledge distribution system 17800 forcontrolling rights related to digital knowledge is depicted. Inembodiments, the knowledge distribution system 17800 may include aninput system 17802, a tokenization system 17812, a ledger managementsystem 17818, a smart contract system 17824, an event monitoring module17850, and a smart contract generator 17858. The input system 17802receives information 17862 and digital knowledge 17808 from a knowledgeprovider device 17804 and the tokenization system 17812 may tokenize thedigital knowledge 17808 resulting in a token/tokenized digital knowledge17814 that is manipulable as a token.

As depicted in FIG. 180 , the tokenized digital knowledge 17814 mayinclude executable algorithmic logic 18002, a 3D printer instruction set18004, an instruction set for a coating process 18008, an instructionsset for a semiconductor fabrication process 18010, a firmware program18012, an instruction set for a field-programmable gate array (FPGA)18014, serverless code logic 18018, an instructions set for a crystalfabrication system 18020, an instruction set for a food preparationprocess 18022, an instruction set for a polymer production process18024, an instruction set for a biological production process 18030, adata set for a digital twin 18032, an instruction set to perform a tradesecret 18034, intellectual property 18038, an instruction set 18040, andthe like.

In some embodiments, the digital knowledge may include a 3D printerinstruction set for 3D printing an object such as a custom part, acustom product, a manufacturing part, a replacement part, a toy, amedical device, a tool, or the like. As depicted in FIG. 179 , the 3Dprinter instruction set for 3D printing an object 17810 may include a 3Dprinting schematic 17902, an origin 17904, a date of creation 17908, aname of a contributing individual 17910, name of a contributing group17912, name of a contributing company 17914, a price 17918, a markettrend for a related schematic 17920, a serial number 17922, a partidentifier 17924, or the like.

The ledger management system 17818 creates and manages one or moredistributed ledgers 17820, where a distributed ledger may include aplurality of a series of cryptographically linked blocks distributedover a plurality of nodes of a network 17848 as described elsewhereherein. The ledger management system 17818 may then store smartcontracts 17822 and the tokenized digital knowledge 17814 in adistributed ledger 17820.

The smart contract system 17824 may implement and manage a smartcontract 17822 where the smart contract 17822 may include one or moretriggering events 17828 and corresponding smart contract actions 17830.The smart contract system may manage rights 17861, such as controlrights 17840 and access rights 17842, to the tokenized digital knowledge17814 according to the smart contract 17822. The smart contract systemmay process commitments of the knowledge provider 17834 and a knowledgerecipient 17838 of the 3D printer instruction set for 3D printing anobject 17810.

In response to an occurrence of a triggering event 17828, the smartcontract system 17824 may perform the corresponding smart contractaction 17830 and manage the smart contract action 17830 according to acondition 17844 and the triggering event 17828. The triggering event maybe a transfer of the 3D printer instructions or use of the 3D printerinstructions and the smart contract action may, based on control rights17840 and access rights 17842, modify on the distributed ledger, whenthe 3D printer instruction set is purchased, downloaded, or used. Asdepicted in FIG. 181 , a smart contract action 17830 may include:assigning a serial number to the object that is 3D printed 18108,monitoring for the triggering event 18110, verifying fulfillment of anobligation based on the condition 18112, verifying payment or transferof the tokenized digital knowledge 18114, logging one or moretransactions in the distributed ledger 18118, transferring the tokenizeddigital knowledge, performing one or more operations with respect to thedistributed ledger 18120, creating new or more new blocks in thedistributed ledger 18122, verifying that the condition is met 18124,generating a payment request of the knowledge recipient 18128, modifyingon the distributed ledger when the 3D printer instruction set ispurchased, downloaded, or used 18130, or the like. A smart contractaction 17830 may include: receiving a purchase request from a knowledgerecipient device 18102, fulfilling a purchase request from a knowledgerecipient device 18104, verifying that the conditions is met when thecondition is a printer requirement, a payment received, a currencytransferred from a knowledge recipient device or the knowledgerecipient, a transfer of the tokenized digital knowledge to theknowledge recipient device, and the like. As depicted in FIG. 182 , acondition 17844 may include a printer requirement 18202, a paymentreceived 18204, a currency transferred from a knowledge recipient orknowledge recipient device 18208, a transfer of the tokenized digitalknowledge to the knowledge recipient or knowledge recipient device18210, or the like.

Referring to FIG. 183 , possible rights 17861 of control of and accessto the tokenized digital knowledge may include at least one ofpermission for a user to 3D print using multiple instances of the 3Dprinter instruction set 18302, a 3D printer requirement 18304, a timeperiod during which the object can be 3D printed 18308, whether thetokenized digital knowledge is transferred to a downstream knowledgerecipient 18310, warranty 18312, disclaimer 18314, indemnification18318, or certification with respect to the object 18320.

Referring to FIG. 184 , possible triggering events 17828 may includetransfer of the 3D printer instructions 18402 or us of the 3D printerinstructions 18404.

Referring to FIG. 185 , a computer-implemented method 18500 forcontrolling rights related to digital knowledge is depicted. Inembodiments, the method may include creating and managing a distributedledger, wherein the distributed ledger comprises a plurality of blockslinked via cryptography distributed over a plurality of nodes of anetwork (step 18502). A smart contract may be implemented andsubsequently managed (step 18502). The smart contract may include atriggering event and be stored in the distributed ledger. In response toan occurrence of the triggering event, a smart contract action may beperformed with respect to the digital knowledge (step 18506). The method18500 may further include receiving, from a knowledge provider device,an instance of the digital knowledge that comprises a three-dimensional(3D) printer instruction set for 3D printing an object (step 18508),tokenizing the digital knowledge (step 18510), and storing the tokenizeddigital knowledge via the distributed ledger (step 18512). The method18500 may further include processing commitments of the knowledgeprovider and a knowledge recipient of the 3D printer instruction set tothe smart contract (step 18514), managing, according to the smartcontract, rights of control of and access to the tokenized digitalknowledge (step 18516), and managing the smart contract action accordingto a condition and the triggering event (step 18518).

In embodiments, and with reference to FIG. 186 through FIG. 188 , acomputer-implemented method 18600 for controlling rights related todigital knowledge is depicted. The computer-implemented method 18600 mayinclude crowdsourcing information regarding the digital knowledge (step18602) where the crowdsourced information may include: an element of theinstance of the digital knowledge 18702, information regarding anelement of the instance of the digital knowledge 18704, informationregarding the knowledge provider 18708, information regarding theknowledge recipient 18710, and the like. The computer-implemented method18600 may further include updating the smart contract in response to thecrowdsourced information (step 18604) or updating a condition (step18608).

With reference to FIG. 190 , a knowledge distribution system 19000 forcontrolling rights related to digital knowledge is depicted. Inembodiments, the knowledge distribution system 19000 may include aninput system 19002, a tokenization system 19012, a ledger managementsystem 19018, and a smart contract system 19024. The input system 19002receives digital knowledge 19008 and the tokenization system 19012 maytokenize the digital knowledge 19008 resulting in a tokenized digitalknowledge 19014 that is manipulable as a token.

The ledger management system 19018 may create and manage a distributedledgers 19020, where the distributed ledger may include a plurality ofcryptographically linked blocks distributed over a plurality of nodes ofa network as described elsewhere herein. The ledger management system19018 may store a smart contract(s) 19022 and the tokenized digitalknowledge 19014 in a distributed ledger 19020. The ledger managementsystem 19018 may provide provable access to the digital knowledge 19008by recording and storing access transactions 19048 in the distributedledger. Other methods of providing provable access are describedelsewhere herein.

A smart contract system 19024 may implement and manage a smart contract1902 which may include the tokenized digital knowledge 19014, and atriggering event 19028. Upon occurrence of a triggering event 19028, thesmart contract system 19024 may perform a smart 19062 including rightsof control 19040 over the tokenized digital knowledge 19014 and rightsof access 19042 regarding who can view, edit, access, or use the digitalknowledge 19008. The smart contract 19022 may process commitments 19032of parties to the smart contract 19034.

In embodiments, the smart contract 19022 may include a smart contractwrapper 19064 which may perform an operation on the distributed ledgerto: add intellectual property 18038, add intellectual property 18038 toa stack of intellectual property, add commitment 17832 by one or moreparties 17532 to: an apportionment of royalties for the addedintellectual property 18804.

In embodiments, the knowledge distribution system 19000 may include anaccount management system 19072, in communication with a distributedledger, to facilitate creation and management of a plurality of useraccounts 19094 corresponding to a plurality of users 19004 of aknowledge distribution system 19500. The knowledge distribution system19000 may include a user interface system 19074 to present a userinterface 19093 to a user(s) 19004 which enables the user to view datarelated to an instance of the digital knowledge 19008.

In embodiments, the knowledge distribution system 19000 may include amarketplace system 19078 to establish and maintain a digital marketplace19090 and visually present data corresponding to an instance of thedigital knowledge 19008 to a user 19004 of the knowledge distributionsystem 19000.

In embodiments, the knowledge distribution system 19000 may include adatastore in communication with the distributed ledger where thedatastore may include a knowledge datastore 19082 configured to storedata related to the digital knowledge 19008, client datastore 19084configured to store data related to a plurality of users 19004 of theknowledge distribution system, smart contract datastore 17164 configuredto store data related to the smart contract 19022, and the like.

The knowledge distribution system 19000 may include a reporting system19080 analyze the tokenized digital knowledge 19014 and report theanalytic result 19098.

In embodiments, the knowledge distribution system 19000 may include asmart contract generator 19088 to generate a smart contract 19022 usinga parameterizable smart contract template 19060. Referring to FIG. 189 ,smart contract parameters 17522 may be based on a type of digitalknowledge to be tokenized and may include a financial parameter 18902, aroyalty parameter 18904, a usage parameter 18906, and output producedparameter 18908, and allocation of consideration parameter 18910, anidentity parameter 18912, an access condition parameter 18914, or thelike

With reference to FIG. 191 , an illustrative and non-limiting examplemethod for controlling rights related to digital knowledge is depicted.The method may include creating and managing a distributed ledger 19102,wherein the distributed ledger comprises a plurality of blocks linkedvia cryptography distributed over a plurality of nodes of a network;tokenizing the digital knowledge 19104; storing the tokenized digitalknowledge via the distributed ledger 19108; implementing and managing asmart contract 19110, wherein the smart contract comprises a triggeringevent, the tokenized knowledge, and a corresponding smart contractaction and is stored in the distributed ledger; receiving an instance ofthe digital knowledge 19112; processing commitments of a plurality ofparties to the smart contract 19114; managing, according to the smartcontract, rights of control of and access to the tokenized digitalknowledge 19118; performing, in response to an occurrence of thetriggering event, the corresponding smart contract action with respectto the tokenized digital knowledge 19120; and managing the smartcontract action in response to the triggering event 19122. In someembodiments, and with reference to FIG. 192 , the method may furtherinclude crowdsourcing information regarding an element of the instanceof the digital knowledge 19224 and updating the smart contract inresponse to the crowdsourced information 19228. In some embodiments, andwith reference to FIG. 193 , the method may further include addingintellectual property to the distributed ledger 19324, committingparties to an apportionment of royalties for the added intellectualproperty 19328, and processing a commitment of a party to a contractterm 19330. In some embodiments, and with reference to FIG. 194 , themethod may further include creating a user account 19424, receiving arequest from a user account to display data related to an instance ofthe digital knowledge 19428, confirming access to the instance of thedigital knowledge allowed for the user account 19430, and presenting auser interface configured to display the data related to an instance ofthe digital knowledge 19432. In some embodiments, and with reference toFIG. 195 , the method may further include buying or selling the digitalknowledge 19524. In some embodiments, and with reference to FIG. 196 ,the method may further include creating and issuing a currency tokenassociated with the distributed ledger 19624.

With reference to FIG. 190 , a knowledge distribution system 19000 forcontrolling rights related to digital knowledge is depicted. Inembodiments, the knowledge distribution system 19000 may include aninput system 19002, a tokenization system 19012, a ledger managementsystem 19018, and a smart contract system 19024. The input system 19002receives digital knowledge 19008 and the tokenization system 19012 maytokenize the digital knowledge 19008 resulting in a tokenized digitalknowledge 19014 that is manipulable as a token.

The ledger management system 19018 may create and manage a distributedledgers 19020, where the distributed ledger may include a plurality ofcryptographically linked blocks distributed over a plurality of nodes ofa network as described elsewhere herein. The ledger management system19018 may store a smart contract(s) 19022 and the tokenized digitalknowledge 19014 in a distributed ledger 19020. The ledger managementsystem 19018 may provide provable access to the digital knowledge 19008by recording and storing access transactions 19048 in the distributedledger. Other methods of providing provable access are describedelsewhere herein.

A smart contract system 19024 may implement and manage a smart contract1902 which may include the tokenized digital knowledge 19014, and atriggering event 19028. Upon occurrence of a triggering event 19028, thesmart contract system 19024 may perform a smart 19062 including rightsof control 19040 over the tokenized digital knowledge 19014 and rightsof access 19042 regarding who can view, edit, access, or use the digitalknowledge 19008. The smart contract 19022 may process commitments 19032of parties to the smart contract 19034.

In embodiments, the smart contract 19022 may include a smart contractwrapper 19064 which may perform an operation on the distributed ledgerto: add intellectual property 18038, add intellectual property 18038 toa stack of intellectual property, add commitment 17832 by one or moreparties 17532 to: an apportionment of royalties for the addedintellectual property 18804.

In embodiments, the knowledge distribution system 19000 may include anaccount management system 19072, in communication with a distributedledger, to facilitate creation and management of a plurality of useraccounts 19094 corresponding to a plurality of users 19004 of aknowledge distribution system 19500. The knowledge distribution system19000 may include a user interface system 19074 to present a userinterface 19093 to a user(s) 19004 which enables the user to view datarelated to an instance of the digital knowledge 19008.

In embodiments, the knowledge distribution system 19000 may include amarketplace system 19078 to establish and maintain a digital marketplace19090 and visually present data corresponding to an instance of thedigital knowledge 19008 to a user 19004 of the knowledge distributionsystem 19000.

In embodiments, the knowledge distribution system 19000 may include adatastore in communication with the distributed ledger where thedatastore may include a knowledge datastore 19082 configured to storedata related to the digital knowledge 19008, client datastore 19084configured to store data related to a plurality of users 19004 of theknowledge distribution system, smart contract datastore 17164 configuredto store data related to the smart contract 19022, and the like.

The knowledge distribution system 19000 may include a reporting system19080 analyze the tokenized digital knowledge 19014 and report theanalytic result 19098.

In embodiments, the knowledge distribution system 19000 may include asmart contract generator 19088 to generate a smart contract 19022 usinga parameterizable smart contract template 19060. Smart contractparameters may be based on a type of digital knowledge to be tokenizedand may include a financial parameter, a royalty parameter, a usageparameter, and output produced parameter, and allocation ofconsideration parameter, an identity parameter, an access conditionparameter, or the like

Workflow Management Systems

In embodiments, the workflow management system may support variousworkflows associated with a facility, such as including interfaces ofthe platform by which a facility manager may review various analyticresults, status information, and the like. In embodiments, the workflowmanagement system tracks the operation of a post-action follow-up moduleto ensure that the correct follow-up messages are automatically, orunder control of a facility agent using the platform, sent toappropriate individuals, systems and/or services.

In the various embodiments, various elements are included for a workflowfor each of an energy project, a compute project (e.g., cryptocurrencyand/or AI) and hybrids. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to at least one of predict alikelihood of a facility production outcome, predict a facilityproduction outcome, optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles, optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs,optimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles, optimize configuration of available energyand compute resources to produce a favorable facility resourceconfiguration profile among a set of available profiles, optimizeselection and configuration of an artificial intelligence system toproduce a favorable facility output profile among a set of availableartificial intelligence systems and configurations, or generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility.

Management Application Platform

Referring to FIG. 33 , a transactional, financial and marketplaceenablement system 3300 is illustrated, including a set of systems,applications, processes, modules, services, layers, devices, components,machines, products, sub-systems, interfaces, connections, and otherelements working in coordination to enable intelligent management of aset of financial and transactional entities 3330 that may occur,operate, transact or the like within, or own, operate, support orenable, one or more platform-operated marketplaces 3327 or externalmarketplaces 3390 or that may otherwise be part of, integrated with,linked to, or operated on by the platform 3300. Platform-operatedmarketplaces 3327 and external marketplaces 3390 may include a widevariety of marketplaces and exchanges for physical goods, services,virtual goods, digital content, advertising, credits (such as renewableenergy credits, pollution abatement credits and the like), currencies,commodities, cryptocurrencies, loyalty points, physical resources, humanresources, attention resources, information technology resources,storage resources, energy resources, options, futures, derivatives,securities, rights of access, tickets, licenses (including seatlicenses, private or government-issued licenses or permissions toundertake regulated activities, medallions, badges and others), and manyothers. Financial and transactional entities 3330 may include any of thewide variety of assets, systems, devices, machines, facilities,individuals or other entities mentioned throughout this disclosure or inthe documents incorporated herein by reference, such as, withoutlimitation: financial machines 3352 and their components (e.g.,automated teller machines, point of sale machines, vending machines,kiosks, smart-card-enabled machines, and many others); financial andtransactional processes 3350 (such as lending processes, softwareprocesses (including applications, programs, services, and others),production processes, banking processes (e.g., lending processes,underwriting processes, investing processes, and many others), financialservice processes, diagnostic processes, security processes, safetyprocesses and many others); wearable and portable devices 3348 (such asmobile phones, tablets, dedicated portable devices for financialapplications, data collectors (including mobile data collectors),sensor-based devices, watches, glasses, hearables, head-worn devices,clothing-integrated devices, arm bands, bracelets, neck-worn devices,AR/VR devices, headphones, and many others); workers 3344 (such asbanking workers, financial service personnel, managers, engineers, floormanagers, vault workers, inspectors, delivery personnel, currencyhandling workers, process supervisors, security personnel, safetypersonnel and many others); robotic systems 3342 (e.g., physical robots,collaborative robots (e.g., “cobots”), software bots and others); andoperating facilities 3340 (such as currency production facilities,storage facilities, vaults, bank branches, office buildings, bankingfacilities, financial services facilities, cryptocurrency miningfacilities, data centers, trading floors, high frequency tradingoperations, and many others), which may include, without limitation,among many others, storage and financial services facilities 3338 (suchas for financial services inventory, components, packaging materials,goods, products, machinery, equipment, and other items); insurancefacilities 3334 (such as branches, offices, storage facilities, datacenters, underwriting operations and others); and banking facilities3332 (such as for commercial banking, investing, consumer banking,lending and many other banking activities).

In embodiments, the transactional, financial and marketplace enablementsystem 3300 may include a set of data handling layers 3308 each of whichis configured to provide a set of capabilities that facilitatedevelopment and deployment of intelligence, such as for facilitatingautomation, machine learning, applications of artificial intelligence,intelligent transactions, state management, event management, processmanagement, and many others, for a wide variety of financial andtransactional applications and end uses. In embodiments, the datahandling layers 3308 include a financial and transactional monitoringsystems layer 3306, a financial and transactional entity-oriented datastorage systems layer 3310 (referred to in some cases herein forconvenience simply as a data storage layer 3310), an adaptiveintelligent systems layer 3304 and a financial and transactionalmanagement application platform layer 3302. Each of the data handlinglayers 3308 may include a variety of services, programs, applications,workflows, systems, components, and modules, as further described hereinand in the documents incorporated herein by reference. In embodiments,each of the data handling layers 3308 (and optionally the transactional,financial and marketplace enablement system 3300 as a whole) isconfigured such that one or more of its elements can be accessed as aservice by other layers 3308 or by other systems (e.g., being configuredas a platform-as-a-service deployed on a set of cloud infrastructurecomponents in a microservices architecture). For example, a datahandling layer 3308 may have a set of application programming interfaces3316, such as application programming interfaces (APIs), brokers,services, connectors, wired or wireless communication links, ports,human-accessible interfaces, software interfaces or the like by whichdata may be exchanged between the data handling layer 3308 and otherlayers, systems or sub-systems of the platform 3300, as well as withother systems, such as financial entities 3330 or external systems, suchas cloud-based or on-premises enterprise systems (e.g., accountingsystems, resource management systems, CRM systems, supply chainmanagement systems and many others. Each of the data handling layers3308 may include a set of services (e.g., microservices), for datahandling, including facilities for data extraction, transformation andloading; data cleansing and deduplication facilities; data normalizationfacilities; data synchronization facilities; data security facilities;computational facilities (e.g., for performing pre-defined calculationoperations on data streams and providing an output stream); compressionand de-compression facilities; analytic facilities (such as providingautomated production of data visualizations) and others.

In embodiments, each data handling layer 3308 has a set of applicationprogramming interfaces 3316 for automating data exchange with each ofthe other data handling layers 3308. These may include data integrationcapabilities, such as for extracting, transforming, loading,normalizing, compression, decompressing, encoding, decoding, andotherwise processing data packets, signals, and other information as itexchanged among the layers and/or the applications 3312, such astransforming data from one format or protocol to another as needed inorder for one layer to consume output from another. In embodiments, thedata handling layers 3308 are configured in a topology that facilitatesshared data collection and distribution across multiple applications anduses within the transactional, financial and marketplace enablementsystem 3300 by the financial and transactional monitoring systems layer3306. The financial and transactional monitoring systems layer 3306 mayinclude, integrate with, and/or cooperate with various data collectionand management systems 3318, referred to for convenience in some casesas data collection systems 3318, for collecting and organizing datacollected from or about financial and transactional entities 3330, aswell as data collected from or about the various data handling layers3308 or services or components thereof. For example, a stream ofphysiological data from a wearable device worn by a worker undertaking atask or a consumer engaged in an activity can be distributed via themonitoring systems layer 3306 to multiple distinct applications in thefinancial and transactional management application platform layer 3302,such as one that facilitates monitoring the physiological,psychological, performance level, attention, or other state of a workerand another that facilitates operational efficiency and/oreffectiveness. In embodiments, the monitoring systems layer 3306facilitates alignment, such as time-synchronization, normalization, orthe like of data that is collected with respect to one or more entities3330. For example, one or more video streams or other sensor datacollected of or with respect to a worker 3344 or other entity in atransactional or financial environment, such as from a set ofcamera-enabled IoT devices, may be aligned with a common clock, so thatthe relative timing of a set of videos or other data can be understoodby systems that may process the videos, such as machine learning systemsthat operate on images in the videos, on changes between images indifferent frames of the video, or the like. In such an example, thefinancial and transactional monitoring systems layer 3306 may furtheralign a set of videos, camera images, sensor data, or the like, withother data, such as a stream of data from wearable devices, a stream ofdata produced by financial or transactional systems (such aspoint-of-sale systems, ATMs, kiosks, handheld transaction systems, cardreaders, and the like), a stream of data collected by mobile datacollectors, and the like. Configuration of the financial andtransactional monitoring systems layer 3306 as a common platform, or setof microservices, that are accessed across many applications, maydramatically reduce the number of interconnections required by anenterprise in order to have a growing set of applications monitoring agrowing set of IoT devices and other systems and devices that are underits control.

In embodiments, the data handling layers 3308 are configured in atopology that facilitates shared or common data storage across multipleapplications and uses of the transactional, financial and marketplaceenablement system 3300 by the financial and transactional entity andtransaction-oriented data storage systems layer 3310, referred to hereinfor convenience in some cases simply as the data storage layer 3310 orstorage layer 3310. For example, various data collected about thefinancial entities 3330, as well as data produced by the other datahandling layers 3308, may be stored in the data storage layer 3310, suchthat any of the services, applications, programs, or the like of thevarious data handling layers 3308 can access a common data source (whichmay comprise a single logical data source that is distributed acrossdisparate physical and/or virtual storage locations). This mayfacilitate a dramatic reduction in the amount of data storage requiredto handle the enormous amount of data produced by or about entities 3330as applications of the financial and transactional IoT proliferate. Forexample, a supply chain or inventory management application in thefinancial and transactional management application platform layer 3302,such as one for ordering replacement parts for a financial ortransactional machine or item of equipment, or for reordering currencyor other inventory, may access the same data set about what parts havebeen replaced for a set of machines as a predictive maintenanceapplication that is used to predict whether a machine is likely torequire replacement parts. Similarly, prediction may be used withrespect to resupply of currency or other items. In embodiments, the datastorage systems layer 3310 may provide an extremely rich environment forcollection of data that can be used for extraction of features or inputsfor intelligence systems, such as expert systems, artificialintelligence systems, robotic process automation systems, machinelearning systems, deep learning systems, supervised learning systems, orother intelligent systems as disclosed throughout this disclosure andthe documents incorporated herein by reference. As a result, eachapplication in the financial and transactional management applicationplatform layer 3302 and each adaptive intelligent system in the adaptiveintelligent systems layer 3304 can benefit from the data collected orproduced by or for each of the others. A wide range of data types may bestored in the storage layer 3310 using various storage media and datastorage types and formats, including, without limitation: asset andfacility data 3320 (such as asset identity data, operational data,transactional data, event data, state data, workflow data, maintenancedata, pricing data, ownership data, transferability data, and many othertypes of data relating to an asset (which may be a physical asset,digital asset, virtual asset, financial asset, securities asset, orother asset); worker data 3322 (including identity data, role data, taskdata, workflow data, health data, attention data, mood data, stressdata, physiological data, performance data, quality data and many othertypes); event data 3324 (including process events, transaction events,exchange events, pricing events, promotion events, discount events,rebate events, reward events, point utilization events, financialevents, output events, input events, state-change events, operatingevents, repair events, maintenance events, service events, damageevents, injury events, replacement events, refueling events, rechargingevents, supply events, and many others); claims data 3354 (such asrelating to insurance claims, such as for business interruptioninsurance, product liability insurance, insurance on goods, facilities,or equipment, flood insurance, insurance for contract-related risks, andmany others, as well as claims data relating to product liability,general liability, workers compensation, injury and other liabilityclaims and claims data relating to contracts, such as supply contractperformance claims, product delivery requirements, contract claims,claims for damages, claims to redeem points or rewards, claims of accessrights, warranty claims, indemnification claims, energy productionrequirements, delivery requirements, timing requirements, milestones,key performance indicators and others); accounting data 3358 (such asdata relating to debits, credits, costs, prices, profits, margins, ratesof return, valuation, write-offs, and many others); underwriting data3360 (such as data relating to identities of prospective and actualparties involved insurance and other transactions, actuarial data, datarelating to probability of occurrence and/or extent of risk associatedwith activities, data relating to observed activities and other dataused to underwrite or estimate risk); access data 3362 (such as datarelating to rights of access, tickets, tokens, licenses and other accessrights described throughout this disclosure, including data structuresrepresenting access rights; pricing data 3364 (including spot marketpricing, forward market pricing, pricing discount information,promotional pricing, and other information relating to the cost or priceof items in any of the platform-operated marketplaces 3327 and/orexternal marketplaces 3390); as well as other types of data not shown,such as production data (such as data relating to production of physicalor digital goods, services, events, content, and the like, as well asdata relating to energy production found in databases of publicutilities or independent services organizations that maintain energyinfrastructure, data relating to outputs of banking, data related tooutputs of mining and energy extraction facilities, outputs of drillingand pipeline facilities and many others); and supply chain data (such asrelating to items supplied, amounts, pricing, delivery, sources, routes,customs information and many others).

In embodiments, the data handling layers 3308 are configured in atopology that facilitates shared adaptation capabilities, which may beprovided, managed, mediated and the like by one or more of a set ofservices, components, programs, systems, or capabilities of the adaptiveintelligent systems layer 3304, referred to in some cases herein forconvenience as the adaptive intelligent systems layer 3304. The adaptiveintelligent systems layer 3304 may include a set of data processing,artificial intelligence, and computational systems 3314 that aredescribed in more detail elsewhere throughout this disclosure. Thus, useof various resources, such as computing resources (such as availableprocessing cores, available servers, available edge computing resources,available on-device resources (for single devices or peered networks),and available cloud infrastructure, among others), data storageresources (including local storage on devices, storage resources in oron financial entities or environments (including on-device storage,storage on asset tags, local area network storage and the like), networkstorage resources, cloud-based storage resources, database resources andothers), networking resources (including cellular network spectrum,wireless network resources, fixed network resources and others), energyresources (such as available battery power, available renewable energy,fuel, grid-based power, and many others) and others may be optimized ina coordinated or shared way on behalf of an operator, enterprise, or thelike, such as for the benefit of multiple applications, programs,workflows, or the like. For example, the adaptive intelligent systemlayer 3304 may manage and provision available network resources for botha financial analytics application and for a financial remote controlapplication (among many other possibilities), such that low latencyresources are used for remote control and longer latency resources areused for the analytics application. As described in more detailthroughout this disclosure and the documents incorporated herein byreference, a wide variety of adaptations may be provided on behalf ofthe various services and capabilities across the various layers 3308,including ones based on application requirements, quality of service,budgets, costs, pricing, risk factors, operational objectives,efficiency objectives, optimization parameters, returns on investment,profitability, uptime/downtime, worker utilization, and many others.

The financial and transactional management application platform layer3302, referred to in some cases herein for convenience as the financialand transactional management application platform layer 3302, mayinclude a set of financial and transactional processes, workflows,activities, events and applications 3312 (referred to collectively,except where context indicates otherwise, as applications 3312) thatenable an operator to manage more than one aspect of an financial ortransactional environment or entity 3330 in a common applicationenvironment, such as one that takes advantage of common data storage inthe data storage layer 3310, common data collection or monitoring in thefinancial and transactional monitoring systems layer 3306 and/or commonadaptive intelligence of the adaptive intelligent system layer 3304.Outputs from the applications 3312 in the financial and transactionalmanagement application platform layer 3302 may be provided to the otherdata handing layers 3308. These may include, without limitation, stateand status information for various objects, entities, processes, flowsand the like; object information, such as identity, attribute andparameter information for various classes of objects of various datatypes; event and change information, such as for workflows, dynamicsystems, processes, procedures, protocols, algorithms, and other flows,including timing information; outcome information, such as indicationsof success and failure, indications of process or milestone completion,indications of correct or incorrect predictions, indications of corrector incorrect labeling or classification, and success metrics (includingrelating to yield, engagement, return on investment, profitability,efficiency, timeliness, quality of service, quality of product, customersatisfaction, and others) among others. Outputs from each application3312 can be stored in the data storage layer 3310, distributed forprocessing by the data collection layer 3318, and used by the adaptiveintelligent system layer 3304. The cross-application nature of thefinancial and transactional management application platform layer 3302thus facilitates convenient organization of all of the necessaryinfrastructure elements for adding intelligence to any givenapplication, such as by supplying machine learning on outcomes acrossapplications, providing enrichment of automation of a given applicationvia machine learning based on outcomes from other applications (or otherelements of the platform 3300, and allowing application developers tofocus on application-native processes while benefiting from othercapabilities of the platform 3300.

Referring to FIG. 34 , additional details, components, sub-systems, andother elements of an optional embodiment of the transactional, financialand marketplace enablement system 3300 of FIG. 33 are illustrated. Thefinancial and transactional management application platform layer 3302may, in various optional embodiments, include a set of applications,systems, solutions, interfaces, or the like, collectively referred tofor convenience as applications 3312, by which an operator or owner of atransactional or financial entity, or other users, may manage, monitor,control, analyze, or otherwise interact with one or more elements of theentity 3330, such as any of the elements noted in connection above inconnection FIG. 33 . The set of applications 3312 may include, withoutlimitation, one or more of any of a wide range of types of applications,such as an investment application 3402 (such as, without limitation, forinvestment in shares, interests, currencies, commodities, options,futures, derivatives, real property, trusts, cryptocurrencies, tokens,and other asset classes); an asset management application 3404 (such as,without limitation, for managing investment assets, real property,fixtures, personal property, real estate, equipment, intellectualproperty, vehicles, human resources, software, information technologyresources, data processing resources, data storage resources, powergeneration and/or storage resources, computational resources and otherassets); a lending application 3410 (such as, without limitation, forpersonal lending, commercial lending, collateralized lending,microlending, peer-to-peer lending, insurance-related lending,asset-backed lending, secured debt lending, corporate debt lending,student loans, mortgage lending, automotive lending, and others); a riskmanagement application 3408 (such as, without limitation, for managingrisk or liability with respect to a product, an asset, a person, a home,a vehicle, an item of equipment, a component, an information technologysystem, a security system, a security event, a cybersecurity system, anitem of property, a health condition, mortality, fire, flood, weather,disability, malpractice, business interruption, infringement,advertising injury, slander, libel, violation of privacy or publicityrights, injury, damage to property, damage to a business, breach of acontract, and others); a payments application 3433 (such as for enablingvarious payments within and across marketplaces, including credit card,debit card, wire transfer, ACH, checking, currency and other payments);a marketing application 3412 (such as, without limitation, anapplication for marketing a financial or transactional product orservice, an advertising application, a marketplace platform or systemfor goods, services or other items, a marketing analytics application, acustomer relationship management application, a search engineoptimization application, a sales management application, an advertisingnetwork application, a behavioral tracking application, a marketinganalytics application, a location-based product or service targetingapplication, a collaborative filtering application, a recommendationengine for a product or service, and others); a trading application 3428(such as, without limitation, a buying application, a sellingapplication, a bidding application, an auction application, a reverseauction application, a bid/ask matching application, a securitiestrading application, a commodities trading application, an optiontrading application, a futures trading application, a derivativestrading application, a cryptocurrency trading application, atoken-trading application, an analytic application for analyzingfinancial or transactional performance, yield, return on investment, orother metrics, a book-building application, or others); a taxapplication 3414 (such as, without limitation, for managing,calculating, reporting, optimizing, or otherwise handling data, events,workflows, or other factors relating to a tax, a levy, a tariff, a duty,a credit, a fee or other government-imposed charge, such as, withoutlimitation, sales tax, income tax, property tax, municipal fees,pollution tax, renewal energy credit, pollution abatement credit, valueadded tax, import duties, export duties, and others); a fraud preventionapplication 3416 (such as, without limitation, one or more of anidentity verification application, a biometric identify validationapplication, a transactional pattern-based fraud detection application,a location-based fraud detection application, a user behavior-basedfraud detection application, a network address-based fraud detectionapplication, a black list application, a white list application, acontent inspection-based fraud detection application, or other frauddetection application, a financial service, application or solution 3409(referred to collectively as a “financial service”, such as, withoutlimitation, a financial planning service, a tax planning service, aportfolio management service, a transaction service, a lending service,a banking service, a currency conversion service, a currency exchangeservice, a remittance service, a money transfer service, a wealthmanagement service, an estate planning service, an investment bankingservice, a commercial banking service, a foreign exchange service, aninsurance service, an investment service, an investment managementservice, a hedge fund service, a mutual fund service, a custody service,a credit card service, a safekeeping service, a checking service, adebit card service, a lending service, an ATM service, an ETF service, awire transfer service, an overdraft service, a reporting service, acertified checking service, a notary service, a capital markets service,a brokerage service, a broker-dealer service, a private banking service,an insurance service, an insurance brokerage service, an underwritingservice, an annuity service, a life insurance service, a healthinsurance service, a retirement insurance service, a property insuranceservice, a casualty insurance service, a finance and insurance service,a reinsurance service, an intermediation service, a trade clearinghouseservice, a private equity service, a venture capital service, an angelinvestment service, a family office investment service, an exchangeservice, a payments service, a settlement service, an interbanknetworking service, a debt resolution service, or other financialservice); a security application, solution or service 3418 (referred toherein as a security application, such as, without limitation, any ofthe fraud prevention applications 3416 noted above, as well as aphysical security system (such as for an access control system (such asusing biometric access controls, fingerprinting, retinal scanning,passwords, and other access controls), a safe, a vault, a cage, a saferoom, or the like), a monitoring system (such as using cameras, motionsensors, infrared sensors and other sensors), a cyber security system(such as for virus detection and remediation, intrusion detection andremediation, spam detection and remediation, phishing detection andremediation, social engineering detection and remediation, cyber-attackdetection and remediation, packet inspection, traffic inspection, DNSattack remediation and detection, and others) or other securityapplication); an underwriting application 3420 (such as, withoutlimitation, for underwriting any insurance offering, any loan, or anyother transaction, including any application for detecting,characterizing or predicting the likelihood and/or scope of a risk,including underwriting based on any of the data sources, events orentities noted throughout this disclosure or the documents incorporatedherein by reference); a blockchain application 3422 (such as, withoutlimitation, a distributed ledger capturing a series of transactions,such as debits or credits, purchases or sales, exchanges of in kindconsideration, smart contract events, or the like, a cryptocurrencyapplication, or other blockchain-based application); a real estateapplication 3424 (such as, without limitation, a real estate brokerageapplication, a real estate valuation application, a real estateinvestment trust application, a real estate mortgage or lendingapplication, a real estate assessment application, a real estatemarketing application, or other); a regulatory application 3426 (suchas, without limitation, an application for regulating any of theapplications, services, transactions, activities, workflows, events,entities, or other items noted herein and in the documents incorporatedby reference herein, such as regulation of pricing, marketing, offeringof securities, offering of insurance, undertaking of broker or dealeractivities, use of data (including data privacy regulations, regulationsrelating to storage of data and others), banking, marketing, sales,financial planning, and many others); a platform-operated marketplace3327 application, solution or service (referred to in some cases simplyas a marketplace application (which term may also, as context permitsinclude various types of external marketplaces 3390), such as, withoutlimitation an e-commerce marketplace, an auction marketplace, a physicalgoods marketplace, a virtual goods marketplace, an advertisingmarketplace, a reverse-auction marketplace, an advertising network, amarketplace for attention resources, an energy trading marketplace, amarketplace for computing resources, a marketplace for networkingresources, a spectrum allocation marketplace, an Internet advertisingmarketplace, a television advertising marketplace, a print advertisingmarketplace, a radio advertising marketplace, an in-game advertisingmarketplace, an in-virtual reality advertising marketplace, anin-augmented reality marketplace, a real estate marketplace, ahospitality marketplace, a travel services marketplace, a financialservices marketplace, a blockchain-based marketplace, a cryptocurrencymarketplace, a token-based marketplace, a loyalty program marketplace, atime share marketplace, a rideshare marketplace, a mobility marketplace,a transportation marketplace, a space-sharing marketplace, or othermarketplace); a warranty application 3417 (such as, without limitation,an application for a warranty or guarantee with respect to a product, aservice, an offering, a solution, a physical product, software, a levelof service, quality of service, a financial instrument, a debt, an itemof collateral, performance of a service, or other item); an analyticssolution 3419 (such as, without limitation, an analytic application withrespect to any of the data types, applications, events, workflows, orentities mentioned throughout this disclosure or the documentsincorporated by reference herein, such as a big data application, a userbehavior application, a prediction application, a classificationapplication, a dashboard, a pattern recognition application, aneconometric application, a financial yield application, a return oninvestment application, a scenario planning application, a decisionsupport application, and many others); a pricing application 3421 (suchas, without limitation, for pricing of goods, services (including anymentioned throughout this disclosure and the documents incorporated byreference herein), applications (including any mentioned throughout thisdisclosure and the documents incorporated by reference herein),software, data services, insurance, virtual goods, advertisingplacements, search engine and keyword placements, and many others; and asmart contract application, solution, or service (referred tocollectively herein as a smart contract application, such as, withoutlimitation, any of the smart contract types referred to in thisdisclosure or in the documents incorporated herein by reference, such asa smart contract using a token or cryptocurrency for consideration, asmart contract that vests a right, an option, a future, or an interestbased on a future condition, a smart contract for a security, commodity,future, option, derivative, or the like, a smart contract for current orfuture resources, a smart contract that is configured to account for oraccommodate a tax, regulatory or compliance parameter, a smart contractthat is configured to execute an arbitrage transaction, or many others).Thus, the financial and transactional management application platformlayer 3302 may host an enable interaction among a wide range ofdisparate applications 3312 (such term including the above-referencedand other financial or transactional applications, services, solutions,and the like), such that by virtue of shared microservices, shared datainfrastructure, and shared intelligence, any pair or larger combinationor permutation of such services may be improved relative to an isolatedapplication of the same type.

In embodiments, the adaptive intelligent systems layer 3304 may includea set of systems, components, services, and other capabilities thatcollectively facilitate the coordinated development and deployment ofintelligent systems, such as ones that can enhance one or more of theapplications 3312 at the financial and transactional managementapplication platform layer 3302. These adaptive intelligence systemslayer 3304 may include an adaptive edge compute management solution3430, a robotic process automation system 3442, a set of protocoladaptors 3491, a packet acceleration system 3434, an edge intelligencesystem 3438, an adaptive networking system 3440, a set of state andevent managers 3444, a set of opportunity miners 3446, a set ofartificial intelligence systems 3448 and other systems.

In embodiments, the financial and transactional monitoring systems layer3306 and its data collection systems 3318 may include a wide range ofsystems for collection of data. This layer may include, withoutlimitation, real time monitoring systems 3468 (such as onboardmonitoring systems like event and status reporting systems on ATMs, POSsystems, kiosks, vending machines and the like; OBD and telematicssystems on vehicle and equipment; systems providing diagnostic codes andevents via an event bus, communication port, or other communicationsystem; monitoring infrastructure (such as cameras, motion sensors,beacons, RFID systems, smart lighting systems, asset tracking systems,person tracking systems, and ambient sensing systems located in variousenvironments where transactions and other events take place), as well asremovable and replaceable monitoring systems, such as portable andmobile data collectors, RFID and other tag readers, smart phones,tablets and other mobile device that are capable of data collection andthe like); software interaction observation systems 3450 (such as forlogging and tracking events involved in interactions of users withsoftware user interfaces, such as mouse movements, touchpadinteractions, mouse clicks, cursor movements, keyboard interactions,navigation actions, eye movements, finger movements, gestures, menuselections, and many others, as well as software interactions that occuras a result of other programs, such as over APIs, among many others);mobile data collectors 3452 (such as described extensively herein and indocuments incorporated by reference), visual monitoring systems 3454(such as using video and still imaging systems, LIDAR, IR and othersystems that allow visualization of items, people, materials,components, machines, equipment, personnel, gestures, expressions,positions, locations, configurations, and other factors or parameters ofentities 3330, as well as inspection systems that monitor processes,activities of workers and the like); point of interaction systems 3470(such as point of sale systems, kiosks, ATMs, vending machines, touchpads, camera-based interaction tracking systems, smart shopping carts,user interfaces of online and in-store vending and commerce systems,tablets, and other systems at the point of sale or other interaction bya customer or worker involved in shopping and/or a transaction);physical process interaction observation systems 3458 (such as fortracking physical activities of customers, physical activities oftransaction parties (such as traders, vendors, merchants, customers,negotiators, brokers, and the like), physical interactions of workerswith other workers, interactions of workers with physical entities likemachines and equipment, and interactions of physical entities with otherphysical entities, including, without limitation, by use of video andstill image cameras, motion sensing systems (such as including opticalsensors, LIDAR, IR and other sensor sets), robotic motion trackingsystems (such as tracking movements of systems attached to a human or aphysical entity) and many others; machine state monitoring systems 3460(including onboard monitors and external monitors of conditions, states,operating parameters, or other measures of the condition of a machine,such as a client, a server, a cloud resource, an ATM, a kiosk, a vendingmachine, a POS system, a sensor, a camera, a smart shopping cart, asmart shelf, a vehicle, a robot, or other machine); sensors and cameras3462 and other IoT data collection systems 3464 (including onboardsensors, sensors or other data collectors (including click trackingsensors) in or about a financial or transactional environment (such as,without limitation, an office, a back office, a store, a mall, a virtualstore, an online environment, a website, a bank, or many others),cameras for monitoring an entire environment, dedicated cameras for aparticular machine, process, worker, or the like, wearable cameras,portable cameras, cameras disposed on mobile robots, cameras of portabledevices like smart phones and tablets, and many others, including any ofthe many sensor types disclosed throughout this disclosure or in thedocuments incorporated herein by reference); indoor location monitoringsystems 3472 (including cameras, IR systems, motion-detection systems,beacons, RFID readers, smart lighting systems, triangulation systems, RFand other spectrum detection systems, time-of-flight systems, chemicalnoses and other chemical sensor sets, as well as other sensors); userfeedback systems 3474 (including survey systems, touch pads, voice-basedfeedback systems, rating system, expression monitoring systems, affectmonitoring systems, gesture monitoring systems, and others); behavioralmonitoring systems 3478 (such as for monitoring movements, shoppingbehavior, buying behavior, clicking behavior, behavior indicating fraudor deception, user interface interactions, product return behavior,behavior indicative of interest, attention, boredom or the like,mood-indicating behavior (such as fidgeting, staying still, movingcloser, or changing posture) and many others); and any of a wide varietyof Internet of Things (IoT) data collectors 3464, such as thosedescribed throughout this disclosure and in the documents incorporatedby reference herein.

In embodiments, the financial entity-oriented data storage systems layer3310 may include a range of systems for storage of data, such as theaccounting data 3358, access data 3362, pricing data 3364, asset andfacility data 3320, worker data 3322, event data 3324, underwriting data3360 and claims data 3354. These may include, without limitation,physical storage systems, virtual storage systems, local storagesystems, distributed storage systems, databases, memory, network-basedstorage, network-attached storage systems (such as using NVME, storageattached networks, and other network storage systems), and many others.In embodiments, the storage layer 3310 may store data in one or moreknowledge graphs (such as a directed acyclic graph, a data map, a datahierarchy, a data cluster including links and nodes, a self-organizingmap, or the like). In embodiments, the data storage layer 3310 may storedata in a digital thread, ledger, or the like, such as for maintaining alongitudinal record of an entity 3330 over time, including any of theentities described herein. In embodiments, the data storage layer 3310may use and enable a virtual asset tag 3488, which may include a datastructure that is associated with an asset and accessible and managed asif the tag were physically located on the asset, such as by use ofaccess controls, so that storage and retrieval of data is optionallylinked to local processes, but also optionally open to remote retrievaland storage options. In embodiments, the storage layer 3310 may includeone or more blockchains 3490, such as ones that store identity data,transaction data, entity data for the entities 3330, pricing data,ownership transfer data, data for operation by smart contracts 3431,historical interaction data, and the like, such as with access controlthat may be role-based or may be based on credentials associated with anentity 3330, a service, or one or more applications 3312.

Referring to FIG. 35 , the adaptive intelligent systems layer 3304 mayinclude a robotic process automation (RPA) system 3442, which mayinclude a set of components, processes, services, interfaces and otherelements for development and deployment of automation capabilities forvarious financial entities 3330, environments, and applications 3312.Without limitation, robotic process automation 3442 may be applied toeach of the processes that is managed, controlled, or mediated by eachof the set of applications 3312 of the platform application layer.

In embodiments, robotic process automation 3442 may take advantage ofthe presence of multiple applications 3312 within the financial andtransactional management application platform layer 3302, such that apair of applications may share data sources (such as in the data storagelayer 3310) and other inputs (such as from the monitoring layer 3306)that are collected with respect to financial entities 3330, as wellsharing outputs, events, state information and outputs, whichcollectively may provide a much richer environment for processautomation, including through use of artificial intelligence 3448(including any of the various expert systems, artificial intelligencesystems, neural networks, supervised learning systems, machine learningsystems, deep learning systems, and other systems described throughoutthis disclosure and in the documents incorporated by reference). Forexample, a real estate application 3424 may use robotic processautomation 3442 for automation of a real estate inspection process thatis normally performed or supervised by a human (such as by automating aprocess involving visual inspection using video or still images from acamera or other that displays images of an entity 3330, such as wherethe robotic process automation 3442 system is trained to automate theinspection by observing interactions of a set of human inspectors orsupervisors with an interface that is used to identify, diagnose,measure, parameterize, or otherwise characterize possible defects orfavorable characteristics of a house, a building, or other real estateproperty or item. In embodiments, interactions of the human inspectorsor supervisors may include a labeled data set where labels or tagsindicate types of defects, favorable properties, or othercharacteristics, such that a machine learning system can learn, usingthe training data set, to identify the same characteristics, which inturn can be used to automate the inspection process such that defects orfavorable properties are automatically classified and detected in a setof video or still images, which in turn can be used within the realestate solution 3424 to flag items that require further inspection, thatshould be rejected, that should be disclosed to a prospective buyer,that should be remediated, or the like. In embodiments, robotic processautomation 3442 may involve multi-application or cross-applicationsharing of inputs, data structures, data sources, events, states,outputs, or outcomes. For example, the real estate application 3424 mayreceive information from a platform-operated marketplace application3327 that may enrich the robotic process automation 3442 of the realestate application 3424, such as information about the current pricingof an item from a particular vendor that is located at a real estateproperty (such as a pool, spa, kitchen appliance, TV or other items),which may assist in populating the characteristics about the real estatefor purpose of facilitating an inspection process, a valuation process,a disclosure process, or the like. These and many other examples ofmulti-application or cross-application sharing for robotic processautomation 3442 across the applications 3312 are encompassed by thepresent disclosure.

In embodiments, robotic process automation may be applied to shared orconverged processes among the various pairs of the applications 3312 ofthe financial and transactional management application platform layer3302, such as, without limitation, of a converged process involving asecurity application 3418 and a lending application 3410, integratedautomation of blockchain-based applications 3422 with platform-operatedmarketplace applications 3327, and many others. In embodiments,converged processes may include shared data structures for multipleapplications 3312 (including ones that track the same transactions on ablockchain but may consume different subsets of available attributes ofthe data objects maintained in the blockchain or ones that use a set ofnodes and links in a common knowledge graph). For example, a transactionindicating a change of ownership of an entity 3330 may be stored in ablockchain and used by multiple applications 3312, such as to enablerole-based access control, role-based permissions for remote control,identity-based event reporting, and the like. In embodiments, convergedprocesses may include shared process flows across applications 3312,including subsets of larger flows that are involved in one or more of aset of applications 3312. For example, an underwriting or inspectionflow about an entity 3330 may serve a lending solution 3410, ananalytics solution 3419, an asset management solution 3404, and others.

In embodiments, robotic process automation 3442 may be provided for thewide range of financial and transactional processes mentioned throughoutthis disclosure and the documents incorporated herein by reference,including without limitation energy trading, banking, transportation,storage, energy storage, maintenance processes, service processes,repair processes, supply chain processes, inspection processes, purchaseand sale processes, underwriting processes, compliance processes,regulatory processes, fraud detection processes, fault detectionprocesses, power utilization optimization processes, and many others. Anenvironment for development of robotic process automation may include aset of interfaces for developers in which a developer may configure anartificial intelligence system 3448 to take inputs from selected datasources of the data storage layer 3310 and events or other data from themonitoring systems layer 3306 and supply them, such as to a neuralnetwork, either as inputs for classification or prediction, or asoutcomes. The RPA development environment 3442 may be configured to takeoutputs and outcomes 3328 from various applications 3312, again tofacilitate automated learning and improvement of classification,prediction, or the like that is involved in a step of a process that isintended to be automated. In embodiments, the development environment,and the resulting robotic process automation 3442 may involve monitoringa combination of both software interaction observation 3450 (e.g., byworkers interacting with various software interfaces of applications3312 involving entities 3330) and physical process interactionobservations 3458 (e.g., by watching workers interacting with or usingmachines, equipment, tools, or the like). In embodiments, softwareinteraction observation 3450 may include interactions among softwarecomponents with other software components, such as how one application3312 interacts via APIs with another application 3312. In embodiments,observation of physical process interaction observations 3458 mayinclude observation (such as by video cameras, motion detectors, orother sensors, as well as detection of positions, movements, or the likeof hardware, such as robotic hardware) of how human workers interactwith financial entities 3330 (such as locations of workers (includingroutes taken through a location, where workers of a given type arelocated during a given set of events, processes or the like, how workersmanipulate pieces of equipment or other items using various tools andphysical interfaces, the timing of worker responses with respect tovarious events (such as responses to alerts and warnings), procedures bywhich workers undertake scheduled maintenance, updates, repairs andservice processes, procedures by which workers tune or adjust itemsinvolved in workflows, and many others). Physical process interactionobservations 3458 may include tracking positions, angles, forces,velocities, acceleration, pressures, torque, and the like of a worker asthe worker operates on hardware, such as with a tool. Such observationsmay be obtained by any combination of video data, data detected within amachine (such as of positions of elements of the machine detected andreported by position detectors), data collected by a wearable device(such as an exoskeleton that contains position detectors, forcedetectors, torque detectors and the like that is configured to detectthe physical characteristics of interactions of a human worker with ahardware item for purposes of developing a training data set). Bycollecting both software interaction observations 3450 and physicalprocess interaction observations 3458 the RPA system 3442 can morecomprehensively automate processes involving financial entities 3330,such as by using software automation in combination with physicalrobots.

In embodiments, robotic process automation 3442 is configured to train aset of physical robots that have hardware elements that facilitateundertaking tasks that are conventionally performed by humans. These mayinclude robots that walk (including walking up and down stairs), climb(such as climbing ladders), move about a facility, attach to items, gripitems (such as using robotic arms, hands, pincers, or the like), liftitems, carry items, remove, and replace items, use tools and manyothers.

With reference to FIG. 35 , in embodiments provided herein is atransactional, financial and marketplace enablement system. An examplesystem may include a robotic process automation circuit structured tointerpret information from a plurality of data sources, and to interfacewith a plurality of management applications; wherein the plurality ofmanagement applications are each associated with a separate one of aplurality of financial entities; and wherein the robotic processautomation circuit further comprises an artificial intelligence circuitstructured to improve a process of at least one of the plurality ofmanagement applications in response to the information from theplurality of data sources.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the artificial intelligence circuitfurther comprises at least one circuit selected from the circuitsconsisting of: a smart contract services circuit, a valuation circuit,and an automated agent circuit.

An example system may include wherein the plurality of managementapplications comprise at least two applications selected from theapplications consisting of: an investment application, as assetmanagement application, a lending application, a risk managementapplication, a marketing application, a trading application, a taxapplication, a fraud application, a financial service application, asecurity application, an underwriting application, a blockchainapplication, a real estate application, a regulatory application, aplatform marketplace application, a warranty application, an analyticsapplication, a pricing application, and a smart contract application.

An example system may include wherein the plurality of data sourcescomprise at least two applications selected from the applicationsconsisting of: an access data source, an asset and facility data source,a worker data source, a claims data source, an accounting data source,an event data source, and an underwriting data source.

An example system may include wherein the plurality of managementapplications includes a real estate application, and wherein the roboticprocess automation circuit is further structured to automate a realestate inspection process.

An example system may include wherein the robotic process automationcircuit is further structured to automate the real estate inspectionprocess by performing at least one operation selected from theoperations consisting of: providing one of a video inspection command ora camera inspection command; utilizing data from the plurality of datasources to schedule an inspection event; and determining inspectioncriteria in response to a plurality of inspection data and inspectionoutcomes, and providing an inspection command in response to theplurality of inspection data and inspection outcomes.

An example system may include wherein the robotic process automationcircuit is further structured to automate the real estate inspectionprocess in response to at least one of the plurality of data sourcesthat is not accessible to the real estate application.

An example system may include wherein at least one of the plurality ofdata sources is not accessible to each of the at least one of theplurality of management applications having an improved process by therobotic automation circuit.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises a real estate application, and wherein theat least one of the plurality of data sources comprises at least onedata source selected from the data sources consisting of: a claims datasource, a pricing data source, an asset and facility data source, aworker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises an asset management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anaccess data source, a pricing data source, an accounting data source, aworker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises a lending management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises a marketing management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, an event data source, and an underwriting data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises a trading management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises an analytics management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anaccess data source, a claims data source, a worker data source, and anevent data source.

An example system may include wherein the robotic process automationcircuit is further structured to improve the process at least one of theplurality of management applications by providing an output to at leastone entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the robotic process automationcircuit is further structured to interpret an outcome from the at leastone entity, and wherein the artificial intelligence circuit is furtherstructured to iteratively improve the process in response to the outcomefrom the at least one entity.

Referring to FIG. 36 , a set of opportunity miners 3446 may be providedas part of the adaptive intelligent systems layer 3304, which may beconfigured to seek and recommend opportunities to improve one or more ofthe elements of the platform 3300, such as via addition of artificialintelligence 3448, automation (including robotic process automation3442), or the like to one or more of the systems, sub-systems,components, applications or the like of the platform 100 or with whichthe platform 100 interacts. In embodiments, the opportunity miners 3446may be configured or used by developers of AI or RPA solutions to findopportunities for better solutions and to optimize existing solutions.In embodiments, the opportunity miners 3446 may include a set of systemsthat collect information within the platform 100 and collect informationwithin, about and for a set of environments and entities 3330, where thecollected information has the potential to help identify and prioritizeopportunities for increased automation and/or intelligence. For example,the opportunity miners 3446 may include systems that observe clusters ofworkers by time, by type, and by location, such as using cameras,wearables, or other sensors, such as to identify labor-intensive areasand processes in set of financial environments. These may be presented,such as in a ranked or prioritized list, or in a visualization (such asa heat map showing dwell times of customers, workers, or otherindividuals on a map of an environment or a heat map showing routestraveled by customers or workers within an environment) to show placeswith high labor activity. In embodiments, analytics solutions 3419 maybe used to identify which environments or activities would most benefitfrom automation for purposes of labor saving, profit optimization, yieldoptimization, increased up time, increased throughput, increasedtransaction flow, improved security, improved reliability, or otherfactors.

In embodiments, opportunity miners 3446 may include systems tocharacterize the extent of domain-specific or entity-specific knowledgeor expertise required to undertake an action, use a program, use amachine, or the like, such as observing the identity, credentials andexperience of workers involved in given processes. This may be ofparticular benefit in situations where very experienced workers areinvolved (such as in complex transactions that require significantexperience (such as multi-party transactions); in complex back-officeprocesses involving significant expertise or training (such as riskmanagement, actuarial and underwriting processes, asset allocationprocesses, investment decision processes, or the like); in update,maintenance, porting, backup, or re-build processes on large or complexmachines; or in fine-tuning of complex processes where accumulatedexperience is required for effective work), especially where thepopulation of those workers may be scarce (such as due to retirement ora dwindling supply of new workers having the same credentials). Thus, aset of opportunity miners 3446 may collect and supply to an analyticssolution 3419, such as for prioritizing the development of automation3442, data indicating what processes of or about an entity 3330 are mostintensively dependent on workers that have particular sets of experienceor credentials, such as ones that have experience or credentials thatare scarce or diminishing. The opportunity miners 3446 may, for example,correlate aggregated data (including trend information) on worker ages,credentials, experience (including by process type) with data on theprocesses in which those workers are involved (such as by trackinglocations of workers by type, by tracking time spent on processes byworker type, and the like). A set of high value automation opportunitiesmay be automatically recommended based on a ranking set, such as onethat weights opportunities at least in part based on the relativedependence of a set of processes on workers who are scarce or areexpected to become scarcer.

In embodiments, the set of opportunity miners 3446 may use informationrelating to the cost of the workers involved in a set of processes, suchas by accessing worker data 3322, including human resource databaseinformation indicating the salaries of various workers (either asindividuals or by type), information about the rates charged by serviceworkers or other contractors, or the like. An opportunity miner 3446 mayprovide such cost information for correlation with process trackinginformation, such as to enable an analytics solution 3419 to identifywhat processes are occupying the most time of the most expensiveworkers. This may include visualization of such processes, such as byheat maps that show what locations, routes, or processes are involvingthe most expensive time of workers in financial environments or withrespect to entities 3330. The opportunity miners 3446 may supply aranked list, weighted list, or other data set indicating to developerswhat areas are most likely to benefit from further automation orartificial intelligence deployment.

In embodiments, mining an environment for robotic process automationopportunities may include searching an HR database and/or otherlabor-tracking database for areas that involve labor-intensiveprocesses; searching a system for areas where credentials of workersindicating potential for automation; tracking clusters of workers by awearable to find labor-intensive machines or processes; trackingclusters of workers by a wearable by type of worker to findlabor-intensive processes, and the like.

In embodiments, opportunity mining may include facilities forsolicitation of appropriate training data sets that may be used tofacilitate process automation. For example, certain kinds of inputs, ifavailable, would provide very high value for automation, such as videodata sets that capture very experienced and/or highly expert workersperforming complex tasks. Opportunity miners 3446 may search for suchvideo data sets as described herein; however, in the absence of success(or to supplement available data), the platform may include systems bywhich a user, such as a developer, may specify a desired type of data,such as software interaction data (such as of an expert working with aprogram to perform a particular task), video data (such as video showinga set of experts performing a certain kind of repair, an expertrebuilding a machine, an expert optimizing a certain kind of complexprocess, or the like), physical process observation data (such as video,sensor data, or the like). The specification may be used to solicit suchdata, such as by offering some form of consideration (e.g., monetaryreward, tokens, cryptocurrency, licenses or rights, revenue share, orother consideration) to parties that provide data of the requested type.Rewards may be provided to parties for supplying pre-existing dataand/or for undertaking steps to capture expert interactions, such as bytaking video of a process. The resulting library of interactionscaptured in response to specification, solicitation and rewards may becaptured as a data set in the data storage layer 3310, such as forconsumption by various applications 3312, adaptive intelligent systemslayer 3304, and other processes and systems. In embodiments, the librarymay include videos that are specifically developed as instructionalvideos, such as to facilitate developing an automation map that canfollow instructions in the video, such as providing a sequence of stepsaccording to a procedure or protocol, breaking down the procedure orprotocol into sub-steps that are candidates for automation, and thelike. In embodiments, such videos may be processed by natural languageprocessing, such as to automatically develop a sequence of labeledinstructions that can be used by a developer to facilitate a map, agraph, or other model of a process that assists with development ofautomation for the process. In embodiments, a specified set of trainingdata sets may be configured to operate as inputs to learning. In suchcases the training data may be time-synchronized with other data withinthe platform 3300, such as outputs and outcomes from applications 3312,outputs and outcomes of financial entities 3330, or the like, so that agiven video of a process can be associated with those outputs andoutcomes, thereby enabling feedback on learning that is sensitive to theoutcomes that occurred when a given process that was captured (such ason video, or through observation of software interactions or physicalprocess interactions).

In embodiments, opportunity miners 3446 may include methods, systems,processes, components, services, and other elements for mining foropportunities for smart contract definition, formation, configuration,and execution. Data collected within the platform 3300, such as any datahandled by the data handling layers 3308, stored by the data storagelayer 3310, collected by the monitoring systems layer 3306 and datacollection systems 3318, collected about or from entities 3330 orobtained from external sources may be used to recognize beneficialopportunities for application or configuration of smart contracts. Forexample, pricing information about an entity 3330, handled by a pricingapplication 3421, or otherwise collected, may be used to recognizesituations in which the same item or items is disparately priced (in aspot market, futures market, or the like), and the opportunity miner3446 may provide an alert indicating an opportunity for smart contractformation, such as a contract to buy in one environment at a price belowa given threshold and sell in another environment at a price above agiven threshold, or vice versa. In embodiments, robotic processautomation 3442 may be used to automate smart contract creation,configuration, and/or execution, such as by training on a training setof data relating to experts who form such contract or based on feedbackon outcomes from past contracts. Smart contract opportunities may alsobe recognized based on patterns, such as where predictions are used toindicate opportunities for options, futures, derivatives, forward marketcontracts, and other forward-looking contracts, such as where a smartcontract is created based on a prediction that a future condition willarise that creates an opportunity for a favorable exchange, such as anarbitrage transaction, a hedging transaction, an “in-the-money” option,a tax-favored transaction, or the like. In embodiments, at a first stepan opportunity miner 3446 seeks a price level for an item, service,good, or the like in a set of current or future markets. At a secondstep the opportunity miner 3446 determines a favorable condition for asmart contract (such as an arbitrage opportunity, tax savingopportunity, favorable option, favorable hedge, or the like). At a nextstep, the opportunity miner 3446 may initiate a smart contract processin which a smart contract is pre-configured with a description of anitem, a description of a price or other term or condition, a domain forexecution (such as a set of markets in which the contract will beformed) and a time. At a next step, an automation process may form thesmart contract and execute it within the applicable domains. At a finalstep the platform may settle the contract, such as when conditions aremet. In embodiments, the opportunity miners 3446 may be configured tomaintain a set of value translators 3447 that may be developed tocalculate exchange values of different items between and acrossdisparate domains, such as by translating the value of various resources(e.g., computational, bandwidth, energy, attention, currency, tokens,credits (e.g., tax credits, renewable energy credits, pollutioncredits), cryptocurrency, goods, licenses (e.g., government-issuedlicenses, such as for spectrum, for the right to perform services or thelike, as well as intellectual property licenses, software licenses andothers), services and other items) with respect to other such resources,including accounting for any costs of transacting across domains toconvert one resource to the other in a contract or series of contracts,such as ones executed via smart contracts. Value translators 3447 maytranslate between and among current (e.g., spot market) value, value indefined futures markets (such as day-ahead energy prices) and predictedfuture value outside defined futures markets. In embodiments,opportunity miners 3446 may operate across pairs or other combinationsof value translators (such as across, two, three, four, five or moredomains) to define a series of transaction amounts, configurations,domains, and timing that will result in generation of value by virtue ofundertaking transactions that result in favorable translation of value.For example, a cryptocurrency token may be exchanged for a pollutioncredit, which may be used to permit generation of energy, which may besold for a price that exceeds the value of the cryptocurrency token bymore than the cost of creating the smart contract and undertaking theseries of exchanges.

With reference to FIG. 36 , in embodiments provided herein is atransactional, financial and marketplace enablement system. An examplesystem may include a robotic process automation circuit structured ininterpret information from a plurality of data sources, and to interfacewith a plurality of management applications; wherein the plurality ofmanagement applications are each associated with a separate one of aplurality of financial entities; and wherein the robotic processautomation circuit further comprises an opportunity miner componentstructured to determine a process improvement opportunity for at leastone of the plurality of management applications in response to theinformation from the plurality of data sources; and to provide an outputto at least one entity associated with the process improvementopportunity in response to the determined process improvementopportunity.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the plurality of managementapplications comprise at least two applications selected from theapplications consisting of: an investment application, as assetmanagement application, a lending application, a risk managementapplication, a marketing application, a trading application, a taxapplication, a fraud application, a financial service application, asecurity application, an underwriting application, a blockchainapplication, a real estate application, a regulatory application, aplatform marketplace application, a warranty application, an analyticsapplication, a pricing application, and a smart contract application.

An example system may include wherein the plurality of data sourcescomprise at least two applications selected from the applicationsconsisting of: an access data source, an asset and facility data source,a worker data source, a claims data source, an accounting data source,an event data source, and an underwriting data source.

An example system may include wherein the at least one entity eachcomprise an entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the opportunity miner component isfurther structured to determine a plurality of process improvementopportunities for one of the plurality of management applications inresponse to the information from the plurality of data sources, and toprovide one of a prioritized list or a visualization of the plurality ofprocess improvement opportunities to the one of the plurality ofmanagement applications.

An example system may include wherein the opportunity miner component isfurther structured to determine the process improvement opportunity inresponse to at least one parameter selected from the parametersconsisting of: a time saving value, a cost saving value, and an improvedoutcome value.

An example system may include wherein the opportunity miner component isfurther structured to determine the process improvement opportunity inresponse to a value translation from a value translation application.

An example system may include wherein the plurality of managementapplications includes a trading application, and wherein the roboticprocess automation circuit is further structured to automate a tradingservice process.

An example system may include wherein the robotic process automationcircuit is further structured to automate the trading service process byperforming at least one operation selected from the operationsconsisting of: utilizing data from the plurality of data sources toschedule a trading event; and determining trading criteria in responseto a plurality of asset data and trading outcomes, and providing atrading command in response to the plurality of asset data and tradingoutcomes.

An example system may include wherein the robotic process automationcircuit is further structured to automate the trading service process inresponse to at least one of the plurality of data sources that is notaccessible to the trading application.

An example system may include wherein the robotic process automationcircuit is further structured to improve the process at least one of theplurality of management applications by providing an output to at leastone entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the robotic process automationcircuit is further structured to interpret an outcome from the at leastone entity, and wherein the opportunity miner component is furtherstructured to iteratively improve the process in response to the outcomefrom the at least one entity.

An example system may include wherein at least one of the plurality ofdata sources is not accessible to each of the at least one of theplurality of management applications having an improved process by therobotic automation circuit.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises a tax application, and wherein the at leastone of the plurality of data sources comprises at least one data sourceselected from the data sources consisting of: a claims data source, apricing data source, an asset and facility data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises an asset management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anaccess data source, a pricing data source, an accounting data source, aworker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises a lending management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises a marketing management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, an event data source, and an underwriting data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises an investment management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticautomation circuit comprises an underwriting management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anaccess data source, a claims data source, a worker data source, and anevent data source.

Referring to FIG. 37 , additional details of an embodiment of theplatform transactional, financial and marketplace enablement system areprovided, in particular relating to elements of the adaptive intelligentsystems layer 3304 that facilitate improved edge intelligence, includingthe adaptive edge compute management system 3430 and the edgeintelligence system 3438. These elements provide a set of systems thatadaptively manage “edge” computation, storage, and processing, such asby varying storage locations for data and processing locations (e.g.,optimized by AI) between on-device storage, local systems, in thenetwork and in the cloud. These elements 3430, 3438 enable facilitationof a dynamic definition by a user, such as a developer, operator, orhost of the platform 100, of what constitutes the “edge” for purposes ofa given application. For example, for environments where dataconnections are slow or unreliable (such as where a facility does nothave good access to cellular networks (such as due to remoteness of someenvironments (such as in geographies with poor cellular networkinfrastructure), shielding or interference (such as where density ofnetwork-using systems, thick walls, underground location, or presence oflarge metal objects (such as vaults) interferes with networkingperformance), and/or congestion (such as where there are many devicesseeking access to limited networking facilities), edge computingcapabilities can be defined and deployed to operate on the local areanetwork of an environment, in peer-to-peer networks of devices, or oncomputing capabilities of local financial entities 3330. Where strongdata connections are available (such as where good back-haul facilitiesexist), edge computing capabilities can be disposed in the network, suchas for caching frequently used data at locations that improveinput/output performance, reduce latency, or the like. Thus, adaptivedefinition and specification of where edge computing operations isenabled, under control of a developer or operator, or optionallydetermined automatically, such as by an expert system or automationsystem, such as based on detected network conditions for an environment,for an entity 3330, or for a network as a whole. In embodiments, an edgeintelligence system 3438 enables adaptation of edge computation(including where computation occurs within various available networkingresources, how networking occurs (such as by protocol selection), wheredata storage occurs, and the like) that is multi-application aware, suchas accounting for QoS, latency requirements, congestion, and cost asunderstood and prioritized based on awareness of the requirements, theprioritization, and the value (including ROI, yield, and costinformation, such as costs of failure) of edge computation capabilitiesacross more than one application, including any combinations and subsetsof the applications 3312 described herein or in the documentsincorporated herein by reference.

With reference to FIG. 37 , in embodiments provided herein is atransactional, financial and marketplace enablement system. An examplesystem may include an adaptive edge computing circuit structured tointerpret information from a plurality of data sources, and to interfacewith a plurality of management applications; wherein the plurality ofmanagement applications are each associated with a separate one of aplurality of financial entities; and wherein the adaptive edge computingcircuit further comprises an edge intelligence component structured todetermine an edge intelligence process improvement for at least one ofthe plurality of management applications in response to the informationfrom the plurality of data sources.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the plurality of managementapplications comprise at least two applications selected from theapplications consisting of: an investment application, as assetmanagement application, a lending application, a risk managementapplication, a marketing application, a trading application, a taxapplication, a fraud application, a financial service application, asecurity application, an underwriting application, a blockchainapplication, a real estate application, a regulatory application, aplatform marketplace application, a warranty application, an analyticsapplication, a pricing application, and a smart contract application.

An example system may include wherein the plurality of data sourcescomprise at least two applications selected from the applicationsconsisting of: an access data source, an asset and facility data source,a worker data source, a claims data source, an accounting data source,an event data source, and an underwriting data source.

An example system may include wherein the at least one entity eachcomprise an entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the edge intelligence component isfurther structured to determine a plurality of process improvementopportunities for one of the plurality of management applications inresponse to the information from the plurality of data sources, and toprovide one of a prioritized list or a visualization of the plurality ofprocess improvement opportunities to the one of the plurality ofmanagement applications.

An example system may include wherein the edge intelligence component isfurther structured to determine a process improvement opportunity inresponse to at least one parameter selected from the parametersconsisting of: a time saving value, a cost saving value, and an improvedoutcome value.

An example system may include wherein the plurality of managementapplications includes a security application, and wherein the adaptiveedge computing circuit is further structured to automate a securityservice process.

An example system may include wherein the adaptive edge computingcircuit is further structured to automate the security service processby performing at least one operation selected from the operationsconsisting of: utilizing data from the plurality of data sources toschedule a security event; and determining security criteria in responseto a plurality of asset data and security outcomes, and providing asecurity command in response to the plurality of asset data and securityoutcomes.

An example system may include wherein the adaptive edge computingcircuit is further structured to automate the security service processin response to at least one of the plurality of data sources that is notaccessible to the security application.

An example system may include wherein the adaptive edge computingcircuit is further structured to improve the process at least one of theplurality of management applications by providing an output to at leastone entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the adaptive edge computingcircuit is further structured to interpret an outcome from the at leastone entity, and wherein the edge intelligence component is furtherstructured to iteratively improve the process in response to the outcomefrom the at least one entity.

An example system may include wherein at least one of the plurality ofdata sources is not accessible to each of the at least one of theplurality of management applications having an improved process by theadaptive edge computing circuit.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveedge computing circuit comprises a risk application, and wherein the atleast one of the plurality of data sources comprises at least one datasource selected from the data sources consisting of: a claims datasource, a pricing data source, an asset and facility data source, aworker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveedge computing circuit comprises an asset management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anaccess data source, a pricing data source, an accounting data source, aworker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveedge computing circuit comprises a security management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveedge computing circuit comprises a platform marketplace application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, an event data source, and an underwriting data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveedge computing circuit comprises a platform marketplace application, andwherein the adaptive edge computing circuit is further structured tooperate an interface to interpret an edge definition, and wherein anedge intelligence component is further structured to determine the edgeintelligence process improvement in response to the edge definition.

An example system may include wherein the edge definition comprises anidentification of at least one of the following parameters: a slow dataconnection, an unreliable data connection, a network interferencedescription, a network caching description, a quality of servicerequirement, or a latency requirement.

Referring to FIG. 38 , additional details, components, sub-systems, andother elements of an optional embodiment of the storage layer 3310 ofthe transactional, financial and marketplace enablement system 3300 areillustrated, relating in particular to embodiments that may include ageofenced virtual asset tag 3488, such as for one or more assets withinthe asset and facility data 3320 described throughout this disclosureand the document incorporated by reference herein. In embodiments, thevirtual asset tag is a data structure that contains data about an entity3330, such as an asset (which may be physical or virtual), machine, itemof equipment, item of inventory, manufactured article, certificate (suchas a stock certificate), deed, component, tool, device, or worker (amongothers), where the data is intended to be tagged to the asset, such aswhere the data relates uniquely to the particular asset (e.g., to aunique identifier for the individual asset) and is linked to proximityor location of the asset (such as being geofenced to an area or locationof or near the asset, or being associated with a geo-located digitalstorage location or defined domain for a digital asset). The virtualasset tag is thus functionally equivalent to a physical asset tag, suchas an RFID tag, in that it provides a local reader or similar device toaccess the data structure (as a reader would access an RFID tag), and inembodiments, access control is managed as if the tag were physicallocated on an asset; for example, certain data may be encrypted withkeys that only permit it to be read, written to, modified, or the likeby an operator who is verified to be in the proximity of a taggedfinancial entity 3330, thereby allowing partitioning of local-only dataprocessing from remote data processing. In embodiments, the virtualasset tag may be configured to recognize the presence of an RF reader orother reader (such as by recognition of an interrogation signal) andcommunicate to the reader, such as with help of protocol adaptors, suchas over an RF communication link with the reader, notwithstanding theabsence of a conventional RFID tag. This may occur by communicationsfrom IoT devices, telematics systems, and by other devices residing on alocal area network. In embodiments, a set of IoT devices in amarketplace or financial or transactional environment can act asdistributed blockchain nodes, such as for storage of virtual asset tagdata, for tracking of transactions, and for validation (such as byvarious consensus protocols) of enchained data, including transactionhistory for maintenance, repair and service. In embodiments, the IoTdevices in a geofence can collectively validate location and identity ofa fixed asset that is tagged by a virtual asset tag, such as where peersor neighbors validate other peers or neighbors as being in a givenlocation, thereby validating the unique identity and location of theasset. Validation can use voting protocols, consensus protocols, or thelike. In embodiments, identity of the financial entities that are taggedcan be maintained in a blockchain. In embodiments, an asset tag mayinclude information that is related to a digital thread 3484, such ashistorical information about an asset, its components, its history, andthe like.

With reference to FIG. 38 , in embodiments provided herein is atransactional, financial and marketplace enablement system. An examplesystem may include an adaptive intelligence circuit structured ininterpret information from a plurality of data sources, and to interfacewith a plurality of management applications, wherein the adaptiveintelligence circuit comprises a protocol adapter component; wherein theplurality of management applications are each associated with a separateone of a plurality of financial entities; and wherein the adaptiveintelligence circuit further comprises an artificial intelligencecomponent structured to determine an artificial intelligence processimprovement for at least one of the plurality of management applicationsin response to the information from the plurality of data sources.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein at least one of the plurality of datasources is a mobile data collector.

An example system may include wherein the adaptive intelligence circuitfurther comprises a protocol adapter component structured to determine acommunication protocol facilitating communication between an entityaccessing the at least one of the plurality of management applicationshaving an improved process.

An example system may include wherein the entity accessing the at leastone of the plurality of management applications comprises an operatorrelated to the at least one of the plurality of management applications,and wherein the protocol adapter component is further structured todetermine the communication protocol as a protocol enabling encryptedcommunications in response to a determination from the mobile datacollector that the operator is in a proximity of a tagged financialentity.

An example system may include wherein the mobile data collector collectsdata from at least one geofenced virtual asset tag.

An example system may include wherein the adaptive intelligence circuitfurther comprises a protocol adapter component structured to determine acommunication protocol facilitating communication between an entityaccessing the at least one of the plurality of management applicationshaving an improved process.

An example system may include wherein the entity accessing the at leastone of the plurality of management applications comprises an operatorrelated to the at least one of the plurality of management applications,and wherein the protocol adapter component is further structured todetermine the communication protocol as a protocol enabling encryptedcommunications in response to a determination from the at least onegeofenced virtual asset tag that the operator is in a proximity of atagged financial entity.

An example system may include wherein at least one of the plurality ofdata sources is an Internet of Things data collector.

An example system may include wherein the adaptive intelligence circuitfurther comprises a protocol adapter component structured to determine acommunication protocol facilitating communication between an entityaccessing the at least one of the plurality of management applicationshaving an improved process.

An example system may include wherein the entity accessing the at leastone of the plurality of management applications comprises an operatorrelated to the at least one of the plurality of management applications,and wherein the protocol adapter component is further structured todetermine the communication protocol as a protocol enabling encryptedcommunications in response to a determination from the Internet ofThings data collector that the operator is in a proximity of a taggedfinancial entity.

An example system may include wherein at least one of the plurality ofdata sources is a blockchain circuit, and wherein the adaptiveintelligence circuit interprets the information from the blockchaincircuit utilizing the adaptive intelligence circuit.

An example system may include wherein the plurality of managementapplications comprise at least two applications selected from theapplications consisting of: an investment application, an assetmanagement application, a lending application, a risk managementapplication, a marketing application, a trading application, a taxapplication, a fraud application, a financial service application, asecurity application, an underwriting application, a blockchainapplication, a real estate application, a regulatory application, aplatform marketplace application, a warranty application, an analyticsapplication, a pricing application, and a smart contract application.

An example system may include wherein the plurality of data sourcescomprise at least two applications selected from the applicationsconsisting of: an access data source, an asset and facility data source,a worker data source, a claims data source, an accounting data source,an event data source, and an underwriting data source.

An example system may include wherein the at least one entity eachcomprises an entity selected from the entities consisting of: anexternal marketplace, a banking facility, an insurance facility, afinancial service facility, an operating facility, a collaborativerobotics facility, a worker, a wearable device, an external process, anda machine.

An example system may include wherein the artificial intelligencecomponent is further structured to determine a plurality of processimprovement opportunities for one of the plurality of managementapplications in response to the information from the plurality of datasources, and to provide one of a prioritized list or a visualization ofthe plurality of process improvement opportunities to the one of theplurality of management applications.

An example system may include wherein the artificial intelligencecomponent is further structured to determine a process improvementopportunity in response to at least one parameter selected from theparameters consisting of: a time saving value, a cost saving value, andan improved outcome value.

An example system may include wherein the plurality of managementapplications includes a risk management application, and wherein theadaptive intelligence circuit is further structured to automate a riskmanagement process.

An example system may include wherein the adaptive intelligence circuitis further structured to automate the risk management process byperforming at least one operation selected from the operationsconsisting of: utilizing data from the plurality of data sources toschedule a risk event; determining risk criteria in response to aplurality of asset data and risk outcomes, and providing a risk commandin response to the plurality of asset data and risk management outcomes;and adjusting a geofencing location to provide at least one of animproved access for an operator related to at least one of the pluralityof management applications or improve a security of communications to atleast one of the plurality of management applications.

An example system may include wherein the adaptive intelligence circuitis further structured to automate the risk management process inresponse to at least one of the plurality of data sources that is notaccessible to the risk management application.

An example system may include wherein the adaptive intelligence circuitis further structured to improve the process of at least one of theplurality of management applications by providing an output to at leastone entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the adaptive intelligence circuitis further structured to interpret an outcome from the at least oneentity, and wherein the artificial intelligence component is furtherstructured to iteratively improve the process in response to the outcomefrom the at least one entity.

An example system may include wherein at least one of the plurality ofdata sources is not accessible to each of the at least one of theplurality of management applications having an improved process by theadaptive intelligence circuit.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveintelligence circuit comprises a smart contract application, and whereinthe at least one of the plurality of data sources comprises at least onedata source selected from the data sources consisting of: a claims datasource, a pricing data source, an asset and facility data source, aworker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveintelligence circuit comprises an asset management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anaccess data source, a pricing data source, an accounting data source, aworker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveintelligence circuit comprises a security management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveintelligence circuit comprises a marketing management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, an event data source, and an underwriting data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveintelligence circuit comprises a pricing management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anasset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the adaptiveintelligence circuit comprises a warranty management application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: anaccess data source, a claims data source, a worker data source, and anevent data source.

Referring to FIG. 39 , in embodiments, a unified RPA system 3442, suchas for developing and deploying one or more automation capabilities mayinclude or enable capabilities for robot operational analytics 3902,such as for analyzing operational actions of a set of robots, includingwith respect to location, mobility and routing of mobile robots, as wellas with respect to motions of robot components, such as where roboticcomponents are used within a wide range of protocols or procedures, suchas banking processes, underwriting processes, insurance processes, riskassessment processes, risk mitigation processes, inspection processes,exchange processes, sale processes, purchase processes, deliveryprocesses, warehousing processes, assembly processes, transportprocesses, maintenance and repair processes, data collection processes,and many others.

In embodiments, the RPA system 3442 may include or enable capabilitiesfor machine learning on unstructured data 3909, such as learning on atraining set of human labels, tags, or other activities that allowcharacterization of the unstructured data, extraction of content fromunstructured data, generation of diagnostic codes or similar summariesfrom content of unstructured data, or the like. For example, the RPAsystem 3442 may include sub-systems or capabilities for processing PDFs(such as technical data sheets, functional specifications, repairinstructions, user manuals and other documentation about financialentities 3330, such as machines and systems), for processinghuman-entered notes (such as notes involved in diagnosis of problems,notes involved in prescribing or recommending actions, notes involved incharacterizing operational activities, notes involved in maintenance andrepair operations, and many others), for processing informationunstructured content contained on websites, social media feeds and thelike (such as information about products or systems in an financialenvironment that can be obtained from vendor websites), and many others.

In embodiments, the RPA system 3442 may comprise a unified platform witha set of RPA capabilities, as well as systems for monitoring (such asthe systems of the monitoring systems layer 3306 and data collectionsystems 3318), systems for raw data processing 3904 (such as by opticalcharacter recognition (OCR), natural language processing (NPL), computervision processing, sound processing, sensor processing and the like);systems for workflow characterization and management 3908; analyticscapabilities 3910; artificial intelligence capabilities 3448; andadministrative systems 3914, such as for policy, governance,provisioning (such as of services, roles, access controls, and the like)among others. The RPA system 3442 may include such capabilities as a setof microservices in a microservices architecture. The RPA system 3442may have a set of interfaces to other platform layers 3308, as well asto external systems, for data exchange, such that the RPA system 3442can be accessed as an RPA platform-as-a-service by external systems thatcan benefit from one or more automation capabilities.

In embodiments, the RPA system 3442 may include a quality-of-workcharacterization capability 3912, such as one that identifies highquality work as compared to other work. This may include recognizinghuman work as different from work performed by machines, recognizingwhich human work is likely to be of highest quality (such as workinvolving the most experienced or expensive personnel), recognizingwhich machine-performed work is likely to be of the highest quality(such as work that is performed by machines that have extensivelylearned on feedback from many outcomes, as compared to machines that arenewly deployed, and recognizing which work has historically providedfavorable outcomes (such as based on analytics or correlation to pastoutcomes). A set of thresholds may be applied, which may be varied undercontrol of a developer or other user of the RPA system 3442, such as toindicate by type, by quality-level, or the like, which data setsindicating past work will be used for training within machine learningsystems that facilitate automation.

With reference to FIG. 39 , in embodiments provided herein is atransactional, financial and marketplace enablement system. An examplesystem may include an robotic process automation circuit structured ininterpret information from a plurality of data sources, and to interfacewith a plurality of management applications; wherein the plurality ofmanagement applications are each associated with a separate one of aplurality of financial entities; and wherein the robotic processautomation circuit further comprises a robot operational analyticscomponent structured to determine a robot operational processimprovement for at least one of the plurality of management applicationsin response to the information from the plurality of data sources.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may further include an administrative system circuitstructured to adapt the robot operational process improvement through atleast one of governance of robotic operations, provisioning roboticoperations, or robotic operations policies.

An example system may include wherein the robot operational processimprovement comprises a robotic workflow characterization andimprovement.

An example system may further include an opportunity mining circuitstructured to adapt the operational process improvement to one of theplurality of management applications.

An example system may include wherein the robot operational processimprovement comprises a robotic quality of work characterization andimprovement.

An example system may include wherein the robot operational analyticscomponent comprises a robotics machine learning component for processingthe information from a plurality of data sources to determine the robotoperational process improvement.

An example system may include wherein the robot operational analyticscomponent comprises a raw data processing component for processing theinformation from a plurality of data sources to determine the robotoperational process improvement.

An example system may include wherein the plurality of managementapplications comprise at least two applications selected from theapplications consisting of: an investment application, as assetmanagement application, a lending application, a risk managementapplication, a marketing application, a trading application, a taxapplication, a fraud application, a financial service application, asecurity application, an underwriting application, a blockchainapplication, a real estate application, a regulatory application, aplatform marketplace application, a warranty application, an analyticsapplication, a pricing application, and a smart contract application.

An example system may include wherein the plurality of data sourcescomprise at least two applications selected from the applicationsconsisting of: an access data source, an asset and facility data source,a worker data source, a claims data source, an accounting data source,an event data source, and an underwriting data source.

An example system may include wherein the at least one entity eachcomprise an entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the robot operational analyticscomponent is further structured to determine a plurality of processimprovement opportunities for one of the plurality of managementapplications in response to the information from the plurality of datasources, and to provide one of a prioritized list or a visualization ofthe plurality of process improvement opportunities to the one of theplurality of management applications.

An example system may include wherein the robot operational analyticscomponent is further structured to determine a process improvementopportunity in response to at least one parameter selected from theparameters consisting of: a time saving value, a cost saving value, andan improved outcome value.

An example system may include wherein the plurality of managementapplications includes a regulatory management application, and whereinthe robotic process automation circuit is further structured to automatea regulatory management process.

An example system may include wherein the robotic process automationcircuit is further structured to automate the regulatory managementprocess by performing at least one operation selected from theoperations consisting of: utilizing data from the plurality of datasources to schedule a regulatory event; and determining regulatorycriteria in response to a plurality of asset data and regulatoryoutcomes, and providing a regulatory command in response to theplurality of asset data and regulatory management outcomes.

An example system may include wherein the robotic process automationcircuit is further structured to automate the regulatory managementprocess in response to at least one of the plurality of data sourcesthat is not accessible to the regulatory management application.

An example system may include wherein the robotic process automationcircuit is further structured to improve the process at least one of theplurality of management applications by providing an output to at leastone entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the robotic process automationcircuit is further structured to interpret an outcome from the at leastone entity, and wherein the robot operational analytics component isfurther structured to iteratively improve the process in response to theoutcome from the at least one entity.

An example system may include wherein at least one of the plurality ofdata sources is not accessible to each of the at least one of theplurality of management applications having an improved process by therobotic process automation circuit.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises an investment application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: aclaims data source, a pricing data source, an asset and facility datasource, a worker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises an asset management application,and wherein the at least one of the plurality of data sources comprisesat least one data source selected from the data sources consisting of:an access data source, a pricing data source, an accounting data source,a worker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises a security management application,and wherein the at least one of the plurality of data sources comprisesat least one data source selected from the data sources consisting of:an asset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises a marketing management application,and wherein the at least one of the plurality of data sources comprisesat least one data source selected from the data sources consisting of:an asset and facility data source, a claims data source, a worker datasource, an event data source, and an underwriting data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises a pricing management application,and wherein the at least one of the plurality of data sources comprisesat least one data source selected from the data sources consisting of:an asset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises a warranty management application,and wherein the at least one of the plurality of data sources comprisesat least one data source selected from the data sources consisting of:an access data source, a claims data source, a worker data source, andan event data source.

Referring to FIG. 40 , in embodiments, various systems, methods,processes, services, components and other elements for enabling ablockchain and smart contract platform for a forward market 4002 foraccess rights to events. Within a transactional enablement system suchas described in connection with various embodiments of the platform3300, a blockchain application 3422 and associated smart contract 3431may be used to enable a forward market 4002 for access rights to events,such as where one or more event tickets, seat licenses, access rights,rights of entry, passes (e.g., backstage passes) or other itemsrepresenting, comprising or embodying an access token for the right toattend, enter, view, consume, or otherwise participate in an event(which may be a live event, a recorded event, an event at a physicalvenue, a digital content event, or other event to which access iscontrolled)(all of which are encompassed by the term access token 4008as used herein, except where context indicates otherwise) is securelystored on a blockchain that is configured by a blockchain application3422, such as one in which the blockchain 3422 comprises a ledger oftransactions in access tokens 4008 (such term comprising tickets andother evidence of the right to access the event), such as withindications of ownership (including identity information, eventinformation, token information, information about terms and conditions,and the like) and a record of transfer of ownership (including terms,condition and policies regarding transferability). In embodiments, sucha blockchain-based access token may be traded in a platform-operatedmarketplace application 3327, such as one configured to operate with orfor a spot market or forward market 4002. In embodiments, the forwardmarket 4002 operated within or by the platform may be a contingentforward market, such as one where a future right vests, is triggered, oremerges based on the occurrence of an event, satisfaction of acondition, or the like, such as enabled by a smart contract 3431 thatoperates on one or more data structures in or associated with aplatform-operated marketplace 3327 or an external marketplace 3390 toexecute or apply a rule, term, condition or the like, optionallyresulting in a transaction that is recorded in the blockchain (such ason a distributed ledger on the blockchain), which may, in turn, initiateother processes and result in other smart contract operations. In suchembodiments, a condition triggering an event may include an eventpromotor or other party scheduling an event having a defined set ofparameters, an event arising having such parameters, or the like, andthe blockchain-based access token 4008 may be configured (optionally inconjunction with a smart contract 3431 and with one or more monitoringsystems 3306) to recognize the presence or existence, such as in anexternal marketplace 3390 of an event, or an access token to an event,that satisfies the defined set of parameters and to initiate anoperation with respect to the access token, such as reporting theexistence of availability of the access token, transferring access tothe access token, transferring ownership, setting a price, or the like.In embodiments, monitoring system layers 3306 may monitor externalmarketplaces 3390 for relevant events, tokens, and the like, as well asfor information indicating the emergence of conditions that satisfy oneor more conditions that result in triggering, vesting, or emergence of acondition that impacts an access token or event. As an illustrativeexample, a sporting event access token 4008 to a playoff game may beconfigured to vest upon the presence of a specific team in a specificgame (e.g., the Super Bowl), at which point the right to a ticket to aspecific seat may be automatically allocated on a distributed ledger,enabled by a blockchain, to the individual listed on the ledger ashaving the right to the ticket for that team. Thus, a distributed ledgeror other blockchains 3422 may securely maintain multiple prospectiveowners for an event token 4008 for the same event, provided accessrights can be divided such that they are mutually exclusive but can bedesignated to a specific owner upon the emergence of a condition (e.g.,a particular seat at a game, concert, or the like) and allocateownership to a specific owner based on upon the emergence of a conditionthat determines which prospective owner has the right to become theactual owner (e.g., that owner's team makes it to the game). In theexample of a sports league, the blockchain can thus maintain as manyowners as there are mutually exclusive conditions for a seat (e.g., byallocating seats across all teams in a conference for the Super Bowl, orall teams in a division for a college football conference final). Thedefined set of parameters may include location (where anas-yet-unscheduled event takes place), participants (teams, individuals,and many others), prices (such as the access token is priced below adefined threshold), timing (such as a span of hours, days, months,years, or other periods), type of event (sports, concerts, comedyperformances, theatrical performances, political events, and manyothers) and others. In embodiments, one or more monitoring system layers3306 or other data collection systems may be configured to monitor oneor more external marketplaces 3390 or platform-operated marketplaces(such as on e-commerce websites and applications, auction sites andapplications, social media sites and applications, exchange sites andapplications, ticketing sites and applications, travel sites andapplications, hospitality sites and applications, concert promotionalsites and applications, or other sites or applications) or otherentities for indicators of available events, for prospective conditionsthat can be used to define potentially divisible or mutually exclusiveaccess right conditions (such as for identifying events that can beconfigured on a multi-party distributed ledger with conditional accessdistributed across different prospective owners, optionally conductedvia one or more opportunity miners 3446) and for actual conditions thatmay trigger distribution of rights to a specific owner based on theconditions. Thus, the blockchain may be used to make a contingent marketin any form of event or access token by securely storing access rightson a distributed ledger, and the contingent market may be automated byconfiguring data collection and a set of business rules that operateupon collected data to determine when ownership rights should be vested,transferred, or the like. Post-vesting of a contingency (or set ofcontingencies), the access token may continue to be traded, with theblockchain providing a secure method of validating access. Security maybe provided by encryption of the chain as with cryptocurrency tokens(and a cryptocurrency token may itself comprise a forward-marketcryptocurrency token for event access), with proof of work, proof ofstake, or other methods for validation in the case of disputes.

In embodiments, the platform 400 may include or interact with variousapplications, services, solutions or the like, such as those describedin connection with the platform 3300, such as pricing applications 3421(such as for setting and monitoring pricing for contingent accessrights, underlying access rights, tokens, fees and the like), analyticssolutions 3419 (such as for monitoring, reporting, predicting, andotherwise analyzing all aspects of the platform 4000, such as tooptimize offerings, timing, pricing, or the like, to recognize andpredict patterns, to establish rules and contingencies, to establishmodels or understanding for use by humans or by machine learning system,and for many other purposes), trading applications 3428 (such as fortrading or exchanging contingent access rights or underlying accessrights or tokens), security applications 3418, or the like.

With reference to FIG. 40 , in embodiments provided herein is atransactional, financial and marketplace enablement system. An examplesystem may include an robotic process automation circuit structured ininterpret information from a plurality of data sources, and to interfacewith a plurality of management applications; wherein the plurality ofmanagement applications are each associated with a separate one of aplurality of financial entities; and wherein the robotic processautomation circuit further comprises an opportunity mining componentstructured to determine a robot operational process improvement for atleast one of the plurality of management applications in response to theinformation from the plurality of data sources.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may further include a data collection circuit structuredto collect and record physical process observation data, wherein thephysical process observation data is one of the plurality of datasources.

An example system may further include a data collection circuitstructured to collect and record software interaction observation data,wherein the software interaction observation data is one of theplurality of data sources.

An example system may include wherein the plurality of managementapplications comprise at least two applications selected from theapplications consisting of: a forward market application, an eventaccess tokens application, a security application, a blockchainapplication, a platform marketplace application, an analyticsapplication, a pricing application, and a smart contract application.

An example system may include wherein the plurality of data sourcescomprise at least two applications selected from the applicationsconsisting of: an access data source, an asset and facility data source,a worker data source, a claims data source, an accounting data source,an event data source, and an underwriting data source.

An example system may include wherein the at least one entity eachcomprise an entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the opportunity mining componentis further structured to determine a plurality of process improvementopportunities for one of the plurality of management applications inresponse to the information from the plurality of data sources, and toprovide one of a prioritized list or a visualization of the plurality ofprocess improvement opportunities to the one of the plurality ofmanagement applications.

An example system may include wherein the opportunity mining componentis further structured to determine a process improvement opportunity inresponse to at least one parameter selected from the parametersconsisting of: a time saving value, a cost saving value, and an improvedoutcome value.

An example system may include wherein the plurality of managementapplications includes a trading management application, and wherein therobotic process automation circuit is further structured to automate atrading management process.

An example system may include wherein the robotic process automationcircuit is further structured to automate the trading management processby performing at least one operation selected from the operationsconsisting of: utilizing data from the plurality of data sources toschedule a trading event; and determining trading criteria in responseto a plurality of asset data and trading outcomes, and providing atrading command in response to the plurality of asset data and tradingmanagement outcomes.

An example system may include wherein the robotic process automationcircuit is further structured to automate the trading management processin response to at least one of the plurality of data sources that is notaccessible to the trading management application.

An example system may include wherein the robotic process automationcircuit is further structured to improve the process at least one of theplurality of management applications by providing an output to at leastone entity selected from the entities consisting of: an externalmarketplace, a banking facility, an insurance facility, a financialservice facility, an operating facility, a collaborative roboticsfacility, a worker, a wearable device, an external process, and amachine.

An example system may include wherein the robotic process automationcircuit is further structured to interpret an outcome from the at leastone entity, and wherein the opportunity mining component is furtherstructured to iteratively improve the process in response to the outcomefrom the at least one entity.

An example system may include wherein at least one of the plurality ofdata sources is not accessible to each of the at least one of theplurality of management applications having an improved process by therobotic process automation circuit.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises a forward market application, andwherein the at least one of the plurality of data sources comprises atleast one data source selected from the data sources consisting of: aclaims data source, a pricing data source, an asset and facility datasource, a worker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises an event access tokens managementapplication, and wherein the at least one of the plurality of datasources comprises at least one data source selected from the datasources consisting of: an access data source, a pricing data source, anaccounting data source, a worker data source, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises a security management application,and wherein the at least one of the plurality of data sources comprisesat least one data source selected from the data sources consisting of:an asset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises a blockchain managementapplication, and wherein the at least one of the plurality of datasources comprises at least one data source selected from the datasources consisting of: an asset and facility data source, a claims datasource, a worker data source, an event data source, and an underwritingdata source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises a pricing management application,and wherein the at least one of the plurality of data sources comprisesat least one data source selected from the data sources consisting of:an asset and facility data source, a claims data source, a worker datasource, and an event data source.

An example system may include wherein the at least one of the pluralityof management applications having an improved process by the roboticprocess automation circuit comprises an analytics managementapplication, and wherein the at least one of the plurality of datasources comprises at least one data source selected from the datasources consisting of: an access data source, a claims data source, aworker data source, and an event data source.

Referring to FIG. 41 , a platform-operated marketplace 3327 for aforward market to access rights to one or more events may be configured,such as in a dashboard 4118 or other user interface for an operator ofthe platform-operated marketplace 3327, using the various enablingcapabilities of the data handling transactional, financial andmarketplace enablement system 3300 described throughout this disclosure.The operator may use the user interface or dashboard 4118 to undertake aseries of steps to perform or undertake an algorithm to create acontingent forward market event access right token as described inconnection with FIG. 40 . In embodiments, one or more of the steps ofthe algorithm to create a contingent forward market event access righttoken within the dashboard 4118 may include identifying one or moreaccess rights for one or more events at a component 4102 to identifyaccess rights, such as by monitoring one or more platform-operatedmarketplaces 3327 or external marketplaces 3390 for messages,announcements, or other data indicative of the event or access right.The dashboard 4118 may be configured with interface elements (includingapplication programming elements) that allow the event to be importedinto the platform-operated marketplace 3327, such as by linking to theenvironment where the access right is offered or maintained, which mayinclude using APIs for backend ticketing systems and the like. In thedashboard 4118, at a component 4104, one or more conditions (of the typedescribed herein) for the access right may be configured (e.g., byinterfacing with a user), such as by defining a set of mutuallyexclusive conditions that, upon triggering, allocate the access right todifferent individuals or entities. The user interface of the dashboard4118 may include a set of drop-down menus, tables, forms, or the likewith default, templated, recommended, or pre-configured conditions, suchas ones that are appropriate for various types of access rights. Forexample, access rights to a playoff game for a sporting event can bepreconfigured to set an access condition as the presence of a specificteam in the playoff game, where the team is a member of the set of teamsthat could be in the game, and access rights are allocated to a givenseat across mutually exclusive possible teams that could make it to thegame (e.g., the teams in one conference for the Super Bowl). As anotherexample, access rights to an as-yet-unplanned entertainment event couldbe preconfigured to set conditions such as a venue, a span of dates anda selected entertainer or group. Once the conditions and otherparameters of the access rights are configured, at a component 4108 ablockchain may be configured to maintain, such as via a ledger, the datarequired to provision, allocate, and exchange ownership of thecontingent access rights (and optionally the underlying access tokens towhich the contingent access rights relate). For example, a ticket to agame may be stored as a cryptographically secure token on the ledger,and another token may be created and stored on the blockchain for eachcontingent access right that could result in the ownership of theticket. The blockchain may be configured to store tokens, identityinformation, transaction information (such as for exchanges ofcontingent rights and/or underlying tokens) and other data. At acomponent 4110 a smart contract 3431 may be configured to embody theconditions that were configured at the component 4104, and to operate onthe blockchain that was created at the component 4108 as well as tooperate on other data, such as data indicating facts, conditions,events, or the like in the platform-operated marketplace 3327 and/or anexternal marketplace 3390. The smart contract may be configured at acomponent 4110 to apply one or more rules, execute one or moreconditional operations, or the like upon data that may include eventdata 3324, access data 3362, pricing data 3364 or other data about orrelevant to access rights. Once configuration of one or more blockchainsand one or more smart contracts is complete, at a component 4112 theblockchain and smart contract may be deployed in the platform-operatedmarketplace, such as for interaction by one or more consumers or otherusers, who may, such as in a marketplace interface, such as a website,application, or the like, enter into the smart contract, such as bypurchasing a contingent right to a future event, at which point theplatform, such as using the adaptive intelligent systems layer 3304 orother capabilities, may store relevant data, such as pricing data andidentity data for the party or parties entering the smart contract onthe blockchain or otherwise on the platform 3300. At a component 4114,once the smart contract is executed, the component 4114 may monitor,such as by the monitoring systems layer 3306, the platform-operatedmarketplace 3327 and/or one or more external marketplaces 3390 for eventdata 3324, access data 3362, pricing data 3364 or other data, such asevents, that may satisfy one or more conditions or trigger applicationof one or more rules of the smart contract. For example, results ofgames or announcements of future entertainment events may be monitored,and smart contract conditions may be satisfied. At a component 4116,upon satisfaction of conditions, smart contracts may be settled,executed, or the like, resulting updates or other operations on theblockchain, such as by transferring ownership of underlying accesstokens and/or contingent access tokens. Thus, via operation of theabove-referenced components, an operator of the platform-operatedmarketplace 3327 may discover, configure, deploy, and have executed aset of smart contracts that offer and deliver contingent access tofuture events that are cryptographically secured and transferred on ablockchain to consumers or others. In embodiments, the adaptiveintelligent systems layer 3304 may be used to monitor the steps of thealgorithm described above, and one or more artificial intelligencesystems may be used to automated, such as by robotic process automation,the entire process or one or more sub-steps or sub-algorithms. This mayoccur as described above, such as by having an artificial intelligencesystem learn on a training set of data resulting from observations, suchas monitoring software interactions, of human users as they undertakethe above-referenced steps. Once trained, the adaptive intelligentsystems layer 3304 may thus enable the transactional, financial andmarketplace enablement system 3300 to provide a fully automated platformfor discovery and delivery of contingent access rights to future events.

Referencing FIG. 42 , in embodiments, a platform is provided herein,with systems, methods, processes, services, components and otherelements for enabling a blockchain and smart contract platform forforward market demand aggregation 4002. In this case, a demandaggregation blockchain and smart contract platform 4200, having variousfeatures and enabled by capabilities similar to those described inconnection with the transactional, financial and marketplace enablementsystem 3300 and the platform 4000 as described above may be based on aset of contingencies 4204 that influence or represent future demand foran offering 4202, which may comprise a set of products, services, or thelike (which may include physical goods, virtual goods, software,physical services, software, access rights, entertainment content, ormany other items). A blockchain 3422, such as enabling distributedledger, may record indicators of interest from a set of parties withrespect to the product, service, or the like, such as ones that defineparameters under which the party is willing to commit to purchase theproduct or service. Interest may be expressed or committed in a demandaggregation interface 4322, which may be included in or associated withone or more sites, applications, communications systems, or the like,which may be independently operated or may comprise aspects of aplatform-operated marketplace 3327 or an external marketplace 3390.Commitments may be taken and administered via a smart contract 3431 orother transaction mechanisms. These commitments may include variousparameters 4208, such as parameters of price, technical specification(e.g., shoe size, dress size, or the like for clothing, or performancecharacteristics for information technology, such as bandwidth, storagecapacity, pixel density, or the like), timing, and many others for oneor more desired offerings 4202. The blockchain 3422 may thus be used toaggregate future demand in a forward market 4002 with respect to avariety of products and services and may be processed by manufacturers,distributors, retailers, and others to help plan for the demand, such asfor assistance (optionally in an analytics system 3419 with pricing,inventory management, supply chain management, smart manufacturing,just-in-time manufacturing, product design and many other activities).The offering 4202, whether a product, service, or other item, need notexist at the time a set of parameters 4208 are configured; for example,an individual can indicate a willingness to pay up to $1000 for a 65inch, 32K quantum dot television display on or before Jan. 1, 2022. Inembodiments, a vendor can offer a range of potential configurations andconditions with respect to which consumers can indicate interest, andoptionally commit to purchase within defined conditions. In embodiments,consumers may present desired items and configurations. In embodiments,an artificial intelligence system, which may be a rule-based system,such as enabled by an adaptive intelligent systems layer 3304, mayprocess a set of potential configurations having different parameters4208 for a subset of configurations that are consistent with each other(e.g., all have 4K or greater capability and all are priced below $500),and the subset of configurations may be used to aggregate committedfuture demand for the offering that satisfies a sufficiently largesubset at a profitable price. In embodiments, the adaptive intelligentsystems 3204 may use a fuzzy logic system, a self-organizing map, or thelike to group potential configurations, such that a human expert maydetermine a configuration that is near enough to ones that have beenidentified, such that it can be presented as a new alternative. Inembodiments, an artificial intelligence system 3448 may be trained tolearn to determine and present new configurations for offerings 4202based on a training data set created by human experts.

In embodiments, a platform 4200 is provided herein, with systems,methods, processes, services, components, and other elements forenabling a blockchain and smart contract platform for forward marketrights for accommodations. An accommodation offering 4210 may comprise acombination of products, services, and access rights that may be handledas with other offerings, including aggregation demand for the offering4210 in a forward market 4002. In embodiments, the forward marketcapabilities noted above may include access tokens 4008 foraccommodations, as well as future accommodations, such as hotel rooms,shared spaces offered by individuals (e.g., Airbnb™ spaces),bed-and-breakfasts, workspaces, conference rooms, convention spaces,fitness accommodations, health and wellness accommodations, diningaccommodations, and many others. Accommodations offerings 4210 may belinked to other access tokens 4008, such as in packages; for example, ahotel room in a city within walking distance of a sporting event may belinked by or on the same blockchain or linked blockchains (e.g., bylinking ownership or access rights to both on the same ledger), so thatwhen a condition is met (e.g., a fan's team makes it to the Super Bowl),vesting of ownership of the access token to the event also automaticallyestablishes (and optionally automatically initiates, such as via anapplication programming interface of the platform) the right to theaccommodation (such as by booking a hotel room and dining reservations).Thus, the forward market for the event may enable a convenient, secureforward market, enabled by automatic processing on the blockchain forpackages of event access tokens, accommodations, and other elements. Inembodiments, accommodations may be provided with configured forwardmarket parameters 4208 (including conditional parameters) apart fromaccess tokens 4008 to events, such as where a hotel room or otheraccommodation is booked in advance upon meeting a certain condition(such as one relating to a price within a given time window). Forexample, an accommodation offering 4210 at a four-star hotel during amusic festival could be pre-configured to be booked if and when theaccommodation (e.g., a room with a king bed and a city view) becomesavailable within a given time window. Thus, demand for accommodationscan be aggregated in advance and conveniently fulfilled by automaticrecognition (such as by monitoring systems 3306) of conditions thatsatisfy pre-configured commitments represented on a blockchain (e.g.,distributed ledger) and automatic initiation (optionally including bysmart contract execution) of settlement or fulfillment of the demand(such as by automated booking of a room or other accommodations).

In embodiments, a platform is provided herein, with systems, methods,processes, services, components, and other elements for enabling ablockchain and smart contract platform for forward market rights totransportation. As with accommodations, transportation offerings 4212may be aggregated and fulfilled, with a wide range of pre-definedcontingencies, using the platform 4200. As with accommodations offerings4210, transportation offerings 4212 can be linked to other access tokens4008 (such as event tickets, accommodations, services, and the like),such as where a flight is automatically booked at or below a predefinedprice threshold if and when the fan's team makes it to the Super Bowl,among many other examples. Transportation offerings 4212 can also beoffered separately (such as where travel is automatically booked basedon a commitment, in a distributed ledger, to buy a ticket if it isoffered within a given time window at a given price). As with othergoods and services, aggregation on the blockchain 3422, such as adistributed ledger, can be used for demand planning, for determiningwhat resources are deployed to what routes or types of travel, and thelike. Transportation offerings 4212 can be configured, with predefinedcontingencies 4204 and parameters 4208, such as with respect to price,mode of transportation (air, bus, rail, private car, ride share orother), level of service (e.g., First Class, business class, or other),mode of payment (e.g., use of loyalty programs, rewards points, orparticular currencies, including cryptocurrencies), timing (e.g.,defined time period or linked to an event, location (e.g., specified tobe where a given type of event takes place (such as this year's SuperBowl) or a specific location), route (e.g., direct or multi-stop, fromthe destination of the consumer to a specific location or to wherever anevent takes place), and many others.

In embodiments, the platform 4200 may include or interact with variousapplications, services, solutions or the like, such as those describedin connection with the platform 3300, such as pricing applications 3421(such as for setting and monitoring pricing for goods, services, accessrights, tokens, fees and other items), analytics solutions 3419 (such asfor monitoring, reporting, predicting, and otherwise analyzing allaspects of the platform 4000, such as to optimize offerings, timing,pricing, or the like, to recognize and predict patterns, to establishrules and contingencies, to establish models or understanding for use byhumans or by machine learning system, and for many other purposes),trading applications 3428 (such as for trading or exchanging contingentaccess rights, futures or options for goods, services, or otherofferings 4202, tokens and other items), security applications 3418, orthe like.

Referring to FIG. 43 , a platform-operated marketplace 3327 for aforward market to future offerings 4202 may be configured, such as in adashboard 4318 or other user interface for an operator of theplatform-operated marketplace 3327, using the various enablingcapabilities of the data handling platform 3300 described throughoutthis disclosure. The operator may use the user interface or dashboard4318 to undertake a series of steps to perform or undertake an algorithmto create an offering 4210 as described in connection with FIG. 42 . Inembodiments, one or more of the steps of the algorithm to create acontingent future offering 4210 within the dashboard 4318 may include,at a component 4302, identifying offering data 4320, which may come froma platform-operated marketplace 3327 or an external marketplace 3390,such as via a demand aggregation interface 4322 presented to one or moreconsumers within one of them, or may be entered via a user interface ofor at a site or application that is created for demand aggregation forofferings 4210, such as via solicitation of consumer interest orconsumer commitments (such as commitments entered into by smartcontracts) based on specification of various possible parameters 4208and contingencies 4204 for such offerings 4210.

The dashboard 4318 may be configured with interface elements (includingapplication programming elements) that allow an offering to be managedin the platform-operated marketplace 3327, such as by linking to the setof environments where various components of the offering 4202, such asdescriptions of goods and services, prices, access rights and the likeare specified, offered or maintained, which may include using APIs forbackend ticketing systems, e-commerce systems, ordering systems,fulfillment systems, and the like. In the dashboard 4318, a component4304 may configure one or more parameters 4208 or contingencies 4204(e.g., via interactions with a user), such as comprising or describingthe conditions (of the type described herein) for the offering, such asby defining a set of conditions that trigger the commitment by aconsumer to partake of the offering 4202, that trigger the right to anallocation of the offering, or the like. The user interface of thedashboard 4318 may include a set of drop down menus, tables, forms, orthe like with default, templated, recommended, or pre-configuredconditions, parameters 4208, contingencies 4204 and the like, such asones that are appropriate for various types of offerings 4202. Forexample, access rights to a new line of shoes can be preconfigured toset an offering condition as the offering of a shoe by a certaindesigner of a certain style and color and may be preconfigured to accepta commitment to buy the shoe if the access is provided below a certainprice during a certain time period. As another example, demand for anas-yet-unplanned entertainment event can be preconfigured to setconditions such as a venue, a span of dates and a selected entertaineror group. Once the conditions and other parameters of the offering 4202are configured, a component 4308 may configure a blockchain to maintain,such as via a ledger, the data required to provision, allocate, andexchange ownership of items comprising the offering (and optionallyunderlying access tokens, virtual goods, digital content items, or thelike that are included in or associated with the offering). For example,a virtual good for a video may be stored as a cryptographically securetoken on the ledger, and another token may be created and stored on theblockchain for each contingent access right that could result in theownership of the virtual good or each smart contract to purchase thevirtual good if and when it becomes available under defined conditions.The blockchain may be configured to store tokens, identity information,transaction information (such as for exchanges of contingent rightsand/or underlying tokens), virtual goods, license keys, digital content,entertainment content, and other data. A component 4310 may configure asmart contract 3431 to embody the conditions that were configured at thecomponent 4304 and to operate on the blockchain that was created at thecomponent 4308 as well as to operate on other data, such as dataindicating facts, conditions, events, or the like in theplatform-operated marketplace 3327 and/or an external marketplace 3390.The smart contract may be configured at the step 4310 to apply one ormore rules, execute one or more conditional operations, or the like upondata that may include offering data 4320, event data 3324, access data3362, pricing data 3364 or other data about or relevant to a set ofofferings 4202. Once configuration of one or more blockchains and one ormore smart contracts is complete, at a component 4312 the blockchain andsmart contract may be deployed in the platform-operated marketplace3327, such as for interaction by one or more consumers or other users,who may, such as in a marketplace interface or a demand aggregationinterface 4322, such as a website, application, or the like, enter intothe smart contract, such as by executing an indication of a commitmentto purchase, attend, or otherwise consume the future offering 4202, atwhich point the platform, such as using the adaptive intelligent systemslayer 3304 or other capabilities, may store relevant data, such aspricing data and identity data for the party or parties entering thesmart contract on the blockchain or otherwise on the platform 3300. At acomponent 4314, once the smart contract is executed, the platform maymonitor, such as by the monitoring systems layer 3306, theplatform-operated marketplace 3327 and/or one or more externalmarketplaces 3390 for offering data 4320, event data 3324, access data3362, pricing data 3364 or other data, such as events, that may satisfyone or more conditions or trigger application of one or more rules ofthe smart contract. For example, announcements of offerings may bemonitored, such as on e-commerce sites, auction sites, or the like, andsmart contract conditions may be satisfied by one or more of theofferings 4202.

At a component 4316, upon satisfaction of conditions, smart contractsmay be settled, executed, or the like, resulting updates or otheroperations on the blockchain, such as by transferring ownership ofgoods, services, underlying access tokens and/or contingent accesstokens and transferring required consideration (such as obtained by apayments system). Thus, via the above-referenced steps, an operator ofthe platform-operated marketplace 3327 may discover, configure, deploy,and have executed a set of smart contracts that aggregate demand for,and offer and deliver contingent access to, offerings 4202 that arecryptographically secured and transferred on a blockchain to consumersor others. In embodiments, the adaptive intelligent systems layer 3304may be used to monitor the steps of the algorithm described above, andone or more artificial intelligence systems may be used to automated,such as by robotic process automation, the entire process or one or moresub-steps or sub-algorithms. This may occur as described above, such asby having an artificial intelligence system learn on a training set ofdata resulting from observations, such as monitoring softwareinteractions, of human users as they undertake the above-referencedsteps. Once trained, the adaptive intelligent systems layer 3304 maythus enable the platform 3300 to provide a fully automated platform fordiscovery and delivery of offerings, as well as demand aggregation forsuch offerings 4202 and automated handling of access to and ownership ofsuch offerings 4202.

Referring to FIG. 44 , in embodiments, a platform is provided herein,with systems, methods, processes, services, components and otherelements for enabling a blockchain and smart contract platform 4400 forcrowdsourcing for innovation. In such embodiments, a party seeking a setof innovations 4402, such as inventions, works of authorship,innovations, technology solutions to a set of problems, satisfaction ofa technical specification, or other advancement may configure, such ason a blockchain 3422 (optionally comprising a distributed ledger), a setof conditions 4410, capable of being expressed in a smart contract 3431,that are required to satisfy the requirement. A reward 4412 may beconfigured for generating an innovation 4402 of a given set ofcapabilities or satisfying a given set of parameters 4408 by a givendate (e.g., a technical specification for a 5G foldable phone that canbe produced for less than $100 per unit before the end of 2019).Satisfaction of the conditions 4410 may be measured by a monitoringsystem 3306, by one or more experts, or by a trained artificialintelligence system 3448 (such as one trained to evaluate responsesbased on a training set created by experts). In embodiments, theblockchain and smart contract platform 4400 may include a dashboard 4414for configuration of the specification, requirements or other conditions4410, the reward 4412, timing and other parameters 4408 (such as anyrequired qualifications, formats, geographical requirements,certifications, credentials, or the like that may be required of asubmission or a submitter), and the blockchain and smart contractplatform 4400 may automatically configure a blockchain 3422 to store theparameters 4408 and a smart contract 3431 to operate, such as incoordination with a website, application, or other marketplaceenvironments, to offer the reward 4412, receive and record submissions4418 (such as on the blockchain 3422), allocate rewards 4412, and thelike, with events, transactions, and activities being recorded inblockchain, optionally using a distributed ledger. In embodiments,rewards 4412 may be configured to be allocated across multiplesubmissions, such as where an innovation requires solution of multipleproblems, such that submissions 4418 may be evaluated for satisfactionof some conditions and rewards may be allocated among contributingsubmissions 4418 when and if a complete solution (comprising aggregationof multiple submissions 4418) is achieved, unlocking the reward, atwhich point the contributing submissions 4418 recorded on thedistributed ledger may be allocated appropriate portions of the reward.Submissions may include software, technical data, know how, algorithms,firmware, hardware, mechanical drawings, prototypes, proof-of-conceptdevices, systems, and many other forms, which may be identified,described, or otherwise documented on the blockchain 3422 (e.g.,distributed ledger), such as by one or more links to one or moreresources (which may be secured by cryptographic or other techniques).Submissions may thus be described and evaluated for purposes ofallocation of rewards 4412 (such as by one or more independent experts,by artificial intelligence systems (which may be trained by experts) orthe like), then locked, such as by encryption, secure storage, or thelike, unless and until a reward is distributed via the distributedledger. Thus, the platform provides a secure system for exchange ofinformation related to innovation that is provided for rewards, such asin crowdsourcing or other innovation programs. An artificialintelligence system 3448 may be trained, such as by a training set ofdata using interactions of experts with submissions 4418, toautomatically evaluate submissions 4418, for either automatic allocationof rewards or to pre-populate evaluation for confirmation by humanexperts. In embodiments, an artificial intelligence system 3448 may betrained, such as by a training set of data reflecting expertinteractions with the dashboard 4414, optionally coupled with outcomeinformation, such as from analytics system 3419, to create rewards 4412,set conditions 4410, specify innovations 4402, and set other parameters4408, thereby providing a fully automated or semi-automated capabilityfor one or more of those capabilities.

Referring to FIG. 45 , a platform-operated marketplace 3327 forcrowdsourcing innovation 4402 may be configured, such as in acrowdsourcing dashboard 4414 or other user interface for an operator ofthe platform-operated marketplace 3327, using the various enablingcapabilities of the data handling platform 3300 described throughoutthis disclosure. The operator may use the user interface orcrowdsourcing dashboard 4414 to undertake a series of steps to performor undertake an algorithm to create crowdsourcing offers as described inconnection with FIG. 44 . In embodiments, one or more of the componentsdepicted are configured to create a reward 4412 within the dashboard4414 which may include, at a component 4502, identifying potentialoffers, such as what innovations 4402 are of interest (such as may beindicated by indications of demand in a platform-operated marketplace3327 or an external marketplace 3390, or by indications by stakeholdersfor an enterprise through various communication channels).

The dashboard 4414 may be configured with a crowdsourcing interface4512, such as with elements (including application programming elements)that allow a crowdsourcing offering to be managed in theplatform-operated marketplace 3327 and/or in one or more externalmarketplaces 3390. In the dashboard 4414, at a component 4504, the usermay configure one or more parameters 4408 or conditions 4410, such ascomprising or describing the conditions (of the type described herein)for the crowdsourcing offer, such as by defining a set of conditions4410 that trigger the reward 4412 and determine allocation of the reward4412 to a set of submitters. The user interface of the dashboard 4414may include a set of drop-down menus, tables, forms, or the like withdefault, templated, recommended, or pre-configured conditions,parameters 4408, conditions 4410 and the like, such as ones that areappropriate for various types of crowdsourcing offers. Once theconditions and other parameters of the offer are configured, at acomponent 4508, a smart contract 3431 and blockchain 3422 may beconfigured to maintain, such as via a ledger, the data required toprovision, allocate, and exchange data related to the offer. Theblockchain may be configured to store tokens, identity information,transaction information (such as for exchanges of information),technical descriptions, virtual goods, license keys, digital content,entertainment content, and other data, content or information that maybe relevant to a submission 4418 or a reward 4412. At a component 4510,a smart contract 3431 may be configured to embody the conditions thatwere configured at the step 4504 and to operate on the blockchain thatwas created at the component 4508 as well as to operate on other data,such as data indicating facts, conditions, events, or the like in theplatform-operated marketplace 3327 and/or an external marketplace 3390,such as ones related to submission data 4418. The smart contract 3431may be responsive to the component 4510 to apply one or more rules,execute one or more conditional operations or the like upon data, suchas submission data 4418 and data indicating satisfaction of parametersor conditions, as well as identity data, transactional data, timingdata, and other data. Once configuration of one or more blockchains andone or more smart contracts is complete, at a component 4512, theblockchain and smart contract may be deployed in the platform-operatedmarketplace 3327, external marketplace 3390 or other environment, suchas for interaction by one or more submitters or other users, who may,such as in a crowdsourcing interface 4512, such as a website,application, or the like, enter into the smart contract, such as bysubmitting a submission 4418 and requesting the reward 4412, at whichpoint the platform, such as using the adaptive intelligent systems layer3304 or other capabilities, may store relevant data, such as submissiondata 4418, identity data for the party or parties entering the smartcontract on the blockchain, or otherwise on the platform 3300. At acomponent 4514, once the smart contract is executed, the platform maymonitor, such as by the monitoring systems layer 3306, theplatform-operated marketplace 3327 and/or one or more externalmarketplaces 3390 for submission data 4418, event data 3324, or otherdata that may satisfy or indicate satisfaction of one or more conditions4410 or trigger application of one or more rules of the smart contract3431, such as to trigger a reward 4412.

At a component 4516, upon satisfaction of conditions, smart contractsmay be settled, executed, or the like, resulting in updates or otheroperations on the blockchain 3422, such as by transferring consideration(such as via a payments system) and transferring access to submissions4418. Thus, via the above-referenced steps, an operator of theplatform-operated marketplace 3327 may discover, configure, deploy, andhave executed a set of smart contracts that crowdsource innovations thatare cryptographically secured and transferred on a blockchain frominnovators to parties seeking innovation. In embodiments, the adaptiveintelligent systems layer 3304 may be used to monitor the steps of thealgorithm described above, and one or more artificial intelligencesystems may be used to automate, such as by robotic process automation,the entire process or one or more sub-steps or sub-algorithms. This mayoccur as described above, such as by having an artificial intelligencesystem learn on a training set of data resulting from observations, suchas monitoring software interactions of human users as they undertake theabove-referenced steps. Once trained, the adaptive intelligent systemslayer 3304 may thus enable the platform 3300 to provide a fullyautomated platform for crowdsourcing of innovation.

Referring to FIG. 46 , in embodiments, a platform is provided herein,with systems, methods, processes, services, components and otherelements for enabling a blockchain and smart contract platform 4600 forcrowdsourcing for evidence. As with other embodiments described above inconnection with sourcing innovation, product demand, or the like, ablockchain 3422, such as optionally embodying a distributed ledger, maybe configured with a set of smart contracts 3431 to administer a reward4612 for the submission of evidence 4618, such as evidence ofinfringement, evidence of prior art, evidence of publication, evidenceof use, evidence of commercial sales, evidence of fraud, evidence offalse statements, evidence of trespassing, evidence of negligence,evidence of misrepresentation, evidence of slander or libel, evidence ofundertaking illegal activities, evidence of undertaking riskyactivities, evidence of omissions, evidence of breach of contract,evidence of torts, evidence of criminal conduct, evidence of regulatoryviolations, evidence of non-compliance with policies or procedures,evidence of the location of an individual (optionally including known orpreferred locations), evidence of a social network or other relationshipof an individual, evidence of a business connection of an individual orbusiness, evidence of an asset of an individual or business, evidence ofdefects, evidence of harm, evidence of counterfeiting, evidence ofidentity (such as DNA, fingerprinting, video, photography or the like),evidence of damage, evidence of confusion (such as in cases of trademarkinfringement) or other evidence that may be relevant to a civil orcriminal legal proceeding, a contract enforcement or negotiation, anarbitration or mediation, a hearing, or other proceeding. Inembodiments, a blockchain 3422, such as optionally distributed in adistributed ledger, may be used to configure a request for evidence 4618(which may be a formal legal request, such as a subpoena, or analternative form of request, such as in a fact-gathering situation),along with terms and conditions 4610 related to the evidence, such as areward 4612 for submission of the evidence 4618, a set of terms andconditions 4610 related to the use of the evidence 4618 (such as whetherit may only be released under subpoena, whether the submitting party hasa right to anonymity, the nature of proceedings in which the evidencecan be used, the permitted conditions for use of the evidence 4618, andthe like), and various parameters 4608, such as timing parameters, thenature of the evidence required (such as scientifically validatedevidence like DNA or fingerprints, video footage, photographs, witnesstestimony, or the like), and other parameters 4608.

The platform 4600 may include a crowdsourcing interface 4620, which maybe included in or provided in coordination with a website, application,dashboard, communications system (such as for sending emails, texts,voice messages, advertisements, broadcast messages, or other messages),by which a message may be presented in the interface 4620 or sent torelevant individuals (whether targeted, such as in the case of asubpoena, or broadcast, such as to individuals in a given location,company, organization, or the like) with an appropriate link to thesmart contract 3431 and associated blockchain 3422, such that a replymessage submitting evidence 4618, with relevant attachments, links, orother information, can be automatically associated (such as via an APIor data integration system) with the blockchain 3422, such that theblockchain 3422, and any optionally associated distributed ledger,maintains a secure, definitive record of evidence 4618 submitted inresponse to the request. Where a reward 4612 is offered, the blockchain3422 and/or smart contract 3431 may be used to record time ofsubmission, the nature of the submission, and the party submitting, suchthat at such time as a submission satisfies the conditions for a reward4612 (such as, for example, upon apprehension of a subject in a criminalcase or invalidation of a patent upon use of submitted prior art, amongmany other examples), the blockchain 3422 and any distributed ledgerstored thereby can be used to identify the submitter and, by executionof the smart contract 3431, convey the reward 4612 (which may take anyof the forms of consideration noted throughout this disclosure). Inembodiments, the blockchain 3422 and any associated ledger may includeidentifying information for submissions of evidence 4618 withoutcontaining actual evidence 4618, such that information may be maintainedsecret (such as being encrypted or being stored separately with onlyidentifying information), subject to satisfying or verifying conditionsfor access (such as a legal subpoena, a warrant, or other identificationor verification of a person who has legitimate access rights, such as byan identity or security application 3418). Rewards 4612 may be providedbased on outcomes of cases or situations to which evidence 4618 relates,based on a set of rules (which may be automatically applied in somecases, such as using a smart contract 3431 in concert with an automationsystem, a rule processing system, an artificial intelligence system 3448or other expert system, which in embodiments may comprise one that istrained on a training data set created with human experts). For example,a machine vision system may be used to evaluate evidence ofcounterfeiting based on images of items, and parties submitting evidenceof counterfeiting may be rewarded, such as via tokens or otherconsideration, via distribution of rewards 4612 through the smartcontract 3431, blockchain 3422 and any distributed ledger. Thus, theplatform 4600 may be used for a wide variety of fact-gathering andevidence-gathering purposes, to facilitate compliance, to deter improperbehavior, to reduce uncertainty, to reduce asymmetries of information,or the like.

Referring to FIG. 47 , a platform-operated marketplace crowdsourcingevidence 4600 may be configured, such as in a crowdsourcing interface4620 or other user interface for an operator of the platform-operatedmarketplace 4600, using the various enabling capabilities of the datahandling platform 3300 described throughout this disclosure. Theoperator may use the user interface 4620 or crowdsourcing dashboard 4614to undertake a series of steps to perform or undertake an algorithm tocreate a crowdsourcing request for evidence 4618 as described inconnection with FIG. 46 . In embodiments, one or more interactions withthe components to create a reward 4612 within the dashboard 4614 mayinclude, at a component 4702, identifying potential rewards 4612, suchas what evidence 4618 is likely to be of value in a given situation(such as may be indicated through various communication channels bystakeholders or representatives of an entity, such as an individual orenterprise, such as attorneys, agents, investigators, parties, auditors,detectives, underwriters, inspectors, and many others).

The dashboard 4614 may be configured with a crowdsourcing interface4620, such as with elements (including application programming elements,data integration elements, messaging elements, and the like) that allowa crowdsourcing request to be managed in the platform marketplace 4600and/or in one or more external marketplaces 3390. In the dashboard 4614,at a component 4704, the user may configure one or more parameters 4608or conditions 4610, such as comprising or describing the conditions (ofthe type described herein) for the crowdsourcing request, such as bydefining a set of conditions 4610 that trigger the reward 4612 anddetermine allocation of the reward 4612 to a set of submitters ofevidence 4618. The user interface of the dashboard 4614, which mayinclude or be associated with the crowdsourcing interface 4620, mayinclude a set of drop down menus, tables, forms, or the like withdefault, templated, recommended, or pre-configured conditions,parameters 4608, conditions 4610 and the like, such as ones that areappropriate for various types of crowdsourcing requests. Once theconditions and other parameters of the request are configured, at acomponent 4708, a smart contract 3431 and blockchain 3422 may beconfigured to maintain, such as via a ledger, the data required toprovision, allocate, and exchange data related to the request and tosubmissions of evidence 4618. The smart contract 3431 and blockchain3422 may be configured to identity information, transaction information(such as for exchanges of information), technical information, otherevidence data 4618 of the type described in connection with FIG. 46 ,including any data, testimony, photo or video content or otherinformation that may be relevant to a submission of evidence 4618, orthe conditions 4610 for a reward 4612. At a component 4710, a smartcontract 3431 may be configured to embody the conditions 4610 that wereconfigured at the component 4704 and to operate on the blockchain 3422that was created at the component 4708, as well as to operate on otherdata, such as data indicating facts, conditions, events, or the like inthe platform-operated marketplace 4600 and/or an external marketplace3390 or other information site or resource, such as ones related tosubmission of evidence data 4618, such as sites indicating outcomes oflegal cases or portions of cases, sites reporting on investigations, andthe like. The smart contract 3431 may be responsive to apply one or morerules configured at component 4710, to execute one or more conditionaloperations or the like upon data, such as evidence data 4618 and dataindicating satisfaction of parameters 4608 or conditions 4610, as wellas identity data, transactional data, timing data, and other data. Onceconfiguration of one or more blockchains 3422 and one or more smartcontracts 3431 is complete, at a component 4712, the blockchain 3422 andsmart contract 3431 may be deployed in the platform-operated marketplace4600, external marketplace 3390 or other site or environment, such asfor interaction by one or more submitters or other users, who may, suchas in a crowdsourcing interface 4620, such as a website, application, orthe like, enter into the smart contract 3431, such as by submitting asubmission of evidence 4618 and requesting the reward 4612, at whichpoint the platform 4600, such as using the adaptive intelligent systemslayer 3304 or other capabilities, may store relevant data, such assubmitted evidence data 4618, identity data for the party or partiesentering the smart contract 3431 on the blockchain 3422, or otherwise onthe platform 4600. At a component 4714, once the smart contract 3431 isexecuted, the platform 4600 may monitor, such as by the monitoringsystems layer 3306, the platform-operated marketplace 4600 and/or one ormore external marketplaces 3390 or other sites for submitted evidencedata 4618, event data 3324, or other data that may satisfy or indicatesatisfaction of one or more conditions 4610 or trigger application ofone or more rules of the smart contract 3431, such as to trigger areward 4612.

At a component 4716, upon satisfaction of conditions 4610, smartcontracts 3431 may be settled, executed, or the like, resulting inupdates or other operations on the blockchain 3422, such as bytransferring consideration (such as via a payment system) andtransferring access to evidence 4618. Thus, via the above-referencedsteps, an operator of the platform-operated marketplace 4600 maydiscover, configure, deploy, and have executed a set of smart contracts3431 that crowdsource evidence and that are cryptographically securedand transferred on a blockchain 3422 from evidence gatherers to partiesseeking evidence. In embodiments, the adaptive intelligent systems layer3304 may be used to monitor the steps of the algorithm described above,and one or more artificial intelligence systems may be used to automate,such as by robotic process automation 3442, the entire process or one ormore sub-steps or sub-algorithms. This may occur as described above,such as by having an artificial intelligence system 3448 learn on atraining set of data resulting from observations, such as monitoringsoftware interactions of human users as they undertake theabove-referenced steps. Once trained, the adaptive intelligent systemslayer 3304 may thus enable the platform 3300 to provide a fullyautomated platform for crowdsourcing of evidence.

In embodiments, evidence may relate to fact-gathering or data-gatheringfor a variety of applications and solutions that may be supported by amarketplace platform 3300, including the evidence crowdsourcing platform4600, such as for underwriting 3420 (e.g., of insurance policies, loans,warranties, guarantees, and other items), including actuarial processes;risk management solutions 3408 (such as managing a wide variety of risksnoted throughout this disclosure); tax solutions (such as relating toevidence supporting deductions and tax credits, among others); lendingsolutions 3410 (such as evidence of the ownership and or value ofcollateral, evidence of the veracity of representations, and the like);regulatory solutions 3426 (such as with respect to compliance with awide range of regulations that may govern entities 3330 and processes,behaviors or activities of or by entities 3330); and fraud preventionsolutions 3416 (such as to detect fraud, misrepresentation, improperbehavior, libel, slander, and the like).

Evidence gathering may include evidence gathering with respect toentities 3330 and their identities, assertions, claims, actions, orbehaviors, among many other factors and may be accomplished bycrowdsourcing in the crowdsourcing platform 4600 or by data collectionsystems 3318 and monitoring systems 3306, optionally with automation viaprocess automation 3442 and adaptive intelligence, such as using anartificial intelligence system 3448.

In embodiments, the evidence gathering platform, whether a crowdsourcingplatform 4600 or a more general data collection platform 3300 that mayor may not encompass crowdsourcing, is provided herein, with systems,methods, processes, services, components, and other elements forenabling a blockchain and smart contract platform for aggregatingidentity and behavior information for insurance underwriting 3420. Inembodiments, a blockchain, with an optional distributed ledger, may beused to record a set of events, transactions, activities, identities,facts, and other information associated with an underwriting process3420, such as identities of applicants for insurance, identities ofparties that may be willing to offer insurance, information regardingrisks that may be insured (of any type, such as property, life, travel,infringement, health, home, commercial liability, product liability,auto, fire, flood, casualty, retirement, unemployment and many otherstraditionally insured by insurance policies, in addition to a host ofother types of risks that are not traditionally insured), informationregarding coverage, exclusions, and the like, information regardingterms and conditions, such as pricing, deductible amounts, interestrates (such as for whole life insurance) and other information. Theblockchain 3422 and an associated smart contract 3431 may, incoordination with or via a website, application, communications system,message system, marketplace, or the like, be used to offer insurance andto record information submitted by applicants, so that an insuranceapplication has a secure, canonical record of submitted information,with access control capabilities that permit only authorized parties,roles and services to access submitted information (such as governed bypolicies, regulations, and terms and conditions of access). Theblockchain 3422 may be used in underwriting 3420, such as by recordinginformation (including evidence as noted in connection with evidencegathering above) that is relevant to pricing, underwriting, coverage,and the like, such as collected by underwriters, submitted byapplicants, collected by artificial intelligence systems 3448, orsubmitted by others (such as in the case of crowdsourcing platform4600). In embodiments, the blockchain 3422, smart contract 3431 and anydistributed ledger may be used to facilitate offering and underwritingof microinsurance, such as for defined risks related to definedactivities for defined time periods that are narrower than for typicalinsurance policies. For example, insurance related to adverse weatherevents may be obtained for the day of a wedding. The blockchain 3422 mayfacilitate allocation of risk and coordination of underwritingactivities for a group of parties, such as where a group of partiesagree to take some fraction of the risk, as recorded in the ledger. Forexample, the ledger may allow a party to take any fraction of the risk,thereby accumulating partial insurance unless and until a risk is fullycovered as the rest of accumulation and aggregation of multiple partiesagreeing, as recorded on the ledger, to insure an activity, a risk, orthe like. The ledger may be used to allocate payments upon occurrence ofthe covered risk event. In embodiments, an artificial intelligencesystem 3448 may be used to collect and analyze underwriting data, suchas one that is trained by human expert underwriters. In embodiments, anautomated system 3442, such one using artificial intelligence 3448, suchas one trained to recognized and validate events, can be used todetermine that an event has happened (e.g., a roof has collapsed, a carhas been damage, or the like), such as from videos, images, sensors, IoTdevices, witness submissions (such as over social networks), or thelike, such that an operation on the distributed ledger may be initiatedto pay out the insured amount, including initiating appropriate debitsand credits that reflect transfer of funds from theunderwriting/insuring parties to the insured. Thus, a blockchain-basedledger may simplify and automate much of the insurance process byreliably validating identities, maintaining confidentiality ofinformation as needed, automatically accumulating evidence needed forpricing and underwriting, automatically processing informationindicating occurrence of insured events, and automatically settling andfulfilling contracts upon occurrence of validated events.

Lending Platform

Referring to FIG. 48 , an embodiment of a financial, transactional andmarketplace enablement system 3300 is illustrated wherein a lendingenablement system 4800 is enabled and wherein a platform-orientedmarketplace 3327 may comprise a lending platform 3410. The lendingenablement system 4800 may include a set of systems, applications,processes, modules, services, layers, devices, components, machines,products, sub-systems, interfaces, connections, and other elements(collectively referred in the alternative, except where contextindicates otherwise, as the “platform,” the “lending platform,” the“system,” and the like) working in coordination (such as by dataintegration and organization in a services oriented architecture) toenable intelligent management of a set of entities 3330 that may occur,operate, transact or the like within, or own, operate, support orenable, one or more applications, services, solutions, programs or thelike of the lending platform 3410 or external marketplaces 3390 thatinvolve lending transactions or lending-related entities, or that mayotherwise be part of, integrated with, linked to, or operated on by theplatform 3300 and system 4800. References to a set of services hereinshould be understood, except where context indicates otherwise, theseand other various systems, applications, processes, modules, services,layers, devices, components, machines, products, sub-systems,interfaces, connections, and other types of elements. A set may includemultiple members or a single member. As with other embodiments of thesystem 3300, the system 4800 may have various data handling layers withcomponents, modules, systems, services, components, functions, and otherelements described in connection with other embodiments describedthroughout this disclosure and the documents incorporated herein byreference. This may include various adaptive intelligent systems layer3304, monitoring systems 3306, data collection systems 3318, and datastorage systems 3310, as well as a set of application programminginterfaces 3316 of, to, and/or among each of those systems and/or thevarious other elements of the platform 3300 and system 4800. Inembodiments, the application programming interfaces 3316 may includeapplication programming interfaces 4812; data integration technologiesfor extracting, transforming, cleansing, normalizing, deduplicating,loading and the like as data is moved among various services usingvarious protocols and formats (collectively referred to as ETL systems4814); and various ports, portals, connectors, gateways, wiredconnections, sockets, virtual private networks, containers, securechannels and other connections configured among elements on aone-to-one, one-to-many, or many-to-one basis, such as in unicast,broadcast and multi-cast transmission (collectively referred to as ports4818). Application programming interfaces 3316 may include, be enabledby, integrate with, or interface with a real time operating system(RTOS) 4810, such as the FreeRTOS™ operating system, that has adeterministic execution pattern in which a user may define an executionpattern, such as based on assignment of a priority to each thread ofexecution. An instance of the RTOS 4810 may be embedded, such as on amicrocontroller of an Internet of Things device, such as one used tomonitor various entities 3330. The RTOS 4810 may provide real-timescheduling (such as scheduling of data transmissions to monitoringsystem layers 3306 and data collection systems 3318, scheduling ofinter-task communication among various service elements, and othertiming and synchronization elements). In embodiments, the applicationprogramming interfaces 3316 may use or include a set of libraries thatenable secure connection between small, low-power edge devices, such asInternet of Things devices used to monitor entities 3330, and variouscloud-deployed services of the platform 3300 and system 4800, as well asa set of edge devices and the systems that enable them, such as onesrunning local data processing and computing systems such as AWS IoTGreengrass™ and/or AWS Lambda™ functions, such as to allow localcalculation, configuration of data communication, execution of machinelearning models (such as for prediction or classification),synchronization of devices or device data, and communication amongdevices and services. This may include use of local device resourcessuch as serial ports, GPUs, sensors, and cameras. In embodiments, datamay be encrypted for secure end-to-end communication.

In the context of a lending enablement system 4800 and set of lendingsolutions 3410, entities 3330 may include any of the wide variety ofassets, systems, devices, machines, facilities, individuals or otherentities mentioned throughout this disclosure or in the documentsincorporated herein by reference, such as, without limitation: machines3352 and their components (e.g., machines that are the subject of a loanor collateral for a loan, such as various vehicles and equipment, aswell as machines used to conduct lending transactions, such as automatedteller machines, point of sale machines, vending machines, kiosks,smart-card-enabled machines, and many others, including ones used toenable microloans, payday loans and others); financial and transactionalprocesses 3350 (such as lending processes, inspection processes,collateral tracking processes, valuation processes, credit checkingprocesses, creditworthiness processes, syndication processes, interestrate-setting processes, software processes (including applications,programs, services, and others), production processes, collectionprocesses, banking processes (e.g., lending processes, underwritingprocesses, investing processes, and many others), financial serviceprocesses, diagnostic processes, security processes, safety processes,assessment processes, payment processes, valuation processes, issuanceprocesses, factoring processes, consolidation processes, syndicationprocesses, collection processes, foreclosure processes, title transferprocesses, title verification processes, collateral monitoringprocesses, and many others); wearable and portable devices 3348 (such asmobile phones, tablets, dedicated portable devices for financialapplications, data collectors (including mobile data collectors),sensor-based devices, watches, glasses, hearables, head-worn devices,clothing-integrated devices, arm bands, bracelets, neck-worn devices,AR/VR devices, headphones, and many others); workers 3344 (such asbanking workers, loan officers, financial service personnel, managers,inspectors, brokers (e.g., mortgage brokers), attorneys, underwriters,regulators, assessors, appraisers, process supervisors, securitypersonnel, safety personnel and many others); robotic systems 3342(e.g., physical robots, collaborative robots (e.g., “cobots”), softwarebots and others); and facilities (such as inventory warehousingfacilities, factories, homes, buildings, storage facilities (such as forloan-related collateral, property that is the subject of a loan,inventory (such as related to loans on inventory), personal property,components, packaging materials, goods, products, machinery, equipment,and other items), banking facilities (such as for commercial banking,investing, consumer banking, lending and many other banking activities)and others). In embodiments, entities 3330 may include externalmarketplaces 3390, such as financial, commodities, e-commerce,advertising, and other marketplaces 3390 (including current and futuremarkets), such as ones within which transactions occur in various goodsand services, such that monitoring of the marketplaces 3390 and entities3330 within them may provide lending-relevant information, such as withrespect to the price or value of items, the liquidity of items, thecharacteristics of items, the rate of depreciation of items, or thelike. For example, for various entities that may comprise collateral4802 or assets for asset-backed lending, a monitoring system layer(s)3306 may monitor not only the collateral 4802 or assets, such as bycameras, sensors, or other monitoring systems 3306, but may also collectdata, such as via data collection systems 3318 of various types, withrespect to the value, price, or other condition of the collateral 4802or assets, such as by determining market conditions for collateral 4802or assets that are in similar condition, of similar age, having similarspecifications, having similar location, or the like. In embodiments, anadaptive intelligent systems layer 3304 may include a clustering system4804, such as one that groups or clusters entities 3330, includingcollateral 4802, parties, assets, or the like by similarity ofattributes, such as a k-means clustering system, self-organizing mapsystem, or other system as described herein and in the documentsincorporated herein by reference. The clustering system may organizecollections of collateral, collections of assets, collections ofparties, and collections of loans, for example, such that they may bemonitored and analyzed based on common attributes, such as to enableperformance of a subset of transactions to be used to predictperformance of others, which in turn may be used for underwriting 3420,pricing 3421, fraud detection 3416, or other applications, including anyof the services, solutions, or applications described in connection withFIG. 48 and FIG. 49 or elsewhere throughout this disclosure or thedocuments incorporated herein by reference. In embodiments, conditioninformation about collateral 4802 or assets is continuously monitored bya monitoring system 3306, such as a set of sensors on the collateral4802 or assets, a set of sensors or cameras in the environment of thecollateral 4802 or assets, or the like, and market information iscollected in real time by a data collection system 3318, such that thecondition and market information may be time-aligned and used as a basisfor real time estimation of the value of the collateral or assets andforward prediction of the future value of the collateral or assets.Present and predicted value for the collateral 4802 or assets may bebased on a model, which may be accessed and used, such as in a smartcontract 3431, to enable automated, or machine-assisted lending on thecollateral or assets, such as the underwriting or offering of amicroloan on the collateral 4802 or assets. Aggregation of data for aset of collateral 4802 or set of assets, such as a collection or fleetof collateral 4802 or fleet of assets owned by an entity 3330 may allowreal time portfolio valuation and larger scale lending, including viasmart contracts 3431 that automatically adjust interest rates and otherterms and conditions based on the individual or aggregated value ofcollateral 4802 or assets based on real time condition monitoring andreal-time market data collection and integration. Transactions, partyinformation, transfers of title, changes in terms and conditions, andother information may be stored in a blockchain 3422, including loantransactions and information (such as condition information forcollateral 4802 or assets and marketplace data) about the collateral4802 or assets. The smart contract 3431 may be configured to require aparty to confirm condition information and/or market value information,such as by representations and warranties that are supported or verifiedby the monitoring system layers 3306 (which may flag fraud in a frauddetection system 3416). A lending model 4808 may be used to valuecollateral 4802 or assets, to determine eligibility for lending based onthe condition and/or value of collateral 4802 or assets, to set pricing(e.g., interest rates), to adjust terms and conditions, and the like.The lending model 4808 may be created by a set of experts, such as usinganalytics solutions 3419 on past lending transactions. The lending model4808 may be populated by data from monitoring system layers 3306 anddata collection systems 3318, may pull data from storage systems 3310,and the like. The lending model 4808 may be used to configure parametersof a smart contract 3431, such that smart contract terms and conditionsautomatically adjust based on adjustments in the lending model 4808. Thelending model 4808 may be configured to be improved by artificialintelligence 3448, such as by training it on a set of outcomes, such asoutcomes from lending transactions (e.g., payment outcomes, defaultoutcomes, performance outcomes, and the like), outcomes on collateral4802 or assets (such as prices or value patterns of collateral or assetsover time), outcomes on entities (such as defaults, foreclosures,performance results, on time payments, late payments, bankruptcies, andthe like), and others. Training may be used to adjust and improve modelparameters and performance, including for classification of collateralor assets (such as automatic classification of type and/or condition,such as using vision-based classification from camera-based monitoringsystems 3306), prediction of value of collateral 4802 or assets,prediction of defaults, prediction of performance, and the like. Inembodiments, configuration or handling of smart contracts 3431 forlending on collateral 4802 or assets may be learned and automated in arobotic process automation (RPA) system 3442, such as by training theRPA system 3442 to create smart contracts 3431, configure parameters ofsmart contracts 3431, confirm title to collateral 4802 or assets, setterms and conditions of smart contracts 3431, initiate securityinterests on collateral 4802 for smart contracts, monitor status orperformance of smart contracts 3431, terminate or initiate terminationfor default of smart contracts 3431, close smart contracts 3431,foreclose on collateral 4802 or assets, transfer title, or the like,such as by using monitoring system layers 3306 to monitor expertentities 3330, such as human managers, as they undertake a training setof similar tasks and actions in the creation, configuration, titleconfirmation, initiation of security interests, monitoring, termination,closing, foreclosing, and the like for a training set of smart contracts3431. Once an RPA system 3442 is trained, it may efficiently create theability to provide lending at scale across a wide range of entities andassets that may serve as collateral 4802, that may provide guarantees orsecurity, or the like, thereby making loans more readily available for awider range of situations, entities 3330, and collateral 4802. The RPAsystem 3442 may itself be improved by artificial intelligence 3448, suchas by continuously adjusting model parameters, weights, configurations,or the like based on outcomes, such as loan performance outcomes,collateral valuation outcomes, default outcomes, closing rate outcomes,interest rate outcomes, yield outcomes, return-on-investment outcomes,or others. Smart contracts 3431 may include or be used for directlending, syndicated lending, and secondary lending contracts, individualloans or aggregated tranches of loans, and the like.

In embodiments, the lending solution 3410 of the financial andtransactional management application platform layer 3302 may, in variousoptional embodiments, include, integrate with, or interact with (such aswithin other embodiments of the platform 3300) a set of applications3312, such as ones by which a lender, a borrower, a guarantor, anoperator or owner of a transactional or financial entity, or other user,may manage, monitor, control, analyze, or otherwise interact with one ormore elements related to a loan, such as an entity 3330 that is a partyto a loan, the subject of a loan, the collateral for a loan, orotherwise relevant to the loan. This may include any of the elementsnoted above in connection with FIG. 33 . The set of applications 3312may include a lending application 3410 (such as, without limitation, forpersonal lending, commercial lending, collateralized lending,microlending, peer-to-peer lending, insurance-related lending,asset-backed lending, secured debt lending, corporate debt lending,student loans, subsidized loans, mortgage lending, municipal lending,sovereign debt, automotive lending, pay day loans, loans againstreceivables, factoring transactions, loans against guaranteed or assuredpayments (such as tax refunds, annuities, and the like), and manyothers). The lending solution 3410 may include, integrate with, or linkwith one or more of any of a wide range of other types of applicationsthat may be relevant to lending, such as an investment application 3402(such as, without limitation, for investment in tranches of loans,corporate debt, bonds, syndicated loans, municipal debt, sovereign debt,or other types of debt-related securities); an asset managementapplication 3404 (such as, without limitation, for managing assets thatmay be the subject of a loan, the collateral for a loan, assets thatback a loan, the collateral for a loan guarantee, or evidence ofcreditworthiness, assets related to a bond, investment assets, realproperty, fixtures, personal property, real estate, equipment,intellectual property, vehicles, and other assets); a risk managementapplication 3408 (such as, without limitation, for managing risk orliability with respect to subject of a loan, a party to a loan, or anactivity relevant to the performance of a loan, such as a product, anasset, a person, a home, a vehicle, an item of equipment, a component,an information technology system, a security system, a security event, acybersecurity system, an item of property, a health condition,mortality, fire, flood, weather, disability, business interruption,injury, damage to property, damage to a business, breach of a contract,and others); a marketing application 3412 (such as, without limitation,an application for marketing a loan or a tranche of loans, a customerrelationship management application for lending, a search engineoptimization application for attracting relevant parties, a salesmanagement application, an advertising network application, a behavioraltracking application, a marketing analytics application, alocation-based product or service targeting application, a collaborativefiltering application, a recommendation engine for loan-related productor service, and others); a trading application 3428 (such as, withoutlimitation, an application for trading a loan, a tranche of loans, aportion of a loan, a loan-related interest, or the like, such as abuying application, a selling application, a bidding application, anauction application, a reverse auction application, a bid/ask matchingapplication, or others); a tax application 3414 (such as, withoutlimitation, for managing, calculating, reporting, optimizing, orotherwise handling data, events, workflows, or other factors relating toa tax-related impact of a loan); a fraud prevention application 3416(such as, without limitation, one or more of an identity verificationapplication, a biometric identity validation application, atransactional pattern-based fraud detection application, alocation-based fraud detection application, a user behavior-based frauddetection application, a network address-based fraud detectionapplication, a black list application, a white list application, acontent inspection-based fraud detection application, or other frauddetection application; a security application, solution or service 3418(referred to herein as a security application, such as, withoutlimitation, any of the fraud prevention applications 3416 noted above,as well as a physical security system (such as for an access controlsystem (such as using biometric access controls, fingerprinting, retinalscanning, passwords, and other access controls), a safe, a vault, acage, a safe room, or the like), a monitoring system (such as usingcameras, motion sensors, infrared sensors and other sensors), a cybersecurity system (such as for virus detection and remediation, intrusiondetection and remediation, spam detection and remediation, phishingdetection and remediation, social engineering detection and remediation,cyberattack detection and remediation, packet inspection, trafficinspection, DNS attack remediation and detection, and others) or othersecurity application); an underwriting application 3420 (such as,without limitation, for underwriting any loan, guarantee, or otherloan-related transaction or obligation, including any application fordetecting, characterizing or predicting the likelihood and/or scope of arisk, including underwriting based on any of the data sources, events orentities noted throughout this disclosure or the documents incorporatedherein by reference); a blockchain application 3422 (such as, withoutlimitation, a distributed ledger capturing a series of transactions,such as debits or credits, purchases or sales, exchanges of in kindconsideration, smart contract events, or the like, a cryptocurrencyapplication, or other blockchain-based application); a real estateapplication 3424 (such as, without limitation, a real estate brokerageapplication, a real estate valuation application, a real estate mortgageor lending application, a real estate assessment application, or other);a regulatory application 3426 (such as, without limitation, anapplication for regulating the terms and conditions of a loan, such asthe permitted parties, the permitted collateral, the permitted terms forrepayment, the permitted interest rates, the required disclosures, therequired underwriting process, conditions for syndication, and manyothers); a platform-operated marketplace application, solution orservice 3327 (referred to as a marketplace application, such as, withoutlimitation, a loan syndication marketplace, a blockchain-basedmarketplace, a cryptocurrency marketplace, a token-based marketplace, amarketplace for items used as collateral, or other marketplace); awarranty or guarantee application 3417 (such as, without limitation, anapplication for a warranty or guarantee with respect to an item that isthe subject of a loan, collateral for a loan, or the like, such as aproduct, a service, an offering, a solution, a physical product,software, a level of service, quality of service, a financialinstrument, a debt, an item of collateral, performance of a service, orother item); an analytics solution 3419 (such as, without limitation, ananalytic application with respect to any of the data types,applications, events, workflows, or entities mentioned throughout thisdisclosure or the documents incorporated by reference herein, such as abig data application, a user behavior application, a predictionapplication, a classification application, a dashboard, a patternrecognition application, an econometric application, a financial yieldapplication, a return on investment application, a scenario planningapplication, a decision support application, and many others); a pricingapplication 3421 (such as, without limitation, for pricing of interestrates and other terms and conditions for a loan). Thus, the financialand transactional management application platform layer 3302 may hostand enable interaction among a wide range of disparate applications 3312(such terms including the above-referenced and other financial ortransactional applications, services, solutions, and the like), suchthat by virtue of shared microservices, shared data infrastructure, andshared intelligence, any pair or larger combination or permutation ofsuch services may be improved relative to an isolated application of thesame type.

In embodiments, the data collection systems 3318 and the monitoringsystem layers 3306 may monitor one or more events related to a loan,debt, bond, factoring agreement, or other lending transaction, such asevents related to requesting a loan, offering a loan, accepting a loan,providing underwriting information for a loan, providing a creditreport, deferring a required payment, setting an interest rate for aloan, deferring a payment requirement, identifying collateral or assetsfor a loan, validating title for collateral or security for a loan,recording a change in title of property, assessing the value ofcollateral or security for a loan, inspecting property that is involvedin a loan, a change in condition of an entity relevant to a loan, achange in value of an entity that is relevant to a loan, a change in jobstatus of a borrower, a change in financial rating of a lender, a changein financial value of an item offered as a security, providing insurancefor a loan, providing evidence of insurance for property related to aloan, providing evidence of eligibility for a loan, identifying securityfor a loan, underwriting a loan, making a payment on a loan, defaultingon a loan, calling a loan, closing a loan, setting terms and conditionsfor a loan, foreclosing on property subject to a loan, and modifyingterms and conditions for a loan.

Microservices Lending Platform with Data Collection Services, Blockchainand Smart Contracts

In embodiments, provided herein is a platform, consisting of variousservices, components, modules, programs, systems, devices, algorithms,and other elements, for lending. An example platform or system forlending includes a set of microservices having a set of applicationprogramming interfaces that facilitate connection among themicroservices and to the microservices by programs that are external tothe platform, wherein the microservices include (a) a multi-modal set ofdata collection services that collect information about and monitorentities related to a lending transaction; (b) a set of blockchainservices for maintaining a secure historical ledger of events related toa loan, the blockchain services having access control features thatgovern access by a set of parties involved in a loan; (c) a set ofapplication programming interfaces, data integration services, dataprocessing workflows and user interfaces for handling loan-relatedevents and loan-related activities; and (d) a set of smart contractservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system includes where the entities relevant to lending include aset of entities among lenders, borrowers, guarantors, equipment, goods,systems, fixtures, buildings, storage facilities, and items ofcollateral.

An example system includes where collateral items are monitored and thecollateral items are selected from among a vehicle, a ship, a plane, abuilding, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

An example system includes where the multi-modal set of data collectionservices include services selected from among a set of Internet ofThings systems that monitor the entities, a set of cameras that monitorthe entities, a set of software services that pull information relatedto the entities from publicly available information sites, a set ofmobile devices that report on information related to the entities, a setof wearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

An example system includes where the events related to a loan areselected from requesting a loan, offering a loan, accepting a loan,providing underwriting information for a loan, providing a creditreport, deferring a required payment, setting an interest rate for aloan, deferring a payment requirement, identifying collateral for aloan, validating title for collateral or security for a loan, recordinga change in title of property, assessing the value of collateral orsecurity for a loan, inspecting property that is involved in a loan, achange in condition of an entity relevant to a loan, a change in valueof an entity that is relevant to a loan, a change in job status of aborrower, a change in financial rating of a lender, a change infinancial value of an item offered as a security, providing insurancefor a loan, providing evidence of insurance for property related to aloan, providing evidence of eligibility for a loan, identifying securityfor a loan, underwriting a loan, making a payment on a loan, defaultingon a loan, calling a loan, closing a loan, setting terms and conditionsfor a loan, foreclosing on property subject to a loan, and modifyingterms and conditions for a loan.

An example system includes where the set of terms and conditions for theloan that are specified and managed by the set of smart contractservices is selected from among a principal amount of debt, a balance ofdebt, a fixed interest rate, a variable interest rate, a payment amount,a payment schedule, a balloon payment schedule, a specification ofcollateral, a specification of substitutability of collateral, a party,a guarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default.

An example system includes where a set of parties to the loan isselected from among a primary lender, a secondary lender, a lendingsyndicate, a corporate lender, a government lender, a bank lender, asecured lender, a bond issuer, a bond purchaser, an unsecured lender, aguarantor, a provider of security, a borrower, a debtor, an underwriter,an inspector, an assessor, an auditor, a valuation professional, agovernment official, and an accountant.

An example system includes where loan-related activities includeactivities selected from the set of finding parties interested inparticipating in a loan transaction, an application for a loan,underwriting a loan, forming a legal contract for a loan, monitoringperformance of a loan, making payments on a loan, restructuring oramending a loan, settling a loan, monitoring collateral for a loan,forming a syndicate for a loan, foreclosing on a loan, and closing aloan transaction.

An example system includes where the loan is of at least one typeselected from among an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

An example system includes where the set of smart contract servicesconfigures at least one smart contract to automatically undertake aloan-related action based on information collected by the multi-modalset of data collection services.

An example system includes where the loan-related action is selectedfrom among offering a loan, accepting a loan, underwriting a loan,setting an interest rate for a loan, deferring a payment requirement,modifying an interest rate for a loan, validating title for collateral,recording a change in title, assessing the value of collateral,initiating inspection of collateral, calling a loan, closing a loan,setting terms and conditions for a loan, providing notices required tobe provided to a borrower, foreclosing on property subject to a loan,and modifying terms and conditions for a loan.

An example system includes where the platform or system may furtherinclude an automated agent that processes events relevant to at leastone of the value, the condition, and the ownership of items ofcollateral and undertakes an action related to a loan to which thecollateral is subject.

Referring to FIG. 49 , additional applications, solutions, programs,systems, services and the like that may be present in a lending solution3410 are depicted, which may be interchangeably included in thefinancial and transactional management application platform layer 3302with other elements noted in connection with FIG. 48 and elsewherethroughout this disclosure and the documents incorporated herein byreference. Also depicted are additional entities 3330, which should beunderstood to be interchangeable with the other entities 3330 describedin connection with various embodiments described herein. In addition toelements already noted above, the lending solution 3410 may include aset of applications, solutions, programs, systems, services and the likethat include one or more of a social network analytics solution 4904that may find and analyze information about various entities 3330 asdepicted in one or more social networks (such as, without limitation,information about parties, behavior of parties, conditions of assets,events relating to parties or assets, conditions of facilities, locationof collateral 4802 or assets, and the like), such as by allowing a userto configure queries that may be initiated and managed across a set ofsocial network sites using data collection systems 3318 and monitoringsystems 3306; a loan management solution 4948 (such as for managing orresponding to one or more events related to a loan, (such eventsincluding, among others, requests for a loan, offering a loan, acceptinga loan, providing underwriting information for a loan, providing acredit report, deferring a required payment, setting an interest ratefor a loan, deferring a payment requirement, identifying collateral fora loan, validating title for collateral or security for a loan,recording a change in title of property, assessing the value ofcollateral or security for a loan, inspecting property that is involvedin a loan, a change in condition of an entity relevant to a loan, achange in value of an entity that is relevant to a loan, a change in jobstatus of a borrower, a change in financial rating of a lender, a changein financial value of an item offered as a security, providing insurancefor a loan, providing evidence of insurance for property related to aloan, providing evidence of eligibility for a loan, identifying securityfor a loan, underwriting a loan, making a payment on a loan, defaultingon a loan, calling a loan, closing a loan, setting terms and conditionsfor a loan, foreclosing on property subject to a loan, and modifyingterms and conditions for a loan) for setting terms and conditions for aloan (such as a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default), or managing loan-related activities (such as, withoutlimitation, finding parties interested in participating in a loantransaction, handling an application for a loan, underwriting a loan,forming a legal contract for a loan, monitoring performance of a loan,making payments on a loan, restructuring or amending a loan, settling aloan, monitoring collateral for a loan, forming a syndicate for a loan,foreclosing on a loan, collecting on a loan, consolidating a set ofloans, analyzing performance of a loan, handling a default of a loan,transferring title of assets or collateral, and closing a loantransaction); a rating solution 6801 (such as for rating an entity 3330(such as a party 4910, collateral 4802, asset 4918 or the like), such asinvolving rating of creditworthiness, financial health, physicalcondition, status, value, presence or absence of defects, quality, orother attribute); a regulatory and/or compliance solution 3426 (such asfor enabling specification, application and/or monitoring of one or morepolicies, rules, regulations, procedures, protocols, processes, or thelike, such as ones that relate to terms and conditions of loantransactions, steps required in forming lending transactions, stepsrequired in performing lending transactions, steps required with respectto security or collateral, steps required for underwriting, stepsrequired for setting prices, interest rates, or the like, steps requiredto provide required legal disclosures and notices (e.g., presentingannualized percentage rates) and others); a custodial solution or set ofcustodial services 6502 (such as for taking custody of a set of assets4918, collateral 4802, or the like (including cryptocurrencies,currency, securities, stocks, bonds, agreements evidencing ownershipinterests, and many other items), such as on behalf of a party 4910,client, or other entity 3330 that needs assistance in maintainingsecurity of the items, or in order to provide security, backing, or aguarantee for an obligation, such as one involved in a lendingtransaction); a marketing solution 6702 (such as for enabling a lenderto market availability of a loan to a set of prospective borrowers, totarget a set of borrowers who are appropriate for a type of transaction,to configure marketing or promotional messages (including placement andtiming of the message), to configure advertisement and promotionalchannels for lending transactions, to configure promotional or loyaltyprogram parameters, and many others); a brokering solution 4944 (such asfor brokering a set of loan transactions among a set of parties, such asa mortgage loan), which may allow a user to configure a set ofpreferences, profiles, parameters, or the like to find a set ofprospective counterparties to a lending transaction; a bond managementsolution 4934 such as for managing, reporting on, syndicating,consolidating, or otherwise handling a set of bonds (such as municipalbonds, corporate bonds, performance bonds, and others); a guaranteemonitoring solution 4930, such as for monitoring, classifying,predicting, or otherwise handling the reliability, quality, status,health condition, financial condition, physical condition or otherinformation about a guarantee, a guarantor, a set of collateralsupporting a guarantee, a set of assets backing a guarantee, or thelike; a negotiation solution 4932, such as for assisting, monitoring,reporting on, facilitating and/or automating negotiation of a set ofterms and conditions for a lending transaction (such as, withoutlimitation, a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclosure condition, a default condition, and aconsequence of default), which may include a set of user interfaces forconfiguration of parameters, profiles, preferences, or the like fornegotiation, such as ones that use or are informed by the lending model4808 and ones that use, are informed by, or that are automated by orwith the assistance of a set of artificial intelligence services andsystems 3448, by robotic process automation 3442, or other adaptiveintelligent systems layer 3304; a collection solution 4938 forcollecting on a loan, which may optionally use, be informed by, or beautomated by or with the assistance of a set of artificial intelligenceservices and systems 3448, by robotic process automation 3442, or otheradaptive intelligent systems layers 3304, such as based on monitoringthe status or condition of various entities 3330 with the monitoringsystem layers 3306 and data collection systems 3318 in order to triggercollection, such as when one or more covenants has not been met, whencollateral is in poor condition, when financial health of a party isbelow a threshold, or the like; a consolidation solution 4940 forconsolidating a set of loans, such as using a lending model 4808 that isconfigured for modeling a consolidated set of loans and such as using orbeing automated by one or more adaptive intelligent systems layers 3304;a factoring solution 4942, such as for monitoring, managing, automatingor otherwise handling a set of factoring transactions, such as using alending model 4808 that is configured for modeling factoringtransactions and such as using or being automated by one or moreadaptive intelligent systems layer 3304; a debt restructuring solution4928, such as for restructuring a set of loans or debt, such as using alending model 4808 that is configured for modeling alternative scenariosfor restructuring a set of loans or debt and such as using or beingautomated by one or more adaptive intelligent systems layers 3304;and/or an interest rate setting solution 4924, such as for setting orconfiguring a set of rules or a model for a set of interest rates for aset of lending transactions or for automating interest rate settingbased on information collected by data collection systems 3318 ormonitoring system layers 3306 (such as information about conditions,status, health, location, geolocation, storage condition, or otherrelevant information about any of the entities 3330), which may setinterest rates or facilitate setting of interest rates for a set ofloans, such as using a lending model 4808 that is configured formodeling interest rate scenarios for a set of loans and such as using orbeing automated by one or more of the adaptive intelligent systemslayers 3304. As with the solutions referenced in connection with FIG. 48, the various solutions may share the adaptive intelligent systemslayers 3304, the monitoring systems 3306, the data collection systems3318 and the storage systems 3310, such as by being integrated into theplatform 4800 in a microservices architecture having various appropriatedata integration services, APIs, and interfaces.

As with the entities 3330 described in connection with FIG. 49 ,entities 3330 may further include a range of entities that are involvedwith loans, debt transactions, bonds, factoring agreements, and otherlending transactions, such as: collateral 4802 and assets 4918 that areused to secure, guarantee, or back a payment obligation (such asvehicles, ships, planes, buildings, homes, real estate, undevelopedland, farms, crops, facilities (such as municipal facilities, factories,warehouses, storage facilities, treatment facilities, plants, andothers), systems, a set of inventory, commodities, securities,currencies, tokens of value, tickets, cryptocurrencies, consumables,edibles, beverages, precious metals, jewelry, gemstones, intellectualproperty, intellectual property rights, contractual rights, legalrights, antiques, fixtures, equipment, furniture, tools, machinery andpersonal property); a set of parties 4910 (such as one or more of aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, a bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, an agent, an attorney, a valuation professional, agovernment official, and/or an accountant); a set of agreements 4920(such as loans, bonds 4912, lending agreements, corporate debtagreements, subsidized loan agreements, factoring agreements,consolidation agreements, syndication agreements, guarantee agreements,underwriting agreements, and others, which may include a set of termsand conditions that may be searched, collected, monitored, modified orotherwise handled by the platform 4800, such as interest rates, paymentschedules, payment amounts, principal amounts, representations andwarranties, indemnities, covenants, and other terms and conditions); aset of guarantees 4914 (such as provided by personal guarantors,corporate guarantors, government guarantors, municipal guarantors andothers to secure or back a payment obligation or other obligation of alending agreement 4920); a set of performance activities 4922 (such asmaking payments of principal and/or interest, maintaining requiredinsurance, maintaining title, satisfying covenants, maintainingcondition of collateral 4802 or assets 4918, conducting business asrequired by an agreement; and many others); and devices 4952 (such asInternet of Things devices that may be disposed on or in goods,equipment or other items, such as ones that are collateral 4802 orassets 4918 used to back a payment obligation or to satisfy a covenantor other requirement, or that may be disposed on or in packaging forgoods, as well as ones disposed in facilities or other environmentswhere entities 3330 may be located). In embodiments, an agreement 4920may be for a bond, a factoring agreement, a syndication agreement, aconsolidation agreement, a settlement agreement, or a loan, such as oneor more of an auto loan, an inventory loan, a capital equipment loan, abond for performance, a capital improvement loan, a building loan, aloan backed by an account receivable, an invoice finance arrangement, afactoring arrangement, a pay day loan, a refund anticipation loan, astudent loan, a syndicated loan, a title loan, a home loan, a venturedebt loan, a loan of intellectual property, a loan of a contractualclaim, a working capital loan, a small business loan, a farm loan, amunicipal bond, and a subsidized loan.

As noted elsewhere herein and in documents incorporated by reference,artificial intelligence (such as any of the techniques or systemsdescribed throughout this disclosure) in connection with varioustransactional and marketplace entities 3330 and related processes andapplications may be used to facilitate, among other things: (a) theoptimization, automation and/or control of various functions, workflows,applications, features, resource utilization and other factors, (b)recognition or diagnosis of various states, entities, patterns, events,contexts, behaviors, or other elements; and/or (c) the forecasting ofvarious states, events, contexts or other factors. As artificialintelligence improves, a large array of domain-specific and/or generalartificial intelligence systems have become available and are likely tocontinue to proliferate. As developers seek solutions to domain-specificproblems, such as ones relevant to entities 3330 and applications of theplatform 100 described throughout this disclosure, they face challengesin selecting artificial intelligence models (such as what set of neuralnetworks, machine learning systems, expert systems, or the like toselect) and in discovering and selecting what inputs may enableeffective and efficient use of artificial intelligence for a givenproblem. As noted above, opportunity mining modules 153 may assist withthe discovery of opportunities for increased automation andintelligence; however, once opportunities are discovered, selection andconfiguration of an artificial intelligence solution still presents asignificant challenge, one that is likely to continue to grow asartificial intelligence solutions proliferate.

One set of solutions to these challenges is an artificial intelligencestore 157 that is configured to enable collection, organization,recommendation, and presentation of relevant sets of artificialintelligence systems based on one or more attributes of a domain and/ora domain-related problem. In embodiments, an artificial intelligencestore 157 may include a set of interfaces to artificial intelligencesystems, such as enabling the download of relevant artificialintelligence applications, establishment of links or other connectionsto artificial intelligence systems (such as links to cloud-deployedartificial intelligence systems via APIs, ports, connectors, or otherinterfaces) and the like. The artificial intelligence store 157 mayinclude descriptive content with respect to each of a variety ofartificial intelligence systems, such as metadata or other descriptivematerial indicating suitability of a system for solving particular typesof problems (e.g., forecasting, NLP, image recognition, patternrecognition, motion detection, route optimization, or many others)and/or for operating on domain-specific inputs, data, or other entities.In embodiments, the artificial intelligence store 157 may be organizedby category, such as domain, input types, processing types, outputtypes, computational requirements and capabilities, cost, energy usage,and other factors. In embodiments, an interface to the artificialintelligence store 157 may take input from a developer and/or from theplatform (such as from an opportunity mining module 153) that indicatesone or more attributes of a problem that may be addressed throughartificial intelligence and may provide a set of recommendations, suchas via an artificial intelligence attribute search engine, for a subsetof artificial intelligence solutions that may represent favorablecandidates based on the developer's domain-specific problem.

In embodiments, criteria for determining the recommendation may includelevel of anticipated human oversight. This may include, among others,understanding the level and types of decisions delegated to humanworkers (such as a decision to purchase a security, taking a marketdecision, taking a license on Intellectual property, financial limits onactions and ordering (e.g. is the RPA able to order or commit totransactions below a certain amount, above which a human is involved),the level and type of anticipated human supervision of robotic processautomation operations, anticipated extent of human supervision and/orgovernance of model training and training data set selection. A furtherconsideration may be the level and type of anticipated human involvementin the curation of model versions (such as identifying historical breakpoints where input data should be discarded); and others.

In embodiments, criteria for determining the recommendation may includesecurity considerations such as adversarial training and complexenvironments such as network attacks, viruses, and the like. Additionalsecurity considerations may include the security and management ofhistoric training datasets, including audit trails. Securityconsiderations may include the model traceability and accuracy, how willthe model or controlling parameters be updated, who will have authorityto update the model, how will the updates be documented, how willresults be correlated with model updates, how will version control beimplemented and documented, and the like. Another security considerationwill be documentation of the results of the AI for audit trails,including financial results and performance results.

In embodiments, criteria for determining the recommendation may includethe availability of different AI types, models, algorithms, or systems(including heuristic/model-based AI, neural networks, and others).Availability may be limited by the computational environment that theuser intends to use such as a given cloud platform, an on-premises ITsystem, or in a network (edge or other networks), and the like andwhether a given type, model, or algorithm will run in the client'senvironment. In embodiments, computational factors and configurationsmay be criteria. For example, the available processor types for runningthe AI solution in the client's environment may be a factor including:chipsets, modules, device, cloud components, number, and architecture ofprocessor types (e.g. multi-core processor availability, GPUavailability, CPU availability, FPGA availability, custom ASICavailability, and the like), and the like. Additionally, computationalfactors, which may be expressed as minimum capability criteria, mayinclude available processing capacity, both for solution training (forexample utilizing a cloud computing resource) and solution operationdeployment environment/capacity (e.g. IoT, in-vehicle, edge, meshnetwork, on-premises IT solution, stand along, or other deploymentenvironments). Additional criteria may include software and interfacecriteria such as software environment such as operating systems (Linux,Mac, PC, and the like), languages and protocols used for APIs for accessto input data sources for solution training as well as access to runtimedata and data integration and output.

In embodiments, criteria may include various network factors such asavailable network type, available network bandwidth (input and output)for both AI solution and AI operation, network uptime, networkredundance, variability of delivery times (sequencing of data may vary),as well as any of the other networks and network criteria describedherein.

In embodiments, criteria may include performance or quality of servicefactors, either in absolute terms or relative to other AI and/or non-AIsolutions (e.g. conventional models or rule-based solutions. Criteriamay include speed/latency, time to train/configure and an AI solution,time for the AI solution to provide result in an operations situation,accuracy, reliability (e.g. ability to resolve to a result),consistency, absence of bias, outcome-based measures of quality such asreturn on investment (ROI), yield (e.g. output from an AI-governedoperation), profitability, revenue and other economic measures,performance on safety measures, performance on security measures, energyconsumption (e.g. overall consumption, timing-based consumption (e.g.ability to shift processing from peak to off-peak hours), ability toaccess renewable or low-carbon energy for model training and/oroperation, management of cost of new model training initiatives (powercosts, latency and validation of new models), and the like), and thelike.

In embodiments, criteria may include the ability of the client to accessa given type or model due to license requirements and limitations,client policies (described elsewhere herein), regulations (including inthe client's jurisdiction, the jurisdiction of the data source (e.g.European data privacy laws and Safe Harbor), a jurisdiction governing aparticular model, algorithm, or the like (e.g. export controls ontechnology), permissions (e.g. training data or operational data), andthe like. Additionally, the recommendation may be influenced by the typeof problem to be solved and whether there are specialized algorithms ormethods that are optimized for the type of problem (e.g. quantumannealing based traveling salesperson solver or even classic heuristicmethods that provide for reasonable baseline results).

In embodiments, criteria may include conformance or adherence togovernance principles and policies. There may be policies regarding whatinput data sources may be used to train the AI solution. There may bepolicies regarding what input sources may be used during operation. Forexample, input data sources may be reviewed for potential bias,appropriate representation (either demographically or of the problemspace), scope, and the like. There may be criteria regardingaccreditation or approval of the solution by a regulatory body,certification organization, internal IT review, and the like. There maybe policies and procedures that must be in place or implemented withrespect to security (e.g. physical security of the system,cybersecurity, and the like), safety requirements (e.g. the safety ofthe user, the safety of output product, and the like), and the like.

In embodiments, the criteria for recommending an AI solution may includecriteria regarding data availability such as the availability of datasources of adequate size, granularity, quality, reliability, location,time zones, accuracy, or the like for effective model training.Additional criteria regarding data availability may include the cost ofdata for: inputs for the model training, and/or input for modeloperation. Additional criteria may include the availability of data foroperation of the AI solution, and the like. Criteria for AI selectionmay further include upstream data processing requirements, master datamanagement considerations such as dimensional cleanup and datavalidation, and the like.

In embodiments, criteria for solution selection may includeapplicability of the model or solution to the given task or workflow ofthe “problem.” Criteria may include benchmark performance of a givenmodel relative to other models performing a known task type (e.g. aconvolutional neural network for 2D object classification, a gatedrecurrent neural network for tasks that tend to produce explodingerrors, or the like). In embodiments, selection of a solution may bebased on the solution having a configuration that is similar oranalogous to how a biological brain solves a similar task (e.g. where asequence of neural network models are arranged to mimic a sequence orflow which may include serial elements, parallel elements, feedbackloops, conditional logic junctions, graph-driven elements and other flowcharacteristics), such as a flow of modular or quasi-modular processes,such as ones involved in the brain of a human or other species, such asin visual or auditory processing, language recognition, speech, motiontracking, image recognition, facial recognition, motion coordination,tactile recognition, spatial orientation, and the like. Criteria mayinclude application of class AI heuristic methods to function as guardrails or operations in less impactful areas.

In embodiments, criteria may include model deployment considerationssuch as requirements for model updates (e.g. frequency and requirementfor retirement of models), management of historic models and maintaininghistorical decision engine, potential for distributed decision makingcapabilities, model curation rules (e.g. how long a model or input dataare considered valid for training), and the like.

Search results or recommendations may, in embodiments, be based at leastin part on collaborative filtering, such as by asking developers toindicate or select elements of favorable models, as well as byclustering, such as by using similarity matrices, k-means clustering, orother clustering techniques that associate similar developers, similardomain-specific problems, and/or similar artificial intelligencesolutions. The artificial intelligence store 157 may include e-commercefeatures, such as ratings, reviews, links to relevant content, andmechanisms for provisioning, licensing, delivery, and payment (includingallocation of payments to affiliates and or contributors), includingones that operate using smart contract and/or blockchain features toautomate purchasing, licensing, payment tracking, settlement oftransactions, or other features.

In embodiments, once a solution has been selected or recommended, thesolution must be configured for the specific client and problem to besolved. Without limitation, configuration may include any of the factorsmentioned in connection with the selection of a solution model above.Configuration of a set of neural network types (e.g., modules) in a flow(with options for serial elements, parallel elements, feedback loops,conditional logic junctions, graph-driven flows, and the like) thatrecognizes the relative strengths and weaknesses of each type of AIsolution (based on any of the selection factors noted above) for thespecific task involved in the flow is critical. In an illustrative andnon-limiting example of a flow includes: a) identify something by visualclassification (such as with a CNN), b) predict its future state (suchas with a gated RNN), c) optimize the future state (using a feed forwardneural network). Configuration options include selection of neuralnetwork type(s) (including hybrids of different neural networks and/orother model types in various flows as noted above); selection of inputmodel type; setting of initial model weights; setting model size (e.g.,number of layers in a deep neural network); selection of computationaldeployment environment; selection of input data sources for training;selection of input data sources for operation; selection of feedbackfunction/outcome measures; selection of data integration language(s) forinputs and outputs; configuration of APIs for model training;configuration of APIs for model inputs; configuration of APIs foroutputs; configuration of access controls (role-based, user-based,policy-based and others); configuration of security parameters;configuration of network protocols; configuration of storage parameters(type, location, duration); configuration of economic factors (e.g.,pricing for access; cost-allocation; and others); and others. Additionalconfiguration options may include configuration of data flows (e.g.flows from multiple security exchanges into centralized decisionengines), configuration of high availability, fault toleranceenvironments (e.g. trading systems are required to fail down tooperation state that meets services levels requirements), price baseddata acquisition strategies (e.g. detailed financial data may requireadditional spending), combination with heuristic methods, coordinationof massively parallel decision making environments (e.g. distributedvision systems), and the like. Additional configurations may includemaking decision models if there is an area that requires furtherconsideration (e.g. pushing a decision to the edge to monitor for aspecific event).

In embodiments, another set of solutions, which may be deployed alone orin connection with other elements of the platform, including theartificial intelligence store, may include a set of functional imagingcapabilities, which may comprise monitoring systems 3306 and datacollection systems 3318 and, in some cases, physical process observationsystems 3458 and/or software interaction observation systems 3450, suchas for monitoring various transactional and marketplace entities 3330.Functional imaging systems may, in embodiments, provide considerableinsight into the types of artificial intelligence that are likely to bemost effective in solving particular types of problems most effectively.As noted elsewhere in this disclosure and in the documents incorporatedby reference herein, computational and networking systems, as they growin scale, complexity and interconnections, manifest problems ofinformation overload, noise, network congestion, energy waste, and manyothers. As the Internet of Things grows to hundreds of billions ofdevices, and virtually countless potential interconnections,optimization becomes exceedingly difficult. One source for insight isthe human brain, which faces similar challenges and has evolved, overmillennia, reasonable solutions to a wide range of very difficultoptimization problems. The human brain operates with a massive neuralnetwork organized into interconnected modular systems, each of which hasa degree of adaptation to solve particular problems, from regulation ofbiological systems and maintenance of homeostasis to detection of a widerange of static and dynamic patterns, to recognition of threats andopportunities, among many others.

Setting up a robotic process automation (RPA) system includes selectionof the best AI solution and configuration. There may be goals to trainthe RPA system, typically on human interactions with software and orhardware (e.g., tools) and to use the system in operation, both of whichmay be enhanced by understanding what is going on in the human brain asit solves a problem. In a single neural network solution (using onenetwork to solve a problem in a single step, like single-steptranslation), the process would likely involve setting initial weightsfor inputs, selection of input data sources, selection of the type ofnetwork (e.g., convolutional or not, gated or not, deep or not, amongothers), the number of layers, and what inputs are provided to it (andoutputs if there are complex outputs). The idea would be to pick inputsand weights that are the ones the human brain tends to use to solve thesame problem. For hybrids of multiple AI modules/systems and/or AIcombined with more conventional software systems (like control systems,analytic models, rule-based systems, conditional logic systems, andothers), the value would likely be the above, plus configuring withawareness of time sequences of processing, such as reflecting patternsof brain activity as visual, auditory, tactile and other sensoryinformation is processed to recognize situation, context, motion,objects, etc. and then other regions (that behave differently) to dothings like solve a logic puzzle, calculate, follow an algorithm,proliferate possibilities, and many others. For these, a series of “legoblocks,” each consisting of a different neural network or other AI type,can be sequenced, set in parallel, linked by conditional logic, etc. toachieve a solution that automates the process.

In embodiments, identification of a type of reasoning and/or a type ofprocessing may be informed by undertaking brain imaging, such asfunctional MRI or other magnetic imaging, electroencephalogram (EEG), orother imaging, such as by identifying broad brain activity (e.g., wavebands of activity, such as delta, theta, alpha and gamma waves), byidentifying a set of brain regions that are activated and/or inactiveduring the set interactions of the user that are being used for trainingof the intelligent agent (such as neocortex regions, such as Fp1(involved in judgment and decision making), F7 (involved in imaginationand mimicry), F3 (involved in analytic deduction), T3 (involved inspeech), C3 (involved in storage of facts), T5 (involved in mediationand empathy), P3 (involved in tactical navigation), O1 (involved invisual engineering), Fp2 (involved in process management), F8 (involvedin belief systems), F4 (involved in expert classification), T4 (involvedin listening and intuition), C4 (involved in artistic creativity), T6(involved in prediction), P4 (involved in strategic gaming), O2(involved in abstraction), and/or combinations of the foregoing) or byother neuroscientific, psychological, or similar techniques that provideinsight into how humans, upon which the intelligent agent is trained,are solving particular types of problems that are involved in workflowsfor which intelligent agents are deployed. In embodiments, anintelligent agent may be configured with a neural network type, orcombination of types, that is selected to replicate or simulate aprocessing activity that is similar to the activity of the brain regionsof a human expert that is performing a set of activities for which theintelligent agent is to be trained. As one example among many possible,a trader may be shown to use visual processing region O1 and strategicgaming region P4 of the neocortex when making successful trades, and aneural network may be configured with a convolutional neural network toprovide effective replication of visual pattern recognition and a gatedrecurrent neural network to replicate strategic gaming. In embodiments,a library of neural network resources representing combinations ofneural network types that mimic or simulate neocortex activities may beconfigured to allow selection and implementation of modules thatreplicate the combinations used by human experts to undertake variousactivities that are subjects of development of intelligent agents, suchas involving robotic process automation. In embodiments, various neuralnetwork types from the library may be configured in series and/or inparallel configurations to represent processing flows, which may bearranged to mimic or replicate flows of processing in the brain, such asbased on spatiotemporal imaging of the brain when involved in theactivity that is the subject of automation. In embodiments, anintelligent software agent for agent development may be trained, such asusing any of the training techniques described herein, to select a setof neural network resource types, to arrange the neural network resourcetypes according to a processing flow, to configure input data sourcesfor the set of neural network resources, and/or to automatically deploythe set of neural network types on available computational resources toinitiate training of the configured set of neural network resources toperform a desired intelligent agent/automation workflow(s). Inembodiments, the intelligent software agent used for agent developmentoperates on an input data set of spatiotemporal imaging data of a humanbrain, such as an expert who is performing the workflows, and uses thespatiotemporal imaging data to automatically select and configure theselection and arrangement of the set of neural network types to initiatelearning. Thus, a system for developing an intelligent agent may beconfigured for (optionally automatic) selection of neural network typesand/or arrangements based on spatiotemporal neocortical activitypatterns of human users involved in workflows for which the agent istrained. Once developed, the resulting intelligent agent/processautomation system may be trained as described throughout thisdisclosure.

In embodiments, a system for developing an intelligent agent (includingthe aforementioned agent for development of intelligent agents) may useinformation from brain imaging of human users to infer (optionallyautomatically) what data sources should be selected as inputs for anintelligent agent. For example, for processes where neocortex region O1is highly active (involving visual processing), visual inputs (such asavailable information from cameras, or visual representations ofinformation like price patterns, among many others) may be selected asfavorable data sources. Similarly, for processes involving region C3(involving storage and retrieval of facts), data sources providingreliable factual information (such as blockchain-based distributedledgers) may be selected. Thus, a system for developing an intelligentagent may be configured for (optionally automatic) selection of inputdata types and sources based on spatiotemporal neocortical activitypatterns of human users involved in workflows for which the agent istrained.

Functional imaging, such as functional magnetic resonance imaging(fMRI), electroencephalogram (EEG), computed tomography (CT) and otherbrain imaging systems have improved to the point that patterns of brainactivity can be recognized in real time and temporally associated withother information, such as behaviors, stimulus information,environmental condition data, gestures, eye movements, and otherinformation, such that via functional imaging, either alone or incombination with other information collected by monitoring systems 3306,the platform may determine and classify what brain modules, operations,systems, and/or functions are employed during the undertaking of a setof tasks or activities, such as ones involving software interactionobservation systems 3345, physical process observations 3340, or acombination thereof. This classification may assist in selection and/orconfiguration of a set of artificial intelligence solutions, such asfrom an artificial intelligence store, that includes a similar set ofcapabilities and/or functions to the set of modules and functions of thehuman brain when undertaking an activity, such as for the initialconfiguration of a robotic process automation (RPA) system 3442 thatautomates a task performed by an expert human.

In embodiments, a system may receive and/or monitor a set of inputsrelating to a user, including image/video feeds, audio feeds, motionsensors, heartbeat monitor, other relevant biosensors, and the like. Inembodiments, the system may also receive input relating to actions takenby the monitored user, such as input to a computing device or actionstaken with respect to a physical environment in which the user isworking. In embodiments, all the collected data is time stamped, sothat, for example, a video feed may capture a series of images of a userwhile the user is performing a task and may concurrently capture the eyemovements of the user (e.g. eye gaze tracking) to determine what theuser is focusing on (e.g., what is the user looking at on a screen).During this time, the system may also track the user's heart rate orother biological sensor measurements to determine whether the user isengaged in a task that requires intense concentration or less focusedconcentration. The system may also track the actions taken and mayfurther determine the amount of time taken between actions. An RPAsolution can then distribute processing, such as to a heavier, morecomputationally intensive activity to an AI solution on a cloud platform(like a deep neural network with many layers) and placing lesscomputationally intensive tasks, such as ones where a human makes veryquick decisions on minimal input data, and/or on an edge or IoT deviceplatform using a much more compact model, such as a TinyML™ model.

In embodiments, the system may determine the relative amount of timetaken between actions, such that long periods of inaction may indicatethat the user is involved in work that requires lots of thought, whileshort periods of inaction may indicate that the user is engaged in workthat requires less thought and more action. The system may also monitoran audio feed and/or state of the computing device that a user isworking on when the period of inaction occurs, which may be indicativeof a user being distracted rather than focusing. Assuming that the useris actively working and not exhibiting distraction, then the system cangenerate a feature vector relating to the work being performed by theuser that indicates the time-stamped data entries, which can be then fedinto a machine-learned model. In embodiments, the machine-learned modelmay determine a brain region (or multiple brain regions) from the set ofbrain regions that were likely engaged during the work period. Inembodiments, the machine-learned model may be trained using a trainingdata set that includes labeled training vectors, where the label of eachtraining vector indicates the brain region (or regions) that were beingengaged by a subject when the training vector was generated. Forexample, each training vector may be labeled with one or more of: Fp1(involved in judgment and decision making), F7 (involved in imaginationand mimicry), F3 (involved in analytic deduction), T3 (involved inspeech), C3 (involved in storage of facts), T5 (involved in mediationand empathy), P3 (involved in tactical navigation), O1 (involved invisual engineering), Fp2 (involved in process management), F8 (involvedin belief systems), F4 (involved in expert classification), T4 (involvedin listening and intuition), C4 (involved in artistic creativity), T6(involved in prediction), P4 (involved in strategic gaming), O2(involved in abstraction). In some embodiments, the training vector mayindicate additional data, such as the type of task being performed,whether the subject was successful in completing the task, or othersuitable information.

In embodiments, these machine-learned models may be trained on differenttypes of work tasks, such as negotiating, drafting, data entry,responding to emails, analyzing data, reviewing documents, or the like.Furthermore, in some embodiments, such machine-learned models may betrained by one party but leveraged by other parties. In theseembodiments, the machine-learned models (and/or the training datavectors) may be bought and sold via a marketplace. Such machine-learnedmodels may be used in a broader RPA system, such that the output of themodels may be used as a specific signal in an RPA learning process.

In general, using data from organizations for predicting positioning ofthe organization in market and adjusting processes within organizationmay be performed. In example embodiments, robotic imaging may be used tocapture data of users (e.g., employees or workers) within theorganization as they complete various tasks and processes while alsocorrelating this information with completion of these tasks/processes.Various analytics may be obtained regarding success of completion oftasks (e.g., efficiency). Then, using data obtained fromtracking/monitoring users, factors may be determined that indicate someusers as being more successful than other users in completion of tasks(e.g., based on physical movements of users in doing tasks correctly,brain regions activated, physical strength of users, etc.). This may bebased on scanning/monitoring of users as they complete tasks. In someexample embodiments, the system may segregate data relating to userswith successful task completions versus data relating to users with lesssuccessful completions. The system may analyze biological data ofworkers to determine what makes one worker more successful than otherworkers. In some example embodiments, this analysis may also be combinedwith data from machines to determine whether workers are using machinesaccurately/efficiently. This biological data from workers may also beused to determine whether more workers may be needed to improveefficiency. The system may use historical data and results from processcompetitions to look at what improvements should be made whether bytraining, selecting workers who are better are some tasks vs. others,etc. The resulting analytics on outcomes, and contributions to outcomes,may be used, for example, as a feedback function for weighting the valueof particular capabilities for design of an AI solution that is intendedto perform the same or similar tasks. In some example embodiments,various data and analysis as described above may be used with respect todetermining whether improvements made based on the analysis also improvethe market positioning of the organization.

An operator skilled in a task may develop strong memory connections tomuscle functions—muscle memory—which translates into easily accomplishedactions that, without this connection, would be difficult or at leastrequire repeated attempts, slower operation, and the like. A system thatcan distinguish between actions accomplished using muscle memory andothers may better identify which actions are worthfollowing/repeating/learning.

Understanding the mechanisms of muscle memory—e.g., understanding thepathways from cognition (visual, auditory, etc.) inputs to developmuscle memory—may be a basis for understanding how to automate humanactions. This may involve repetition type actions, association of onetype of action with another type of action based on similarities, suchas body positioning, expected result (dropping the hammer in theholster, etc.), and the like.

Additional value might be in understanding how two individuals candevelop a form of muscle memory that allows them to “get into a rhythm,”such as when exchanging physical items, what cues are they exchanging,visually recognizable actions (placement of hand/orientation), and howare those interpreted.

In embodiments, an imaging system may analyze brain images of multiplemembers of a team for a set of tasks or workflows that involve differenttypes of expertise. Team performance can be tracked, and AI solutionsmay be configured to replicate the types of neural processing that areundertaken by different team members, such as motion tracking andcoordination by one team member and executive decision making byanother.

In embodiments, an imaging system may analyze brain images of multiplemembers of a mock trial or negotiation practice sessions for a set ofverbal exchanges regarding an argument, point-count-point, and the likefor negotiations, and the like. In addition to brain images, audiocapture and bio-indicators of response to exchanges could also beharvested to increase the range of multi-dimensional data useful forlearning how to automate human actions associated with successfulnegotiation and the like.

Given the level of abstraction humans use to trigger actions, e.g.,recognizing an alarm tone or recognizing an action from a fellow worker,we can get less abstract in machine-machine communication, e.g., theinput that triggered the alarm tone can trigger a direct machine-machinecommunication or, if the fellow worker is now a machine, they canindicate their positioning in their routine indicating they are ready tohand-off their work. This is similar to how less intelligent robots havebeen automated, even with simple macros where the “intelligence” iswrung out of the process to make it more robust, and there arestrategies and methods for this that could be applied to thesebiologic-type inputs which are a level of abstraction beyond what isneeded. This down-shift in complexity can, itself, be trained into thesystem as they recognize what myriad of “soft” triggers (e.g. imagerecognition) can be turned into “hard” triggers.

Using systems like Fp1 (involved in judgment and decision making), P3(involved in tactical navigation), O1 (involved in visual engineering),Fp2 (involved in process management), F8 (involved in belief systems),and T4 (involved in listening and intuition), the training vectors mayindicate, in some embodiments, a system of mixed audio and visualconcepts. The system may use an expert system to monitor a set of inputsand reconfigure those inputs to monitor an asset including image feedsat various electromagnetic frequencies (such as visual light, thermal,UV, and the like), and audio feeds from those frequencies to determineuse, sounds of use, and possible sounds of concerns. When examplesinclude fixed assets (those that cannot move), ambient measurement ofthe environment may be measured along with signatures of use or non-useof the product such as lack of motion, thermal imprints, or lackthereof. The changing environment in the room and/or the contact withasset by user or other fixtures can cause reconfiguration of the sensorslooking to appreciate the space. When fixed in a room, such systems maydetermine that ambient conditions, such as strong outside lighting (toorich of UV content) relative to more appropriate lighting, could bedetrimental to the asset. Also included is sensing the motion of use. Inmore moveable assets, detection and parsing of benign motion rather thanmotion that may have a higher propensity to age or damage an asset canbe recorded and characterized as an aggregated feed.

Risk management (Combination of F3 (analytic deduction) and Fp1(judgment and decision making)), analytics, and decision making in thehuman brain are informed by experience and knowledge (which may bepartial, limited, negative, positive, factual, emotional, etc.). AI canpossibly recognize a situation using sensors, image recognition,proximity, text and conversation analysis, and the like and apply betterrisk management in decision making using stored fact-based outcomes forsimilar situations. This could be applied to enable consumers to makebetter purchasing and financial decisions. In other applications, itcould be applied to emergency response, policing actions, etc.

In embodiments, an AI solution may be configured as a companion riskmanager for a main operational AI solution, such as sharing commoninputs and resources, but focused on identifying risks, externalities,and other factors that are not required for the core process automation,but may improve governance, safety, emergency response, and otheraspects.

In embodiments, an AI solution may be configured as a companion riskmanager for a main operational AI solution, such as sharing commoninputs and resources, but focused on identifying risks, externalities,and other factors that are not required for the core process automation,but may improve governance, safety, emergency response, and otheraspects.

Thus, the platform may include a system that takes input from afunctional imaging system to configure, optionally automatically basedon matching of attributes between one or more biological systems, suchas brain systems, and one or more artificial intelligence systems, a setof artificial intelligence capabilities for a robotic process automationsystem. Selection and configuration may further comprise selection ofinputs to robotic process automation and/or artificial intelligence thatare configured at least in part based on functional imaging of the brainwhile workers undertake tasks, such as selection of visual inputs (suchas images from cameras) where vision systems of the brain are highlyactivated, selection of acoustic inputs where auditory systems of thebrain are highly activated, selection of chemical inputs (such aschemical sensors) where olfactory systems of the brain are highlyactivated, or the like. Thus, a biologically aware robotic processautomation system may be improved by having initial configuration, oriterative improvement, be guided, either automatically or underdeveloper control, by imaging-derived information collected as workersperform expert tasks that may benefit from automation.

Functional imaging may provide insight into which tasks involve serialprocessing versus parallel processing, providing insight into the typeof AI solution that may be best suited to a similar task or tasks (e.g.is it best to receive language and visual data/inputs at once (inparallel) or sequentially). For example, the system may determinewhether there is an order in which a user takes in data that mightsuggest an optimal ordering for performance. Analysis of functionalimages may enable identification of which computations tasks are mostquickly processed through visual inputs versus textual inputs (languageprocessing) and may enable improved matching of task to the bestinput/stimulus.

Functional imaging may enable determining efficiencies resulting fromthe pairing or multiple combinations of stimuli (e.g., is a task/commandmost efficiently communicated by providing multiple, diverse inputs atonce, and/or is it best to omit certain stimuli from inputs/commands).

Functional imaging may enable ranking tasks or events to perform/solvebased on the probabilistic improvement in the performance of asubsequent task (where task could be a computation or an actual actionperformed by a device based on a data/stimulus input).

Functional imaging may enable measuring negative impacts onperformance/computation based on “noise,” where noise may be unneededdata, irrelevant data, or overwhelming data sizes, similar todetermining “negative stimuli” (in the human context this could beambient noise in distinguishing a human voice within a cascade ofauditory inputs, or ambient lighting in image recognition, or movementin counting objects in a region and so forth).

As one example among many possible, a marketplace host may be shown touse prediction region T6 and judgment and decision making region Fp1when configuring a new marketplace, such as to predict favorablemarketplace configuration parameters (such as to optimize marketplaceefficiency profitability, and/or fairness) and to generate decisionsrelated to marketplace parameters, and a neural network may beconfigured with a neural network to provide effective replication ofprediction and a neural network to replicate decision making. Themarketplace configuration parameters may include, but are not limitedto, assets, asset types, description of assets, method for verificationof ownership, method for delivery of traded goods, estimated size ofmarketplace, methods for advertising the marketplace, methods forcontrolling the marketplace, regulatory constraints, data sources,insider trading detection techniques, liquidity requirements, accessrequirements (such as whether to implement dealer-to-dealer trading,dealer-to-customer trading, or customer-to-customer trading), anonymity(such as determining whether counterparty identities are disclosed),continuity of order handling (e.g., continuous or periodic orderhandling), interaction (e.g., bilateral or multilateral), pricediscovery, pricing drivers (e.g., order-driven pricing or quote-drivenpricing), price formation (e.g., centralized price formation orfragmented price formation), custodial requirements, types of ordersallowed (such as limit orders, stop orders, market orders, andoff-market orders), supported market types (such as dealer markets,auction markets, absolute auction markets, minimum bid auction markets,reverse auction markets, sealed bid auction markets, Dutch auctionmarkets, multi-step auction markets (e.g., two-step, three-step, n-step,etc.), forward markets, futures markets, secondary markets, derivativesmarkets, contingent markets, markets for aggregates (e.g., mutualfunds), and the like), trading rules (e.g., tick size, trading halts,open/close hours, escrow requirements, liquidity requirements,geographic rules, jurisdictional rules, rules on publicity, insidertrading prohibitions, conflict of interest rules, timing rules (e.g.,involving spot-market trading, futures trading and the like) and manyothers), asset listing requirements (e.g., financial reportingrequirements, auditing requirements, minimum capital requirements),deposit minimums, trading minimums, verification rules, commissionrules, fee rules, marketplace lifetime rules (e.g., short-termmarketplace with timing constraints vs. long-term marketplace), datastorage and use rules, and transparency (e.g., the amount and extent ofinformation disseminated).

In embodiments, the configuration and/or orchestration of a newmarketplace may involve the combination of existing marketplaces and/orthe combination of products and/or services. For example, a marketplacefor lodging (e.g., homestays for vacation rentals) and a marketplace forpersonal chef services may be combined to provide a marketplace forlodging with personal chef services (e.g., a personal chef at thevacation rental).

An RPA system may use AI systems related to biological brain functionsF3 (involved in analytic deduction) and 01 (involved in visualengineering) in conjunction with one another to perform tasks related tovisual calculus. The tasks related to visual calculus may include, forexample, processing image sensor data via the O1 visual engineeringsystem to determine what the RPA system “sees,” and how to interpret,classify, identify, etc. what is “seen.” Then, the F3 analytic deductionsystem may perform 1) deductions to determine what has led to thecurrent state of what is “seen,” and 2) prediction to determine a futurestate of what is “seen” based on the current state of visual data. TheRPA system may use the T6 prediction function to assist in performingsuch predictions. The deductions may be useful in determining a cause ofan issue, inefficiency, or problem in a system being analyzed. Thepredictions may be useful in determining solutions to problems and/orpotential efficiency improvements. The AI system using F3, O1, and/or T6may then also be used to choose a machine learned model suitable forperforming the problem solving and/or efficiency improvement. Forexample, in a manufacturing environment, the RPA system and AI systemmay intake data from a plurality of visual IoT sensors, the visual databeing from one or more sites on the manufacturing floor. The O1 visualengineering system may determine and/or classify what the visual data isseeing, such as one or more machines, products, assembly lines, etc. TheF3 analytic deduction system may determine whether one or more of themachines, products, assembly lines, etc. are indicative of issues orinefficiencies. The T6 system may then make predictions and forward thepredictions to a suitable machine learned model for determiningsolutions to problems and/or improvements to efficiencies.

Referring to FIG. 50 , in embodiments, devices 4952 may be connecteddevices that connect (such as through any of the wide range ofapplication programming interfaces 3316) to a set of Internet of Things(IoT) data collection services 4908, which may be part of or integratedwith the data collection systems 3318 and monitoring system layers 3306of the platform 4800. The application programming interfaces 3316 mayinclude network interfaces, APIs, SDKs, ports, brokers, connectors,gateways, cellular network facilities, data integration interfaces, datamigration systems, cloud computing interfaces (including ones thatinclude computational capabilities, such as AWS IoT Greengrass™, Amazon™Lambda™ and similar systems), and others. For example, the IoT datacollection services 4908 may be configured to take data from a set ofedge data collection devices in the Internet of Things, such aslow-power sensor devices (e.g., for sensing movement of entities, forsensing, temperatures, pressures or other attributes about entities 3330or their environments, or the like), cameras that capture still or videoimages of entities 3330, more fully enabled edge devices (such asRaspberry Pi™ or other computing devices, Unix™ devices, and devicesrunning embedded systems, such as including microcontrollers, FPGAs,ASICs and the like), and many others. The IoT data collection services4908 may, in embodiments, collect data about collateral 4802 or assets4918, such as, for example, regarding the location, condition (health,physical, or otherwise), quality, security, possession, or the like. Forexample, an item of personal property, such as a gemstone, vehicle, itemof artwork, or the like, may be monitored by a motion sensor and/or acamera having a known location (or having a location confirmed by GPS orother location system), to ensure that it remains in a safe, designatedlocation. The camera can provide evidence that the item remains inundamaged condition and in the possession of a party 4910, such as toindicate that it remains appropriate and adequate collateral 4802 for aloan. In embodiments, this may include items of collateral formicroloans, such as clothing, collectibles, and other items.

In embodiments, the lending platform 4800 has a set of data-integratedmicroservices including data collection systems 3318, monitoringservices 3306, blockchain services 3422, and smart contract services3431 for handling lending entities and transactions. The smart contractservices 3431 may take data from the data collection systems 3318 andmonitoring system layers 3306 (such as from IoT devices) andautomatically execute a set of rules or conditions that embody the smartcontract based on the collected data. For example, upon recognition thatcollateral 4802 for a loan has been damaged (such as evidenced by acamera or sensor), the smart contract services 3431 may automaticallyinitiate a demand for payment of a loan, automatically initiate aforeclosure process, automatically initiate an action to claimsubstitute or backup collateral, automatically initiate an inspectionprocess, automatically change a payment or interest rate term that isbased on the collateral (such as setting an interest rate at a level foran unsecured loan, rather than a secured loan), or the like. Smartcontract events may be recorded on a blockchain by the blockchainservices 3422, such as in a distributed ledger. Automated monitoring ofcollateral 4802 and assets 4918 and handling of loans via smart contractservices 3431 may facilitate lending to a much wider range of parties4910 and undertaking of loans based on a much wider range of collateral4802 and assets 4918 than for conventional loans, as lenders may havegreater certainty as to the condition of collateral. Monitoring systemlayers 3306 and data collection systems 3318 may also monitor andcollect data from external marketplaces 3390 or for marketplacesoperated with the platform 4800 to maintain awareness of the value ofcollateral 4802 and assets 4918, such as to ensure that items remain ofadequate value and liquidity to assure repayment of a loan. For example,public e-commerce auction sites like eBay™ can be monitored to confirmthat personal property items are of a type and condition likely to bedisposed of easily by a lender in a liquid public market, so that thelender is sure to receive payment if the borrower defaults. This mayallow loans to be made and administered on a wide range of personalproperty that is normally difficult to use as collateral. Inembodiments, an automated foreclosure process may be initiated by asmart contract, which may, upon occurrence of a condition of defaultthat permits foreclosure (such as uncured failure to make payments)include a process for automatically initiating placement of an item ofcollateral on a public auction site (such as eBay™ or an auction siteappropriate for a particular type of property), automatically securingcollateral (such as by locking a connected device, such as a smart lock,smart container, or the like that contains or secures collateral),automatically configuring a set of instructions to a carrier, freightforwarder, or the like for shipping collateral, automaticallyconfiguring a set of instructions for a drone, a robot, or the like fortransporting collateral, or the like. In embodiments, a system isprovided for facilitating foreclosure on collateral. An example systemfor facilitating foreclosure on collateral may include a set of datacollection and monitoring services for monitoring at least one conditionof a lending agreement; and a set of smart contract servicesestablishing terms and conditions of the lending agreement that includeterms and conditions for foreclosure on at least one item that providescollateral securing a repayment obligation of the lending agreement,wherein upon detection of a default based on data collected by the datacollection and monitoring services, the set of smart contract servicesautomatically initiates a foreclosure process on the collateral. Certainfurther aspects of an example system are described following, any one ormore of which may be present in certain embodiments. An example systemincludes where the set of smart contract services initiates a signal toat least one of a smart lock and a smart container to lock thecollateral. An example system includes where the set of smart contractservices configures and initiates a listing of the collateral on apublic auction site. An example system includes where the set of smartcontract services configures and delivers a set of transportinstructions for the collateral. An example system includes where theset of smart contract services configures a set of instructions for adrone to transport the collateral. An example system includes where theset of smart contract services configures a set of instructions for arobot to transport the collateral. An example system includes where theset of smart contract services initiates a process for automaticallysubstituting a set of substitute collateral. An example system includeswhere the set of smart contract services initiates a message to aborrower initiating a negotiation regarding the foreclosure. An examplesystem includes where the negotiation is managed by a robotic processautomation system that is trained on a training set of foreclosurenegotiations. An example system includes where the negotiation relatesto modification of at least one of the interest rate, the payment terms,and the collateral for the lending transaction.

Referring to FIG. 51 , in embodiments, the lending platform 4800 isprovided having an Internet of Things data collection platform 4908(with various IoT and edge devices as described throughout thisdisclosure) for monitoring at least one of a set of assets 4918 and aset of collateral 4802 for a loan, a bond, or a debt transaction. Theplatform 4800 may include a guarantee and/or security monitoringsolution 4930 for monitoring assets 4918 and/or collateral 4802 based onthe data collected by the IoT data collection platform 4908, such aswhere the guarantee and/or security monitoring solution 4930 usesvarious adaptive intelligent systems layers 3304, such as ones that mayuse models (which may be adjusted, reinforced, trained, or the like,such as using artificial intelligence 3448) that determine the conditionor value of items based on images, sensor data, location data, or otherdata of the type collected by the IoT data collection platform 4908.Monitoring may include monitoring of location of collateral 4802 orassets 4918, behavior of parties 4910, financial condition of parties4910, or the like. The guarantee and/or security monitoring solution4930 may include a set of interfaces by which a user may configureparameters for monitoring, such as rules or thresholds regardingconditions, behaviors, attributes, financial values, locations, or thelike, in order to obtain alerts regarding collateral 4802 or assets4918. For example, a user may set a rule that collateral must remain ina given jurisdiction, a threshold value of the collateral as apercentage of a loan balance, a minimum status condition (e.g., freedomfrom damage or defects), or the like. Configured parameters may be usedto provide alerts to personnel responsible for monitoring loancompliance and/or used or embodied into one or more smart contracts thatmay take input from the interface of the guarantee and/or securitymonitoring solution 4930 to configure conditions for foreclosure,conditions for changing interest rates, conditions for acceleratingpayments, or the like. The platform 4800 may have a loan managementsolution 4948 that allows a loan manager to access information from theIoT data collection system 4908 and/or the guarantee and/or securitymonitoring solution 4930, such that a user may manage various actionswith respect to a loan (of the many types described herein, such assetting interest rates, foreclosing, sending notices, and the like)based on the condition of collateral 4802 or assets 4918, based onevents involving entities 3330, based on behaviors, based onloan-related actions (such as payments), and other factors. The loanmanagement solution 4948 may include a set of interfaces, workflows,models (including adaptive intelligent systems layers 3304) that areconfigured for a particular type of loan (of the many types describedherein) and that allow a user to configure parameters, set rules, setthresholds, design workflows, configure smart contract services,configure blockchain services, and the like in order to facilitateautomated or assisted management of a loan, such as enabling automatedhanding of loan actions by a smart contract in response to collecteddata from the IoT data collection system 4908 or enabling generation ofa set of recommended actions for a human user based on that data.

In embodiments, a lending platform is provided having a smart contractand distributed ledger platform for managing at least one of ownershipof a set of collateral and a set of events related to a set ofcollateral. A set of smart contract services 3431 may, for example,transfer ownership of the collateral 4802 or other assets 4918 uponrecognition of an event of failure to make payment or other default,occurrence of a foreclosure condition (such as failure to satisfy with acovenant or failure to comply with an obligation), or the like, wherethe ownership transfer and related events are recorded by the set ofblockchain services 3422 in a distributed ledger, such as one thatprovides a secure record of title to the assets 4918 or collateral 4802.As an example, a covenant of a loan embodied in a smart contract mayrequire that collateral 4802 have a value that exceeds a minimumfraction (or multiple) of the remaining balance of a loan. Based on datacollected about the value of collateral (such as by monitoring one ormore external marketplaces 3390 or marketplaces of the platform 4800), asmart contract may calculate whether the covenant is satisfied andrecord the outcome on a blockchain. If the covenant is not satisfied,such as if market factors indicate that the type of collateral hasdiminished, while the loan balance remains high, the smart contract mayinitiate a foreclosure, including recording an ownership transfer on adistributed ledger via the blockchain services 3422. A smart contractmay also process events related to an entity 3330 such as a party 4910.For example, a covenant of a loan may require the party to maintain alevel of debt below a threshold or ratio, to maintain a level of income,to maintain a level of profit, or the like. The monitoring system layers3306 or data collection systems 3318 may provide data used by the smartcontract services 3431 to determine covenant compliance and to enableautomated action, including recording events like foreclosure andownership transfers on a distributed ledger. In another example, acovenant may relate to a behavior of a party 4910 or a legal status of aparty 4910, such as requiring the party to refrain from taking aparticular action with respect to an item of property. For example, acovenant may require a party to comply with zoning regulations thatprohibit certain usage of real property. IoT data collection systems4908 may be used to monitor the party 4910, the property, or other itemsto confirm compliance with the covenant or to trigger alerts orautomated actions in cases of non-compliance.

Referring to FIG. 52 , in embodiments, a lending platform is providedhaving a crowdsourcing system for obtaining information about at leastone of a state of a set of collateral for a loan and a state of anentity relevant to a guarantee for a loan. Thus, in embodiments, aplatform is provided herein, with systems, methods, processes, services,components, and other elements for enabling a blockchain and smartcontract platform 5200 for crowdsourcing information relevant tolending. As with other embodiments described above in connection withsourcing innovation, product demand, or the like, a blockchain 3422,such as optionally embodying a distributed ledger, may be configuredwith a set of smart contracts 3431 to administer a reward 5212 for thesubmission of loan information 4418, such as evidence of ownership ofproperty, evidence of title, information about ownership of collateral,information about condition of collateral, information about thelocation of collateral, information about a party's identity,information about a party's creditworthiness, information about aparty's activities or behavior, information about a party's businesspractices, information about the status of performance of a contract,information about accounts receivable, information about accountspayable, information about the value of collateral, and many other typesof information. In embodiments, a blockchain 3422, such as optionallydistributed in a distributed ledger, may be used to configure a requestfor information 17862, along with terms and conditions 5210 related tothe information, such as a reward 5212 for submission of the information4418, a set of terms and conditions 5210 related to the use of theinformation 17862), and various parameters 5208, such as timingparameters, the nature of the information required (such asindependently validated information like title records, video footage,photographs, witnessed statements, or the like), and other parameters5208.

The blockchain and smart contract platform 5200 may include acrowdsourcing interface 5220, which may be included in or provided incoordination with a website, application, dashboard, communicationssystem (such as for sending emails, texts, voice messages,advertisements, broadcast messages, or other messages), by which amessage may be presented in the interface 5220 or sent to relevantindividuals (whether targeted, such as in the case of a request to aparticular individual, or broadcast, such as to individuals in a givenlocation, company, organization, or the like) with an appropriate linkto the smart contract 3431 and associated blockchain 3422, such that areply message submitting information 4418, with relevant attachments,links, or other information, can be automatically associated (such asvia an API or data integration system) with the blockchain 3422, suchthat the blockchain 3422, and any optionally associated distributedledger, maintains a secure, definitive record of information 17862submitted in response to the request. Where a reward 5212 is offered,the blockchain 3422 and/or smart contract 3431 may be used to recordtime of submission, the nature of the submission, and the partysubmitting, such that at such time as a submission satisfies theconditions for a reward 5212 (such as, for example, upon completion of aloan transaction in which the information 17862 was useful), theblockchain 3422 and any distributed ledger stored thereby can be used toidentify the submitter and, by execution of the smart contract 3431,convey the reward 5212 (which may take any of the forms of considerationnoted throughout this disclosure). In embodiments, the blockchain 3422and any associated ledger may include identifying information forsubmissions of information 4418 without containing actual information17862, such that information may be maintained secret (such as beingencrypted or being stored separately with only identifying information),subject to satisfying or verifying conditions for access (such asidentification or verification of a person who has legitimate accessrights, such as by an identity or security application 3418). Rewards5212 may be provided based on outcomes of cases or situations to whichinformation 17862 relates, based on a set of rules (which may beautomatically applied in some cases, such as using a smart contract 3431in concert with an automation system, a rule processing system, anartificial intelligence system 3448 or other expert system, which inembodiments may comprise one that is trained on a training data setcreated with human experts). For example, a machine vision system may beused to evaluate evidence of the existence and/or condition ofcollateral based on images of items, and parties submitting informationabout collateral may be rewarded, such as via tokens or otherconsideration, via distribution of rewards 5212 through the smartcontract 3431, blockchain 3422 and any distributed ledger. Thus, theblockchain and smart contract platform 5200 may be used for a widevariety of fact-gathering and information-gathering purposes, tofacilitate validation of collateral, to validate representations aboutbehavior, to validate occurrence of conditions of compliance, tovalidate occurrence of conditions of default, to deter improper behavioror misrepresentations, to reduce uncertainty, to reduce asymmetries ofinformation, or the like.

In embodiments, information may relate to fact-gathering ordata-gathering for a variety of applications and solutions that may besupported by a marketplace platform 3300, including the crowdsourcingplatform 5220, such as for underwriting 3420 (e.g., of various types ofloans, guarantees, and other items); risk management solutions 3408(such as managing a wide variety of risks noted throughout thisdisclosure, such as risks associated with individual loans, packages ofloans, tranches of loans and the like); lending solutions 3410 (such asevidence of the ownership and or value of collateral, evidence of theveracity of representations, evidence of performance or compliance withloan covenants, and the like); regulatory solutions 3426 (such as withrespect to compliance with a wide range of regulations that may governentities 3330 and processes, behaviors or activities of or by entities3330); and fraud prevention solutions 3416 (such as to detect fraud,misrepresentation, improper behavior, libel, slander, and the like). Forexample, a capital loan for a building may include a covenant regardingthe use of the property, such as permitting certain uses and prohibitingothers, permitting a given occupancy, or the like, and the crowdsourcingplatform 5220 may solicit and provide consideration for complianceinformation about the building (e.g., requesting confirmation from thecrowd that a building is in fact being used for its intended use aspermitted by zone regulations). Crowdsourced information may be combinedwith information from monitoring systems 3306. In embodiments, anadaptive intelligent systems layers 3304 may, for example, continuouslymonitor a property, an item of collateral 4802, or other entity 3330and, upon recognition (such as by an AI system, such as a neural networkclassifier) of a suspicious event (e.g., one that may indicate violationof a loan covenant), the adaptive intelligent systems layers 3304 mayprovide a signal to the crowdsourcing system 5220 indicating that acrowdsourcing process should be initiated to verify the presence orabsence of the violation. In embodiments, this may include classifyingthe covenant-related condition that using a machine classifier,providing the classification along with identifying data about anentity, and automatically configuring, such as based on a model or setof rules, a crowdsource request that identifies what information isrequested about what entity 3330 and what reward 5212 is provided. Inembodiment, rewards 5212 may be configured by experts, rewards 5212 maybe based on a set of rules (such as ones that operate on parameters ofthe loan, the terms and conditions of a covenant in a smart contract3431 (such as loan value, remaining term, and the like), the value ofcollateral 4802, or the like), and/or rewards 5212 may be set by roboticprocess automation 3442, such as where an RPA system 3442 is trained ona training set of expert activities in setting rewards in variouscontexts that collectively show what rewards are appropriate in givensituations. Robotic process automation 3442 of reward configuration maybe continuously improved by artificial intelligence 3448, such as basedon a continuous feedback of outcomes of crowdsourcing, such as outcomesof success (e.g., verification of covenant defaults, yield outcomes, andthe like).

Information gathering may include information gathering with respect toentities 3330 and their identities, assertions, claims, actions, orbehaviors, among many other factors, and may be accomplished bycrowdsourcing in the platform 5220 or by data collection systems 3318and monitoring systems 3306, optionally with automation via processautomation 3442 and adaptive intelligence, such as using an artificialintelligence system 3448.

Referring to FIG. 53 , a platform-operated marketplace crowdsourcingevidence 5220 may be configured, such as in a crowdsourcing interface5220 or other user interface for an operator of the platform-operatedmarketplace 5201, using the various enabling capabilities of the datahandling transactional, financial, and marketplace enablement system3300 described throughout this disclosure. The operator may use the userinterface or crowdsourcing dashboard 5414 to undertake a series of stepsto perform or undertake an algorithm to create a crowdsourcing requestfor information 17862 as described in connection with FIG. 52 . Inembodiments, one or more of the steps of the algorithm to create areward 5212 within the dashboard 5414 may include, at a component 5302,identifying potential rewards 5312, such as what information 5318 islikely to be of value in a given situation (such as may be indicatedthrough various communication channels by stakeholders orrepresentatives of an entity, such as an individual or enterprise, suchas attorneys, agents, investigators, parties, auditors, detectives,underwriters, inspectors, and many others).

The dashboard 5414 may be configured with a crowdsourcing interface5220, such as with elements (including application programming elements,data integration elements, messaging elements, and the like) that allowa crowdsourcing request to be managed in the platform marketplace 5201and/or in one or more external marketplaces 5204. In the dashboard 5414,at a component 5304 the user may configure one or more parameters 5208or conditions 5210, such as comprising or describing the conditions (ofthe type described herein) for the crowdsourcing request, such as bydefining a set of conditions 5210 that trigger the reward 5212 anddetermine allocation of the reward 5212 to a set of submitters ofinformation 5218. The user interface of the dashboard 5414, which mayinclude or be associated with the crowdsourcing interface 5220, mayinclude a set of drop down menus, tables, forms, or the like withdefault, templated, recommended, or pre-configured conditions,parameters 5208, conditions 5210 and the like, such as ones that areappropriate for various types of crowdsourcing requests. Once theconditions and other parameters of the request are configured, at acomponent 5308, a smart contract 3431 and blockchain 3422 may beconfigured to maintain, such as via a ledger, the data required toprovision, allocate, and exchange data related to the request and tosubmissions of information 5218. The smart contract 3431 and blockchain3422 may be configured to identity information, transaction information(such as for exchanges of information), technical information, otherevidence data 518 of the type described in connection with FIG. 52 ,including any data, testimony, photo or video content or otherinformation that may be relevant to a submission of information 5218 orthe conditions 5210 for a reward 5212. At a component 5310, a smartcontract 3431 may be configured to embody the conditions 5210 that wereconfigured at the component 5304 and to operate on the blockchain 3422that was created at the component 5308, as well as to operate on otherdata, such as data indicating facts, conditions, events, or the like inthe platform-operated marketplace 5201 and/or an external marketplace5204 or other information site or resource, such as ones related tosubmission data 4418, such as sites indicating outcomes of legal casesor portions of cases, sites reporting on investigations, and the like.The smart contract 3431 may be responsive to the configuration fromcomponent 5310 to apply one or more rules, execute one or moreconditional operations, or the like upon data, such as evidence data5218 and data indicating satisfaction of parameters 5208 or conditions5210, as well as identity data, transactional data, timing data, andother data. Once configuration of one or more blockchains 3422 and oneor more smart contracts 3431 is complete, at a component 5312, theblockchain 3422 and smart contract 3431 may be deployed in theplatform-operated marketplace 5201, external marketplace 5204, or othersite or environment, such as for interaction by one or more submittersor other users, who may, such as in a crowdsourcing interface 5220, suchas a website, application, or the like, enter into the smart contract3431, such as by submitting a submission of information 4418 andrequesting the reward 5212, at which point the platform 5200, such asusing the adaptive intelligent systems layers 3304 or othercapabilities, may store relevant data, such as submission data 4418,identity data for the party or parties entering the smart contract 3431on the blockchain 3422, or otherwise on the platform-operatedmarketplace 5201. At a component 5314, once the smart contract 3431 isexecuted, the platform 5201 may monitor, such as by the monitoringsystems layer 3306, the platform-operated marketplace 5201 and/or one ormore external marketplaces 5204 or other sites for submission data 4418,event data 3324, or other data that may satisfy or indicate satisfactionof one or more conditions 5210 or trigger application of one or morerules of the smart contract 3431, such as to trigger a reward 5212.

At a component 5316, upon satisfaction of conditions 5210, smartcontracts 3431 may be settled, executed, or the like, resulting updatesor other operations on the blockchain 3422, such as by transferringconsideration (such as via a payments system) and transferring access toinformation 17862. Thus, via the above-referenced steps, an operator ofthe platform-operated marketplace 5201 may discover, configure, deploy,and have executed a set of smart contracts 3431 that crowdsourceinformation relevant to a loan (such as information about value orcondition of collateral 4802, compliance with covenants, fraud ormisrepresentation, and the like) and that are cryptographically securedand transferred on a blockchain 3422 from information gatherers toparties seeking information. In embodiments, the adaptive intelligentsystems layers 3304 may be used to monitor the steps of the algorithmdescribed above, and one or more artificial intelligence systems may beused to automate, such as by robotic process automation 3442, the entireprocess or one or more sub-steps or sub-algorithms. This may occur asdescribed above, such as by having an artificial intelligence system3448 learn on a training set of data resulting from observations, suchas monitoring software interactions of human users as they undertake theabove-referenced steps. Once trained, the adaptive intelligent systemslayers 3304 may thus enable the transactional, financial and marketplaceenablement system 3300 to provide a fully automated platform forcrowdsourcing of loan information.

Referring to FIG. 54 , in embodiments, a lending platform is providedhaving a smart contract system 3431 that automatically adjusts aninterest rate for a loan based on information collected via at least oneof an Internet of Things system, a crowdsourcing system, a set of socialnetwork analytic services and a set of data collection and monitoringservices. The platform 4800 may include an interest rate automationsolution 4924 that may include a set of interfaces, workflows, andmodels (which may include, use or be enabled by various adaptiveintelligent systems layers 3304) and other components that areconfigured to enable automation of the setting of interest rates basedon a set of conditions, which may include smart contract 3431 terms andconditions, marketplace conditions (of platform marketplaces and/orexternal marketplaces 3390), conditions monitored by monitoring systemlayers 3306 and data collection systems 3318, and the like (such as ofentities 3330, including without limitation parties 4910, collateral4802 and assets 4918, among others). For example, a user of the interestrate automation solution 4924 may set (such as in a user interface)rules, thresholds, model parameters, and the like that determine, orrecommend, an interest rate for a loan based on the above, such as basedon interest rates available to the lender from secondary lenders, riskfactors of the borrower (including predicted risk based on one or morepredictive models using artificial intelligence 3448), or the system mayautomatically recommend or set such rules, thresholds, parameters andthe like (optionally by learning to do so based on a training set ofoutcomes over time). Interest rates may be determined based on marketingfactors (such as competing interest rates offered by other lenders).Interest rates may be calculated for new loans, for modifications ofexisting loans, for refinancing, for foreclosure situations (e.g.,changing from secured loan rates to unsecured loan rates), and the like.

Smart Contract that Automatically Restructures Debt Based on a MonitoredCondition

Referring to FIG. 55 , in embodiments, a lending platform is providedhaving a smart contract that automatically restructures debt based on amonitored condition. The platform 4800 may include a debt restructuringsolution 4928 that may include a set of interfaces, workflows, andmodels (which may include, use or be enabled by various adaptiveintelligent systems layers 3304) and other components that areconfigured to enable automation of the restructuring of debt based on aset of conditions, which may include smart contract 3431 terms andconditions, marketplace conditions (of platform marketplaces and/orexternal marketplaces 3390, conditions monitored by monitoring systemlayers 3306 and data collection systems 3318, and the like (such as ofentities 3330, including without limitation parties 4910, collateral4802 and assets 4918, among others)). For example, a user of the debtrestructuring solution 4928 may create, configure (such as using one ormore templates or libraries), modify, set or otherwise handle (such asin a user interface of the debt restructuring solution 4928) variousrules, thresholds, procedures, workflows, model parameters, and the likethat determine, or recommend, a debt restructuring action for a loanbased on one or more events, conditions, states, actions, or the like,where restructuring may be based on various factors, such as prevailingmarket interest rates, interest rates available to the lender fromsecondary lenders, risk factors of the borrower (including predictedrisk based on one or more predictive models using artificialintelligence 3448), status of other debt (such as new debt of aborrower, elimination of debt of a borrower, or the like), condition ofcollateral 4802 or assets 4918 used to secure or back a loan, state of abusiness or business operation (e.g., receivables, payables, or thelike), and many others. Restructuring may include changes in interestrate, changes in priority of secured parties, changes in collateral 4802or assets 4918 used to back or secure debt, changes in parties, changesin guarantors, changes in payment schedule, changes in principal balance(e.g., including forgiveness or acceleration of payments), and others.In embodiments, the debt restructuring solution 4928 may automaticallyrecommend or set such rules, thresholds, actions, parameters and thelike (optionally by learning to do so based on a training set ofoutcomes over time), resulting in a recommended restructuring plan,which may specify a series of actions required to accomplish arecommended restructuring, which may be automated and may involveconditional execution of steps based on monitored conditions and/orsmart contract terms, which may be created, configured, and/or accountedfor by the debt restructuring plan.

Restructuring plans may be determined and executed based at least onepart on market factors (such as competing interest rates offered byother lenders, values of collateral, and the like), as well asregulatory and/or compliance factors. Restructuring plans may begenerated and/or executed for modifications of existing loans, forrefinancing, for foreclosure situations (e.g., changing from securedloan rates to unsecured loan rates), for bankruptcy or insolvencysituations, for situations involving market changes (e.g., changes inprevailing interest rates) and others. In embodiments, adaptiveintelligent systems layers 3304, including artificial intelligence 3448,may be trained on a training set of restructuring activities by expertsand/or on outcomes of restructuring actions to generate a set ofpredictions, classifications, control instructions, plans, models, orthe like for automated creation, management and/or execution of one ormore aspects of a restructuring plan. In embodiments, provided herein isa smart contract system for modifying a loan, the system having a set ofcomputational services. An example platform or system includes (a) a setof data collection and monitoring services for monitoring a set ofentities involved in a loan; and (b) a set of smart contract servicesfor managing a smart lending contract, wherein the set of smart contractservices processes information from the set of data collection andmonitoring services and automatically restructures debt based on amonitored condition. Certain further aspects of an example system aredescribed following, any one or more of which may be present in certainembodiments.

Referring to FIG. 56 , in embodiments, a lending platform 4800 isprovided having a social network analytics solution 4904 for validatingthe reliability of a guarantee for a loan. The platform 4800 may includea guarantee and/or security monitoring solution 4930 that may include aset of interfaces, workflows, and models (which may include, use or beenabled by various adaptive intelligent systems layers 3304) and othercomponents that are configured to enable monitoring of a guaranteeand/or security for a lending transaction based on a set of conditions,which may include smart contract 3431 terms and conditions, marketplaceconditions (of platform marketplaces and/or external marketplaces 3390),conditions monitored by monitoring system layers 3306 and datacollection systems 3318, and the like (such as of entities 3330,including without limitation parties 4910, collateral 4802 and assets4918, among others). For example, a user of the guarantee and/orsecurity monitoring solution 4930 may set (such as in a user interface)rules, thresholds, model parameters, and the like that determine, orrecommend, a monitoring plan for lending transaction such as based onrisk factors of the borrower, risk factors of the lender, market riskfactors, and/or risk factors of collateral 4802 or assets 4918(including predicted risk based on one or more predictive models usingartificial intelligence 3448), or the platform 4800 may automaticallyrecommend or set such rules, thresholds, parameters and the like(optionally by learning to do so based on a training set of outcomesover time). The guarantee and/or security monitoring solution 4930 mayconfigure a set of social network analytics solutions 4904 and/or othermonitoring system layers 3306 and/or data collection systems 3318 tosearch, parse, extract, and process data from one or more socialnetworks, websites, or the like, such as ones that may containinformation about collateral 4802 or assets 4918 (e.g., photos that showa vehicle, boat, or other personal property of a party 4910, photos of ahome or other real property, photos or text that describes activities ofa party 4910 (including ones that indicate financial risk, physicalrisk, health risk, or other risk that may be relevant to the quality ofthe guarantor and/or the guarantee for a payment obligation and/or theability of the borrower to repay a loan when due)). For example, a photoshowing a borrower driving a regular passenger vehicle in off-roadconditions may be flagged as indicating that the vehicle cannot be fullyrelied upon as collateral for an automobile loan that has a highremaining balance.

Social Network Monitoring System for Validating Quality of a PersonalGuarantee for a Loan

Thus, in embodiments, provided herein is a social network monitoringsystem for validating conditions of a guarantee for a loan. An exampleplatform or system includes (a) a set of social network data collectionand monitoring services by which data is collected by a set ofalgorithms that are configured to monitor social network informationabout entities involved in a loan; and (b) an interface to the set ofsocial networking services that enables configuration of parameters ofthe social network data collection and monitoring services to obtaininformation related to the condition of guarantee. Certain furtheraspects of an example system are described following, any one or more ofwhich may be present in certain embodiments.

An example system includes where the set of social network datacollection and monitoring services obtains information about thefinancial condition of an entity that is the guarantor for the loan.

An example system includes where the financial condition is determinedat least in part based on information contained in a social networkabout the entity selected from among a publicly stated valuation of theentity, a set of property owned by the entity as indicated by publicrecords, a valuation of a set of property owned by the entity, abankruptcy condition of an entity, a foreclosure status of an entity, acontractual default status of an entity, a regulatory violation statusof an entity, a criminal status of an entity, an export controls statusof an entity, an embargo status of an entity, a tariff status of anentity, a tax status of an entity, a credit report of an entity, acredit rating of an entity, a website rating of an entity, a set ofcustomer reviews for a product of an entity, a social network rating ofan entity, a set of credentials of an entity, a set of referrals of anentity, a set of testimonials for an entity, a set of behavior of anentity, a location of an entity, and a geolocation of an entity.

An example system includes where the loan is of at least one typeselected from among an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

An example system includes where the platform or system may furtherinclude an interface of the social network data collection andmonitoring services. An example system includes where the datacollection and monitoring service is configured to obtain informationabout conditions of a set of collateral for the loan, wherein the set ofcollateral items is selected from among a vehicle, a ship, a plane, abuilding, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

An example system includes where condition of collateral includescondition attributes selected from the group consisting of the qualityof the collateral, the condition of the collateral, the status of titleto the collateral, the status of possession of the collateral, thestatus of a lien on the collateral, a new or used status of item, a typeof item, a category of item, a specification of an item, a productfeature set of an item, a model of item, a brand of item, a manufacturerof item, a status of item, a context of item, a state of item, a valueof item, a storage location of item, a geolocation of item, an age ofitem, a maintenance history of item, a usage history of item, anaccident history of an item, a fault history of an item, an ownership ofan item, an ownership history of an item, a price of a type of item, avalue of a type of item, an assessment of an item, and a valuation of anitem.

An example system includes where the interface is a graphical userinterface configured to enable a workflow by which a human user entersparameters to establish the social network data collection andmonitoring request.

An example system includes where the platform or system may furtherinclude a set of smart contract services that administer a smart lendingcontract, wherein the smart contract services process information fromthe set of social network data collection and monitoring services andautomatically undertake an action related to the loan.

An example system includes where the action is at least one of aforeclosure action, a lien administration action, an interest-ratesetting action, a default initiation action, a substitution ofcollateral, and a calling of the loan.

An example system includes where the platform or system may furtherinclude a robotic process automation system that is trained, based on atraining set of interactions of human users with the interface to theset of social network data collection and monitoring services, toconfigure a data collection and monitoring action based on a set ofattributes of a loan.

An example system includes where the attributes of the loan are obtainedfrom a set of smart contract services that manage the loan.

An example system includes where the robotic process automation systemis configured to be iteratively trained and improved based on a set ofoutcomes from a set of social network data collection and monitoringrequests.

An example system includes where training includes training the roboticprocess automation system to determine a set of domains to which thesocial network data collection and monitoring services will applied.

An example system includes where training includes training the roboticprocess automation system to configure the content of a social networkdata collection and monitoring search.

IoT Data Collection and Monitoring System for Validating Quality of aPersonal Guarantee for a Loan

Referring still to FIG. 56 , in embodiments, a lending platform isprovided having an Internet of Things data collection and monitoringsystem for validating reliability of a guarantee for a loan. Theguarantee and/or security monitoring solution 4930 may include thecapability to use data from, and configure collection activities by, aset of Internet of Things services 4908 (which may include various IoTdevices, edge devices, edge computation and processing capabilities, andthe like as described in connection with various embodiments), such asones that monitor various entities 3330 and their environments involvedin lending transactions.

In embodiments, provided herein is a monitoring system for validatingconditions of a guarantee for a loan. For example, a set of algorithmsmay be configured to initiate data collection by IoT devices, to managedata collection, and the like such as based on the conditions referencedabove, including conditions that relate to risk factors of the borroweror lender, market risk factors, physical risk factors, or the like. Forexample, an IoT system may be configured to capture video or images of ahome during periods of bad weather, such as to determine whether thehome is at risk of a flood, wind damage, or the like, in order toconfirm whether the home can be predicted to serve as adequatecollateral for a home loan, a line of credit, or other lendingtransaction.

An example platform or system includes (a) a set of Internet of Thingsdata collection and monitoring services by which data is collected by aset of algorithms that are configured to monitor Internet of Thingsinformation collected from and about entities involved in a loan; and(b) an interface to the set of Internet of Things data collection andmonitoring services that enables configuration of parameters of thesocial network data collection and monitoring services to obtaininformation related to the condition of guarantee. Certain furtheraspects of an example system are described following, any one or more ofwhich may be present in certain embodiments.

An example system includes where the set of Internet of Things datacollection and monitoring services obtains information about thefinancial condition of an entity that is the guarantor for the loan.

An example system includes where the financial condition is determinedat least in part based on information collected by an Internet of Thingsdevice about the entity selected from among a publicly stated valuationof the entity, a set of property owned by the entity as indicated bypublic records, a valuation of a set of property owned by the entity, abankruptcy condition of an entity, a foreclosure status of an entity, acontractual default status of an entity, a regulatory violation statusof an entity, a criminal status of an entity, an export controls statusof an entity, an embargo status of an entity, a tariff status of anentity, a tax status of an entity, a credit report of an entity, acredit rating of an entity, a web site rating of an entity, a set ofcustomer reviews for a product of an entity, a social network rating ofan entity, a set of credentials of an entity, a set of referrals of anentity, a set of testimonials for an entity, a set of behavior of anentity, a location of an entity, and a geolocation of an entity.

An example system includes where the loan is of at least one typeselected from among an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

An example system includes where the platform or system may furtherinclude an interface of the set of Internet of Things data collectionand monitoring services. An example system includes where the set ofdata collection and monitoring services is configured to obtaininformation about conditions of a set of collateral for the loan,wherein the set of collateral items is selected from among a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

An example system includes where condition of collateral includescondition attributes selected from the group consisting of the qualityof the collateral, the condition of the collateral, the status of titleto the collateral, the status of possession of the collateral, thestatus of a lien on the collateral, a new or used status of item, a typeof item, a category of item, a specification of an item, a productfeature set of an item, a model of item, a brand of item, a manufacturerof item, a status of item, a context of item, a state of item, a valueof item, a storage location of item, a geolocation of item, an age ofitem, a maintenance history of item, a usage history of item, anaccident history of an item, a fault history of an item, an ownership ofan item, an ownership history of an item, a price of a type of item, avalue of a type of item, an assessment of an item, and a valuation of anitem.

An example system includes where the interface is a graphical userinterface configured to enable a workflow by which a human user entersparameters to establish an Internet of Things data collection andmonitoring services monitoring action.

An example system includes where the platform or system may furtherinclude a set of smart contract services that administer a smart lendingcontract, wherein the set of smart contract services process informationfrom the set of Internet of Things data collection and monitoringservices and automatically undertakes an action related to the loan.

An example system includes where the action is at least one of aforeclosure action, a lien administration action, an interest-ratesetting action, a default initiation action, a substitution ofcollateral, and a calling of the loan.

An example system includes where the platform or system may furtherinclude a robotic process automation system that is trained, based on atraining set of interactions of human users with the interface to theset of Internet of Things data collection and monitoring services, toconfigure a data collection and monitoring action based on a set ofattributes of a loan.

An example system includes where the attributes of the loan are obtainedfrom a set of smart contract services that manage the loan.

An example system includes where the robotic process automation systemis configured to be iteratively trained and improved based on a set ofoutcomes from a set of Internet of Things data collection and monitoringservices activities.

An example system includes where training includes training the roboticprocess automation system to determine a set of domains to which theInternet of Things data collection and monitoring services will applied.

An example system includes where training includes training the roboticprocess automation system to configure the content of Internet of Thingsdata collection and monitoring services activities.

Oracles

In embodiments, oracles may be deployed to collect data from specificdata sources and report the data to a distributed ledger network. Inembodiments, an oracle may be deployed to access data from off-chainsources, such as from legacy systems, IoT networks, connected products,and/or the like. In this way, an oracle may bridge off-chain ecosystemsto on-chain ecosystems. In some embodiments, oracles may be deployed toaccess data that is used by one or more smart contracts thatauto-execute actions based thereon. For example, a smart contract may beconfigured to take a specific action (e.g., auto-execute a transaction,automatically write data to a blockchain, transfer an asset from oneentity to another entity, and/or the like) upon detecting a specifiedtriggering event (e.g., as defined in the programmatic logic of thesmart contract) that is determined at least in part on off-chain data.In these examples, an oracle may collect data from one or moredesignated data sources and may process and/or structure the data, suchthat the data (or a derivation therefrom) may be reported to the smartcontract. It is appreciated that an oracle may be configured toaggregate, fuse, and/or otherwise process data from one or more datasources to obtain “oracle data”. Oracle data may refer to data that isoutput by an oracle to a distributed ledger network (e.g., a smartcontract or other software/hardware that interfaces with a distributedledger).

In embodiments, an interface configuration system may be configured toconfigure digital interfaces (e.g., application programming interfaces(APIs)) that facilitate communication between on-chain and off-chainecosystems. In embodiments, the digital interfaces may include APIs forcollecting data from one or more off-chain data sources that is providedto an on-chain ecosystem and/or providing data from the on-chainecosystem to one or more off-chain devices or systems. In some of theseembodiments, the APIs may be smart APIs that process data received byfrom one or more data sources to obtain analytics, classifications,predictions, and/or inferences from the collected data. In theseembodiments, a smart API may report the analytics, classifications,predictions and/or inferences to a designated system (e.g., distributedledger network, centralized system, and/or the like).

In some embodiments, the interface configuration system may configureoracles that collect and report on data for an on-chain ecosystem (e.g.,specific smart contract or set of smart contracts). In some of theseembodiments, the oracle may be deployed as part of a digital networkinterface (e.g., as a component of an API) and deployed to a computingnetwork (e.g., a centralized cloud service, a decentralized network ofcomputing nodes and/or the like). In some of these embodiments, theinterface configuration system may also configure programmatic logic(e.g., scripts, plug-ins, or the like) that may be installed atoff-chain data sources (e.g., centralized computing systems, IoTdevices, connected products, public or private databases, and/or thelike), whereby the programmatic logic instructs a respective off-chaindata source on what type(s) of data to collect and report, when toreport the data to the oracle, and how to report the data to the oracle.For example, the programmatic logic may indicate the type or types ofdata that are to be collected and may define a schedule and/orconditions for reporting to report one or more specific types of dataoff-chain devices

In embodiments, the interface configuration system may configure oraclesto be deployed for a particular purpose (e.g., serving specific types ofdata to a particular smart contract or for a group of smart contracts)using predefined oracle templates. In embodiments, oracle templates mayinclude a set of modules that may be implemented in furtherance of theparticular purpose. These may include modules for creating andmaintaining an API, modules for processing received data (e.g.,filtering, fusing, cleaning, or the like), and modules for reporting thedata to a consumer of the data (e.g., a smart contract). In theseembodiments, a user or an intelligence system may provide oracleparameterization data that the interface configuration system utilizesto select and configure an oracle template. For example, the oracleparameterization data may define a set of device types in a digitalproduct network that are allowed to report to the oracle, the types ofdata that the devices are to report to the oracle, data processingtechniques to be applied to the collected data, conditions that triggera reporting workflow, one or more smart contracts that receive the data,and/or the like. In response, the interface configuration module mayretrieve an oracle template from a library and may parameterize thetemplate based on the oracle parameterization data. In some of theseembodiments, the interface configuration module may further configure aset of scripts or other suitable instruction sets that may bedistributed to the oracle data sources (e.g., the set of devices in thedigital product network), whereby the script will instruct the datasources on what type of data to collect, how to structure the data(e.g., a schema for reporting the data), and how to interface with theconfigured oracle. Once configured, an oracle may be deployed to aserver, thereby exposing the API to the oracle data sources. The oraclemay then collect data from the data sources and report to the intendedrecipient (e.g., a particular smart contract).

RPA Systems

An RPA Bank Loan Negotiator Trained on a Training Set of Expert LenderInteractions with Borrowers

Referring to FIG. 57 , in embodiments, a lending platform is providedhaving a robotic process automation system 3442 for negotiation of a setof terms and conditions for a loan. The RPA system 3442 may provideautomation for one or more aspects of a negotiation solution 4932 thatenables automated negotiation and/or provides a recommendation or planfor a negotiation relevant to a lending transaction. The negotiationsolution 4932 and/or RPA system 3442 for negotiation may include a setof interfaces, workflows, and models (which may include, use, or beenabled by various adaptive intelligent systems layers 3304) and othercomponents that are configured to enable automation of one or moreaspects of a negotiation of one or more terms and conditions of alending transaction, such as based on a set of conditions, which mayinclude smart contract 3431 terms and conditions, marketplace conditions(of platform marketplaces and/or external marketplaces 3390, conditionsmonitored by monitoring system layers 3306 and data collection systems3318, and the like (such as of entities 3330, including withoutlimitation parties 4910, collateral 4802 and assets 4918, amongothers)). For example, a user of the negotiation solution 4932 maycreate, configure (such as using one or more templates or libraries),modify, set, or otherwise handle (such as in a user interface of thenegotiation solution 4932 and/or RPA system 3442) various rules,thresholds, conditional procedures, workflows, model parameters, and thelike that determine, or recommend, a negotiation action or plan for alending transaction negotiation based on one or more events, conditions,states, actions, or the like, where the negotiation plan may be based onvarious factors, such as prevailing market interest rates, interestrates available to the lender from secondary lenders, risk factors ofthe borrower, the lender, one or more guarantors, market risk factors,and the like (including predicted risk based on one or more predictivemodels using artificial intelligence 3448), status of debt, condition ofcollateral 4802 or assets 4918 used to secure or back a loan, state of abusiness or business operation (e.g., receivables, payables, or thelike), conditions of parties 4910 (such as net worth, wealth, debt,location, and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating negotiation styles), andmany others. Negotiation may include negotiation of lending transactionterms and conditions, debt restructuring, foreclosure activities,setting interest rates, changes in interest rate, changes in priority ofsecured parties, changes in collateral 4802 or assets 4918 used to backor secure debt, changes in parties, changes in guarantors, changes inpayment schedule, changes in principal balance (e.g., includingforgiveness or acceleration of payments), and many other transactions orterms and conditions. In embodiments, the negotiation solution 4932 mayautomatically recommend or set rules, thresholds, actions, parameters,and the like (optionally by learning to do so based on a training set ofoutcomes over time), resulting in a recommended negotiation plan, whichmay specify a series of actions required to accomplish a recommended ordesired outcome of negotiation (such as within a range of acceptableoutcomes), which may be automated and may involve conditional executionof steps based on monitored conditions and/or smart contract terms,which may be created, configured, and/or accounted for by thenegotiation plan. Negotiation plans may be determined and executed basedat least one part on market factors (such as competing interest ratesoffered by other lenders, values of collateral, and the like) as well asregulatory and/or compliance factors. Negotiation plans may be generatedand/or executed for creation of new loans, for creation of guaranteesand security, for secondary loans, for modifications of existing loans,for refinancing, for foreclosure situations (e.g., changing from securedloan rates to unsecured loan rates), for bankruptcy or insolvencysituations, for situations involving market changes (e.g., changes inprevailing interest rates), and others. In embodiments, adaptiveintelligent systems layers 3304, including artificial intelligence 3448,may be trained on a training set of negotiation activities by expertsand/or on outcomes of negotiation actions to generate a set ofpredictions, classifications, control instructions, plans, models, orthe like for automated creation, management and/or execution of one ormore aspects of a negotiation plan.

In embodiments, provided herein is a robotic process automation systemfor negotiating a loan. An example platform or system includes (a) a setof data collection and monitoring services for collecting a training setof interactions among entities for a set of loan transactions; (b) anartificial intelligence system that is trained on the training set ofinteractions to classify a set of loan negotiation actions; and (c) arobotic process automation system that is trained on a set of loantransaction interactions and a set of loan transaction outcomes tonegotiate the terms and conditions of a loan on behalf of a party to aloan. Certain further aspects of an example system are describedfollowing, any one or more of which may be present in certainembodiments.

An example system includes where the set of data collection andmonitoring services includes services selected from among a set ofInternet of Things systems that monitor the entities, a set of camerasthat monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about theentities, and a set of crowdsourcing services configured to solicit andreport information related to the entities.

An example system includes where the entities are a set of parties to aloan transaction.

An example system includes where the set of parties is selected fromamong a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,bond issuer, a bond purchaser, an unsecured lender, a guarantor, aprovider of security, a borrower, a debtor, an underwriter, aninspector, an assessor, an auditor, a valuation professional, agovernment official, and an accountant.

An example system includes where the artificial intelligence systemincludes at least one of a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, and a simulation system.

An example system includes where the robotic process automation istrained on a set of interactions of parties with a set of userinterfaces involved in a set of lending processes.

An example system includes where upon completion of negotiation of asmart contract for a loan is automatically configured by a set of smartcontract services based on the outcome of the negotiation.

An example system includes where at least one of an outcome and anegotiating event of the negotiation is recorded in a distributed ledgerassociated with the loan.

An example system includes where the loan is of a type selected fromamong an auto loan, an inventory loan, a capital equipment loan, a bondfor performance, a capital improvement loan, a building loan, a loanbacked by an account receivable, an invoice finance arrangement, afactoring arrangement, a pay day loan, a refund anticipation loan, astudent loan, a syndicated loan, a title loan, a home loan, a venturedebt loan, a loan of intellectual property, a loan of a contractualclaim, a working capital loan, a small business loan, a farm loan, amunicipal bond, and a subsidized loan.

An example system includes where the artificial intelligence systemincludes at least one of a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, and a simulation system.

An RPA Bank Loan Refinancing Negotiator Trained on a Training Set ofExpert Lender Re-Financing Interactions with Borrowers

In embodiments, provided herein is a robotic process automation systemfor negotiating refinancing of a loan. An example platform or systemincludes (a) a set of data collection and monitoring services forcollecting a training set of interactions between entities for a set ofloan refinancing activities; (b) an artificial intelligence system thatis trained on the training set of interactions to classify a set of loanrefinancing actions; and (c) a robotic process automation system that istrained on a set of loan refinancing interactions and a set of loanrefinancing outcomes to undertake a loan refinancing activity on behalfof a party to a loan. Certain further aspects of an example system aredescribed following, any one or more of which may be present in certainembodiments.

An example system includes where the loan refinancing activity includesinitiating an offer to refinance, initiating a request to refinance,configuring a refinancing interest rate, configuring a refinancingpayment schedule, configuring a refinancing balance, configuringcollateral for a refinancing, managing use of proceeds of a refinancing,removing or placing a lien associated with a refinancing, verifyingtitle for a refinancing, managing an inspection process, populating anapplication, negotiating terms and conditions for a refinancing, andclosing a refinancing.

An example system includes where the set of data collection andmonitoring services includes services selected from among a set ofInternet of Things systems that monitor the entities, a set of camerasthat monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about theentities, and a set of crowdsourcing services configured to solicit andreport information related to the entities.

An example system includes where the entities are a set of parties to aloan transaction.

An example system includes where the set of parties is selected fromamong a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,bond issuer, a bond purchaser, an unsecured lender, a guarantor, aprovider of security, a borrower, a debtor, an underwriter, aninspector, an assessor, an auditor, a valuation professional, agovernment official, and an accountant.

An example system includes where the artificial intelligence systemincludes at least one of a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, and a simulation system.

An example system includes where the robotic process automation istrained on a set of interactions of parties with a set of userinterfaces involved in a set of lending processes.

An example system includes where upon completion of a refinancingprocess a smart contract for a refinance loan is automaticallyconfigured by a set of smart contract services based on the outcome ofthe refinancing activity.

An example system includes where at least one of an outcome and an eventof the refinancing is recorded in a distributed ledger associated withthe refinancing loan.

An example system includes where the loan is of a type selected fromamong an auto loan, an inventory loan, a capital equipment loan, a bondfor performance, a capital improvement loan, a building loan, a loanbacked by an account receivable, an invoice finance arrangement, afactoring arrangement, a pay day loan, a refund anticipation loan, astudent loan, a syndicated loan, a title loan, a home loan, a venturedebt loan, a loan of intellectual property, a loan of a contractualclaim, a working capital loan, a small business loan, a farm loan, amunicipal bond, and a subsidized loan.

An example system includes where the artificial intelligence systemincludes at least one of a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, and a simulation system.

An RPA Bank Loan Collector Trained on a Training Set of ExpertCollection Interactions with Borrowers

Referring to FIG. 58 , in embodiments, a lending platform is providedhaving a robotic process automation system for loan collection. The RPAsystem 3442 may provide automation for one or more aspects of acollection solution 4938 that enables automated collection and/orprovides a recommendation or plan for a collection activity relevant toa lending transaction. The collection solution 4938 and/or RPA system3442 for collection may include a set of interfaces, workflows, andmodels (which may include, use or be enabled by various adaptiveintelligent systems layers 3304) and other components that areconfigured to enable automation of one or more aspects of a collectionaction of one or more terms and conditions of a collection process for alending transaction, such as based on a set of conditions, which mayinclude smart contract 3431 terms and conditions, marketplace conditions(of platform marketplaces and/or external marketplaces 3390), conditionsmonitored by monitoring system layers 3306 and data collection systems3318, and the like (such as of entities 3330, including withoutlimitation parties 4910, collateral 4802 and assets 4918, among others).For example, a user of the collection solution 4938 may create,configure (such as using one or more templates or libraries), modify,set or otherwise handle (such as in a user interface of the collectionsolution 4938 and/or RPA system 3442) various rules, thresholds,conditional procedures, workflows, model parameters, and the like thatdetermine, or recommend, a collection action or plan for a lendingtransaction or loan monitoring solution based on one or more events,conditions, states, actions, or the like, where the collection plan maybe based on various factors, such as the status of payments, the statusof the borrower, the status of collateral 4802 or assets 4918, riskfactors of the borrower, the lender, one or more guarantors, market riskfactors and the like (including predicted risk based on one or morepredictive models using artificial intelligence 3448), status of debt,condition of collateral 4802 or assets 4918 used to secure or back aloan, state of a business or business operation (e.g., receivables,payables, or the like), conditions of parties 4910 (such as net worth,wealth, debt, location, and other conditions), behaviors of parties(such as behaviors indicating preferences, behaviors indicating howborrowers respond to communication styles, communication cadence, andthe like), and many others. Collection may include collection withrespect to loans, communications to encourage payments, and the like. Inembodiments, the collection solution 4938 may automatically recommend orset rules, thresholds, actions, parameters, and the like (optionally bylearning to do so based on a training set of outcomes over time),resulting in a recommended collection plan, which may specify a seriesof actions required to accomplish a recommended or desired outcome ofcollection (such as within a range of acceptable outcomes), which may beautomated and may involve conditional execution of steps based onmonitored conditions and/or smart contract terms, which may be created,configured, and/or accounted for by the collection plan. Collectionplans may be determined and executed based at least one part on marketfactors (such as competing interest rates offered by other lenders,values of collateral, and the like), as well as regulatory and/orcompliance factors. Collection plans may be generated and/or executedfor creation of new loans, for secondary loans, for modifications ofexisting loans, for refinancing, for foreclosure situations (e.g.,changing from secured loan rates to unsecured loan rates), forbankruptcy or insolvency situations, for situations involving marketchanges (e.g., changes in prevailing interest rates), and others. Inembodiments, adaptive intelligent systems layers 3304, includingartificial intelligence 3448, may be trained on a training set ofcollection activities by experts and/or on outcomes of collectionactions to generate a set of predictions, classifications, controlinstructions, plans, models, or the like for automated creation,management and/or execution of one or more aspects of a collection plan.

In embodiments, provided herein is a robotic process automation systemfor handling collection of a loan. An example platform or systemincludes (a) a set of data collection and monitoring services forcollecting a training set of interactions among entities for a set ofloan transactions that involve collection of a set of payments for a setof loans; (b) an artificial intelligence system that is trained on thetraining set of interactions to classify a set of loan collectionactions; and (c) a robotic process automation system that is trained ona set of loan transaction interactions and a set of loan collectionoutcomes to undertake a loan collection action on behalf of a party to aloan. Certain further aspects of an example system are describedfollowing, any one or more of which may be present in certainembodiments.

An example system includes where the loan collection action undertakenby the robotic process automation system is selected from amonginitiation of a collection process, referral of a loan to an agent forcollection, configuration of a collection communication, scheduling of acollection communication, configuration of content for a collectioncommunication, configuration of an offer to settle a loan, terminationof a collection action, deferral of a collection action, configurationof an offer for an alternative payment schedule, initiation of alitigation, initiation of a foreclosure, initiation of a bankruptcyprocess, a repossession process, and placement of a lien on collateral.

An RPA Bank Loan Consolidator Trained on a Training Set of ExpertConsolidation Interactions with Other Lenders

Referring to FIG. 59 , in embodiments a lending platform is providedhaving a robotic process automation system for consolidating a set ofloans. The RPA system 3442 may provide automation for one or moreaspects of a consolidation solution 4940 that enables automatedconsolidation and/or provides a recommendation or plan for aconsolidation activity relevant to a lending transaction. Theconsolidation solution 4940 and/or RPA system 3442 for consolidation mayinclude a set of interfaces, workflows, and models (which may include,use or be enabled by various adaptive intelligent systems layers 3304)and other components that are configured to enable automation of one ormore aspects of a consolidation action or a consolidation process for alending transaction, such as based on a set of conditions, which mayinclude smart contract 3431 terms and conditions, marketplace conditions(of platform marketplaces and/or external marketplaces 3390, conditionsmonitored by monitoring system layers 3306 and data collection systems3318), and the like (such as of entities 3330, including withoutlimitation parties 4910, collateral 4802 and assets 4918, among others).For example, a user of the consolidation solution 4940 may create,configure (such as using one or more templates or libraries), modify,set or otherwise handle (such as in a user interface of theconsolidation solution 4940 and/or RPA system 3442) various rules,thresholds, conditional procedures, workflows, model parameters, and thelike that determine, or recommend, a consolidation action or plan for alending transaction or a set of loans based on one or more events,conditions, states, actions, or the like, where the consolidation planmay be based on various factors, such as the status of payments,interest rates of the set of loans, prevailing interest rates in aplatform marketplace or external marketplace, the status of theborrowers of a set of loans, the status of collateral 4802 or assets4918, risk factors of the borrower, the lender, one or more guarantors,market risk factors and the like (including predicted risk based on oneor more predictive models using artificial intelligence 3448), status ofdebt, condition of collateral 4802 or assets 4918 used to secure or backa set of loans, the state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties 4910 (such asnet worth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatingdebt preferences), and many others. Consolidation may includeconsolidation with respect to terms and conditions of sets of loans,selection of appropriate loans, configuration of payment terms forconsolidated loans, configuration of payoff plans for pre-existingloans, communications to encourage consolidation, and the like. Inembodiments, the consolidation solution 4940 may automatically recommendor set rules, thresholds, actions, parameters, and the like (optionallyby learning to do so based on a training set of outcomes over time),resulting in a recommended consolidation plan, which may specify aseries of actions required to accomplish a recommended or desiredoutcome of consolidation (such as within a range of acceptableoutcomes), which may be automated and may involve conditional executionof steps based on monitored conditions and/or smart contract terms,which may be created, configured, and/or accounted for by theconsolidation plan. Consolidation plans may be determined and executedbased at least one part on market factors (such as competing interestrates offered by other lenders, values of collateral, and the like) aswell as regulatory and/or compliance factors. Consolidation plans may begenerated and/or executed for creation of new consolidated loans, forsecondary loans related to consolidated loans, for modifications ofexisting loans related to consolidation, for refinancing terms of aconsolidated loan, for foreclosure situations (e.g., changing fromsecured loan rates to unsecured loan rates), for bankruptcy orinsolvency situations, for situations involving market changes (e.g.,changes in prevailing interest rates), and others. In embodiments,adaptive intelligent systems layers 3304, including artificialintelligence 3448, may be trained on a training set of consolidationactivities by experts and/or on outcomes of consolidation actions togenerate a set of predictions, classifications, control instructions,plans, models, or the like for automated creation, management and/orexecution of one or more aspects of a consolidation plan.

In embodiments, provided herein is a robotic process automation systemfor consolidating a set of loans. An example platform or system includes(a) a set of data collection and monitoring services for collectinginformation about a set of loans and for collecting a training set ofinteractions between entities for a set of loan consolidationtransactions: (b) an artificial intelligence system that is trained onthe training set of interactions to classify a set of loans ascandidates for consolidation; and (c) a robotic process automationsystem that is trained on a set of loan consolidation interactions tomanage consolidation of at least a subset of the set of loans on behalfof a party to the consolidation.

An example system includes where the set of data collection andmonitoring services includes services selected from among a set ofInternet of Things systems that monitor the entities, a set of camerasthat monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about theentities, and a set of crowdsourcing services configured to solicit andreport information related to the entities.

An RPA Factoring Loan Negotiator Trained on a Training Set of ExpertFactoring Interactions with Borrowers

Referring to FIG. 60 , in embodiments, a lending platform is providedhaving a robotic process automation system for managing a factoringtransaction. The RPA system 3442 may provide automation for one or moreaspects of a factoring solution 4942 that enables automated factoringand/or provides a recommendation or plan for a factoring activityrelevant to a lending transaction, such as one involving factoring ofreceivables. The factoring solution 4942 and/or RPA system 3442 forfactoring may include a set of interfaces, workflows, and models (whichmay include, use or be enabled by various adaptive intelligent systemslayers 3304) and other components that are configured to enableautomation of one or more aspects of a factoring action of one or moreterms and conditions of a factoring transaction, such as based on a setof conditions, which may include smart contract 3431 terms andconditions, marketplace conditions (of platform marketplaces and/orexternal marketplaces 3390, conditions monitored by monitoring systemlayers 3306 and data collection systems 3318, and the like (such as ofentities 3330, including without limitation parties 4910, collateral4802 and assets 4918, accounts receivable, and inventory, among others).For example, a user of the factoring solution 4942 may create, configure(such as using one or more templates or libraries), modify, set orotherwise handle (such as in a user interface of the factoring solution4942 and/or RPA system 3442) various rules, thresholds, conditionalprocedures, workflows, model parameters, and the like that determine, orrecommend, a factoring action or plan for a factoring transaction ormonitoring solution based on one or more events, conditions, states,actions, or the like, where the factoring plan may be based on variousfactors, such as the status of receivables, the status ofwork-in-progress, the status of inventory, the status of delivery and/orshipment, the status of payments, the status of the borrower, the statusof collateral 4802 or assets 4918, risk factors of the borrower, thelender, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 3448), status of debt, condition of collateral4802 or assets 4918 used to secure or back a loan, state of a businessor business operation (e.g., receivables, payables, or the like),conditions of parties 4910 (such as net worth, wealth, debt, location,and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating negotiation styles, and thelike), and many others. Factoring may include factoring with respect toloans, communications to encourage payments, and the like. Inembodiments, the factoring solution 4942 may automatically recommend orset rules, thresholds, actions, parameters, and the like (optionally bylearning to do so based on a training set of outcomes over time),resulting in a recommended factoring plan, which may specify a series ofactions required to accomplish a recommended or desired outcome offactoring (such as within a range of acceptable outcomes), which may beautomated and may involve conditional execution of steps based onmonitored conditions and/or smart contract terms, which may be created,configured, and/or accounted for by the factoring plan. Factoring plansmay be determined and executed based at least one part on market factors(such as competing interest rates or other terms and conditions offeredby other lenders, values of collateral, values of accounts receivable,interest rates, and the like), as well as regulatory and/or compliancefactors. Factoring plans may be generated and/or executed for creationof new factoring arrangements, for modifications of existing factoringarrangements, and others. In embodiments, adaptive intelligent systemslayers 3304, including artificial intelligence 3448, may be trained on atraining set of factoring activities by experts and/or on outcomes offactoring actions to generate a set of predictions, classifications,control instructions, plans, models, or the like for automated creation,management and/or execution of one or more aspects of a factoring plan.

In embodiments, provided herein is a robotic process automation systemfor consolidating a set of loans. An example platform or system includes(a) a set of data collection and monitoring services for collectinginformation about entities involved in a set of factoring loans and forcollecting a training set of interactions between entities for a set offactoring loan transactions; (b) an artificial intelligence system thatis trained on the training set of interactions to classify the entitiesinvolved in the set of factoring loans; and (c) a robotic processautomation system that is trained on the set of factoring loaninteractions to manage a factoring loan. Certain further aspects of anexample system are described following, any one or more of which may bepresent in certain embodiments.

An example system includes where the set of data collection andmonitoring services includes services selected from among a set ofInternet of Things systems that monitor the entities, a set of camerasthat monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about theentities, and a set of crowdsourcing services configured to solicit andreport information related to the entities.

An RPA Mortgage Loan Broker Trained on a Training Set of Expert BrokerInteractions with Borrowers

Referring to FIG. 61 , in embodiments, a lending platform is providedhaving a robotic process automation system for brokering a loan. Theloan may be, for example, a mortgage loan.

The RPA system 3442 may provide automation for one or more aspects of abrokering solution 4944 that enables automated brokering and/or providesa recommendation or plan for a brokering activity relevant to a lendingtransaction, such as for brokering a set of mortgage loans, home loans,lines of credit, automobile loans, construction loans, or other loans ofany of the types described herein. The brokering solution 4944 and/orRPA system 3442 for brokering may include a set of interfaces,workflows, and models (which may include, use or be enabled by variousadaptive intelligent systems layers 3304) and other components that areconfigured to enable automation of one or more aspects of a brokeringaction or a brokering process for a lending transaction, such as basedon a set of conditions, which may include smart contract 3431 terms andconditions, marketplace conditions (of platform marketplaces and/orexternal marketplaces 3390), conditions monitored by monitoring systemlayers 3306 and data collection systems 3318, and the like (such as ofentities 3330, including without limitation parties 4910, collateral4802 and assets 4918, among others, as well as of interest rates,available lenders, available terms and the like). For example, a user ofthe brokering solution 4944 may create, configure (such as using one ormore templates or libraries), modify, set or otherwise handle (such asin a user interface of the brokering solution 4944 and/or RPA system3442) various rules, thresholds, conditional procedures, workflows,model parameters, and the like that determine, or recommend, a brokeringaction or plan for brokering a set of loans of a given type or typesbased on one or more events, conditions, states, actions, or the like,where the brokering plan may be based on various factors, such as theinterest rates of the set of loans available from various primary andsecondary lenders, permitted attributes of borrowers (e.g., based onincome, wealth, location, or the like), prevailing interest rates in aplatform marketplace or external marketplace, the status of theborrowers of a set of loans, the status or other attributes ofcollateral 4802 or assets 4918, risk factors of the borrower, thelender, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 3448), status of debt, condition of collateral4802 or assets 4918 available to secure or back a set of loans, thestate of a business or business operation (e.g., receivables, payables,or the like), conditions of parties 4910 (such as net worth, wealth,debt, location, and other conditions), behaviors of parties (such asbehaviors indicating preferences, behaviors indicating debtpreferences), and many others. Brokering may include brokering withrespect to terms and conditions of sets of loans, selection ofappropriate loans, configuration of payment terms for consolidatedloans, configuration of payoff plans for pre-existing loans,communications to encourage borrowing, and the like. In embodiments, thebrokering solution 4944 may automatically recommend or set rules,thresholds, actions, parameters, and the like (optionally by learning todo so based on a training set of outcomes over time), resulting in arecommended brokering plan, which may specify a series of actionsrequired to accomplish a recommended or desired outcome of brokering(such as within a range of acceptable outcomes), which may be automatedand may involve conditional execution of steps based on monitoredconditions and/or smart contract terms, which may be created,configured, and/or accounted for by the brokering plan. Brokering plansmay be determined and executed based at least one part on market factors(such as competing interest rates offered by other lenders, propertyvalues, attributes of borrowers, values of collateral, and the like), aswell as regulatory and/or compliance factors. Brokering plans may begenerated and/or executed for creation of new loans, for secondaryloans, for modifications of existing loans, for refinancing terms, forsituations involving market changes (e.g., changes in prevailinginterest rates or property values), and others. In embodiments, adaptiveintelligent systems layers 3304, including artificial intelligence 3448,may be trained on a training set of brokering activities by expertsand/or on outcomes of brokering actions to generate a set ofpredictions, classifications, control instructions, plans, models, orthe like for automated creation, management and/or execution of one ormore aspects of a brokering plan.

In embodiments, provided herein is a robotic process automation systemfor automating brokering of a mortgage. An example platform or systemincludes (a) a set of data collection and monitoring services forcollecting information about entities involved in a set of mortgage loanactivities and for collecting a training set of interactions betweenentities for a set of mortgage loan transactions; (b) an artificialintelligence system that is trained on the training set of interactionsto classify the entities involved in the set of mortgage loans; and (c)a robotic process automation system that is trained on at least one ofthe set of mortgage loan activities and the set of mortgage loaninteractions to broker a mortgage loan. Certain further aspects of anexample system are described following, any one or more of which may bepresent in certain embodiments.

An example system includes where at least one of the set of mortgageloan activities and the set of mortgage loan interactions includesactivities among marketing activity, identification of a set ofprospective borrowers, identification of property, identification ofcollateral, qualification of borrower, title search, title verification,property assessment, property inspection, property valuation, incomeverification, borrower demographic analysis, identification of capitalproviders, determination of available interest rates, determination ofavailable payment terms and conditions, analysis of existing mortgage,comparative analysis of existing and new mortgage terms, completion ofapplication workflow, population of fields of application, preparationof mortgage agreement, completion of schedule to mortgage agreement,negotiation of mortgage terms and conditions with capital provider,negotiation of mortgage terms and conditions with borrower, transfer oftitle, placement of lien, and closing of mortgage agreement.

An example system includes where the set of data collection andmonitoring services includes services selected from among a set ofInternet of Things systems that monitor the entities, a set of camerasthat monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about theentities, and a set of crowdsourcing services configured to solicit andreport information related to the entities.

An example system includes where the artificial intelligence system usesa model that processes attributes of entities involved in the set ofmortgage loans, wherein the attributes are selected from properties thatare subject to mortgages, assets used for collateral, identity of aparty, interest rate, payment balance, payment terms, payment schedule,type of mortgage, type of property, financial condition of party,payment status, condition of property, and value of property.

An example system includes where managing a mortgage loan includesmanaging at least one of a property that is subject to a mortgage,identification of candidate mortgages from a set of borrower situations,preparation of a mortgage offer, preparation of content communicating amortgage offer, scheduling a mortgage offer, communicating a mortgageoffer, negotiating a modification of a mortgage offer, preparing amortgage agreement, executing a mortgage agreement, modifying collateralfor a set of mortgage loans, handing transfer of a lien, handling anapplication workflow, managing an inspection, managing an assessment ofa set of assets to be subject to a mortgage, setting an interest rate,deferring a payment requirement, setting a payment schedule, and closinga mortgage agreement. An example system includes where the entities area set of parties to a loan transaction. An example system includes wherethe set of parties is selected from among a primary lender, a secondarylender, a lending syndicate, a corporate lender, a government lender, abank lender, a secured lender, a bond issuer, a bond purchaser, anunsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

An example system includes where the artificial intelligence systemincludes at least one of a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, and a simulation system.

An example system includes where the robotic process automation istrained on a set of interactions of parties with a set of userinterfaces involved in a set of mortgage-related activities. An examplesystem includes where upon completion of negotiation a smart contractfor a mortgage loan is automatically configured by a set of smartcontract services based on the outcome of the negotiation. An examplesystem includes where at least one of an outcome and a negotiating eventof the negotiation is recorded in a distributed ledger associated withthe loan. An example system includes where the artificial intelligencesystem includes at least one of a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, and a simulation system.

Crowdsourcing and Automated Classification System for ValidatingCondition of an Issuer for a Bond

Referring to FIG. 62 , in embodiments, a lending platform is providedhaving a crowdsourcing and automated classification system forvalidating condition of an issuer for a bond. The RPA system 3442 mayprovide automation for one or more aspects of a bond management solution4934 that enables automated bond management and/or provides arecommendation or plan for a bond management activity relevant to a bondtransaction, such as for municipal bonds, corporate bonds, governmentbonds, or other bonds that may be backed by assets, collateral, orcommitments of a bond issuer. The bond management solution 4934 and/orRPA system 3442 for bond management may include a set of interfaces,workflows, and models (which may include, use or be enabled by variousadaptive intelligent systems layers 3304) and other components that areconfigured to enable automation of one or more aspects of a bondmanagement action or a management process for a bond transaction, suchas based on a set of conditions, which may include smart contract 3431terms and conditions, marketplace conditions (of platform marketplacesand/or external marketplaces 3390), conditions monitored by monitoringsystem layers 3306 and data collection systems 3318, and the like (suchas of entities 3330, including without limitation parties 4910,collateral 4802 and assets 4918, among others, as well as of interestrates, available lenders, available terms and the like). For example, auser of the bond management solution 4934 may create, configure (such asusing one or more templates or libraries), modify, set or otherwisehandle (such as in a user interface of the bond management solution 4934and/or RPA system 3442) various rules, thresholds, conditionalprocedures, workflows, model parameters, and the like that determine, orrecommend, a bond management action or plan for management a set ofbonds of a given type or types based on one or more events, conditions,states, actions, or the like, where the bond management plan may bebased on various factors, such as the interest rates available fromvarious primary and secondary lenders or issuers, permitted attributesof issuers and buyers (e.g., based on income, wealth, location, or thelike) prevailing interest rates in a platform marketplace or externalmarketplace, the status of the issuers of a set of bonds, the status orother attributes of collateral 4802 or assets 4918, risk factors of theissuer, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 3448), status of debt, condition of collateral4802 or assets 4918 available to secure or back a set of bonds, thestate of a business or business operation (e.g., receivables, payables,or the like), conditions of parties 4910 (such as net worth, wealth,debt, location, and other conditions), behaviors of parties (such asbehaviors indicating preferences, behaviors indicating debtpreferences), and many others. Bond management may include managementwith respect to terms and conditions of sets of bonds, selection ofappropriate bonds, communications to encourage transactions, and thelike. In embodiments, the bond management solution 4934 mayautomatically recommend or set rules, thresholds, actions, parameters,and the like (optionally by learning to do so based on a training set ofoutcomes over time), resulting in a recommended bond management plan,which may specify a series of actions required to accomplish arecommended or desired outcome of bond management (such as within arange of acceptable outcomes), which may be automated and may involveconditional execution of steps based on monitored conditions and/orsmart contract terms, which may be created, configured, and/or accountedfor by the bond management plan. Bond management plans may be determinedand executed based at least one part on market factors (such ascompeting interest rates offered by other issuers, property values,attributes of issuers, values of collateral or assets, and the like), aswell as regulatory and/or compliance factors. Bond management plans maybe generated and/or executed for creation of new bonds, for secondaryloans or transactions to back bonds, for modifications of existingbonds, for situations involving market changes (e.g., changes inprevailing interest rates or property values), and others. Inembodiments, adaptive intelligent systems layers 3304, includingartificial intelligence 3448, may be trained on a training set of bondmanagement activities by experts and/or on outcomes of bond managementactions to generate a set of predictions, classifications, controlinstructions, plans, models, or the like for automated creation,management and/or execution of one or more aspects of a bond managementplan.

Systems that Varies the Interest Rate or Other Terms on a SubsidizedLoan Based on a Parameter Monitored by the IoT

Referring to FIG. 63 , in embodiments, a lending platform is providedhaving a system that varies the terms and conditions of a loan based ona parameter monitored by the IoT. The loan may be a subsidized loan. TheRPA system 3442 may provide automation for one or more aspects of a loanmanagement solution 4948 that enables automated loan management and/orprovides a recommendation or plan for a loan management activityrelevant to a loan transaction, such as for personal loans, corporateloans, subsidized loans, student loans, or other loans, including onesthat may be backed by assets, collateral, or commitments of a borrower.The loan management solution 4948 and/or RPA system 3442 for loanmanagement may include a set of interfaces, workflows, and models (whichmay include, use or be enabled by various adaptive intelligent systemslayers 3304) and other components that are configured to enableautomation of one or more aspects of a loan management action or amanagement process for a loan transaction, such as based on a set ofconditions, which may include smart contract 3431 terms and conditions,marketplace conditions (of platform marketplaces and/or externalmarketplaces 3390), conditions monitored by monitoring system layers3306 and data collection systems 3318, and the like (such as of entities3330, including without limitation parties 4910, collateral 4802 andassets 4918, among others, as well as of interest rates, availablelenders, available terms and the like). For example, a user of the loanmanagement solution 4948 may create, configure (such as using one ormore templates or libraries), modify, set or otherwise handle (such asin a user interface of the loan management solution 4948 and/or RPAsystem 3442) various rules, thresholds, conditional procedures,workflows, model parameters, and the like that determine, or recommend,a loan management action or plan for management a set of loans of agiven type or types based on one or more events, conditions, states,actions, or the like, where the loan management plan may be based onvarious factors, such as the interest rates available from variousprimary and secondary lenders or issuers, permitted attributes ofborrowers (e.g., based on income, wealth, location, or the like),prevailing interest rates in a platform marketplace or externalmarketplace, the status of the parties of a set of loans, the status orother attributes of collateral 4802 or assets 4918, risk factors of theborrower, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 3448), status of debt, condition of collateral4802 or assets 4918 available to secure or back a set of loans, thestate of a business or business operation (e.g., receivables, payables,or the like), conditions of parties 4910 (such as net worth, wealth,debt, location, and other conditions), behaviors of parties (such asbehaviors indicating preferences, behaviors indicating debt preferences,payment preferences, or communication preferences), and many others.Loan management may include management with respect to terms andconditions of sets of loans, selection of appropriate loans,communications to encourage transactions, and the like. In embodiments,the loan management solution 4948 may automatically recommend or setrules, thresholds, actions, parameters, and the like (optionally bylearning to do so based on a training set of outcomes over time),resulting in a recommended loan management plan, which may specify aseries of actions required to accomplish a recommended or desiredoutcome of loan management (such as within a range of acceptableoutcomes), which may be automated and may involve conditional executionof steps based on monitored conditions and/or smart contract terms,which may be created, configured, and/or accounted for by the loanmanagement plan. Loan management plans may be determined and executedbased at least one part on market factors (such as competing interestrates offered by other issuers, property values, attributes of issuers,values of collateral or assets, and the like), as well as regulatoryand/or compliance factors. Loan management plans may be generated and/orexecuted for creation of new loans, for secondary loans or transactionsto back loans, for collection, for consolidation, for foreclosure, forsituations of bankruptcy or insolvency, for modifications of existingloans, for situations involving market changes (e.g., changes inprevailing interest rates or property values), and others. Inembodiments, adaptive intelligent systems layers 3304, includingartificial intelligence 3448, may be trained on a training set of loanmanagement activities by experts and/or on outcomes of loan managementactions to generate a set of predictions, classifications, controlinstructions, plans, models, or the like for automated creation,management and/or execution of one or more aspects of a loan managementplan.

Automated Blockchain Custody Service

Referring to FIG. 64 , in embodiments, a lending platform is providedhaving an automated blockchain custody service and solution for managinga set of custodial assets. The RPA system 3442 may provide automationfor one or more aspects of a custodial solution 6502 that enablesautomated custodial management and/or provides a recommendation or planfor a custodial activity relevant to a set of assets, such as onesinvolved in or backing a lending transaction or ones for which clientsseek custodial for security or administrative purposes, such as forassets of any of the types described herein, including cryptocurrenciesand other currencies, stock certificates and other evidence ofownership, securities, and many others. The custodial solution 6502and/or RPA system 3442 for handling custodial activity may include a setof interfaces, workflows, and models (which may include, use or beenabled by various adaptive intelligent systems layers 3304) and othercomponents that are configured to enable automation of one or moreaspects of a custodial action or a management process for trust orcustody of a set of assets 4918, such as based on a set of conditions,which may include smart contract 3431 terms and conditions, marketplaceconditions (of platform marketplaces and/or external marketplaces 3390),conditions monitored by monitoring system layers 3306 and datacollection systems 3318, and the like (such as of entities 3330,including without limitation parties 4910, collateral 4802 and assets4918, among others, and the like). For example, a user of the custodialsolution 6502 may create, configure (such as using one or more templatesor libraries), modify, set or otherwise handle (such as in a userinterface of the custodial solution 6502 and/or RPA system 3442) variousrules, thresholds, conditional procedures, workflows, model parameters,and the like that determine, or recommend, a custodial action or planfor management a set of assets of a given type or types based on one ormore events, conditions, states, actions, status or the like, where thecustodial plan may be based on various factors, such as the storageoptions available, the basis for retrieval of assets, the basis fortransfer of ownership of assets, and the like, condition of assets 4918for which custodial services will be required, behaviors of parties(such as behaviors indicating preferences), and many others. Custodialservices may include management with respect to terms and conditions ofsets of assets, selection of appropriate terms and conditions for trustand custody, selection of parameters for transfer of ownership,selection and provision of storage, selection and provision of secureinfrastructure for data storage, and others. In embodiments, thecustodial solution 48802 may automatically recommend or set rules,thresholds, actions, parameters, and the like (optionally by learning todo so based on a training set of outcomes over time), resulting in arecommended custodial plan, which may specify a series of actionsrequired to accomplish a recommended or desired outcome of custodialservices (such as within a range of acceptable outcomes), which may beautomated and may involve conditional execution of steps based onmonitored conditions and/or smart contract terms, which may be created,configured, and/or accounted for by the custodial plan. Custodial plansmay be determined and executed based at least one part on market factors(such as competing terms and conditions offered by other custodians,property values, attributes of clients, values of collateral or assets,costs of physical storage, costs of data storage, and the like), as wellas regulatory and/or compliance factors. In embodiments, adaptiveintelligent systems layers 3304, including artificial intelligence 3448,may be trained on a training set of custodial activities by expertsand/or on outcomes of custodial actions to generate a set ofpredictions, classifications, control instructions, plans, models, orthe like for automated creation, management and/or execution of one ormore aspects of a custodial plan. In embodiments, actions with respectto custody of a set of assets may be stored in a blockchain 3422, suchas in a distributed ledger.

In embodiments, provided herein is a system for handling trust andcustody for a set of assets. An example platform or system for handlingtrust and custody for a set of assets may include (a) a set of assetidentification services for identifying a set of assets for which afinancial institution is responsible for taking custody; and (b) a setof identity management services by which the financial institutionverifies identities and credentials of a set of entities entitled totake action with respect to the assets and a set of blockchain services,wherein at least one of the set of assets and identifying informationfor the set of assets is stored in a blockchain and wherein eventsrelated to the set of assets are recorded in a distributed ledger.Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments.

An example system includes where the credentials include ownercredentials, agent credentials, beneficiary credentials, trusteecredentials, and custodian credentials.

In embodiments, the events related to the set of assets include transferof title, death of an owner, disability of an owner, bankruptcy of anowner, foreclosure, placement of a lien, use of assets as collateral,designation of a beneficiary, undertaking a loan against assets,providing a notice with respect to assets, inspection of assets,assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, and designation of an ownership status.

In embodiments, the platform or system further includes a set of datacollection and monitoring services for monitoring at least one of theset of assets, a set of entities, and a set of events related to theassets.

In embodiments, the set of entities includes at least one of an owner, abeneficiary, an agent, a trustee, and a custodian.

In embodiments, the platform or system further includes a set of smartcontract services for managing the custody of the set of assets, whereinat least one event related to the set of assets is managed automaticallyby the smart contract based on a set of terms and conditions embodied inthe smart contract and based on information collected by the set of datacollection and monitoring services.

In embodiments, the events related to the set of assets include transferof title, death of an owner, disability of an owner, bankruptcy of anowner, foreclosure, placement of a lien, use of assets as collateral,designation of a beneficiary, undertaking a loan against assets,providing a notice with respect to assets, inspection of assets,assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, and designation of an ownership status.

Referring to FIG. 65 , in embodiments, a lending platform is providedhaving an underwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.The RPA system 3442 may provide automation for one or more aspects of anunderwriting solution 3420 that enables automated underwriting and/orprovides a recommendation or plan for an underwriting activity relevantto a loan transaction, such as for personal loans, corporate loans,subsidized loans, student loans, or other loans, including ones that maybe backed by assets, collateral, or commitments of a borrower. Theunderwriting solution 3420 and/or RPA system 3442 for underwriting mayinclude a set of interfaces, workflows, and models (which may include,use or be enabled by various adaptive intelligent systems layers 3304)and other components that are configured to enable automation of one ormore aspects of a underwriting action or a management process for a loantransaction, such as based on a set of conditions, which may includesmart contract 3431 terms and conditions, marketplace conditions (ofplatform marketplaces and/or external marketplaces 3390), conditionsmonitored by monitoring system layers 3306 and data collection systems3318, and the like (such as of entities 3330, including withoutlimitation parties 4910, collateral 4802 and assets 4918, among others,as well as of interest rates, available lenders, available terms, andthe like). For example, a user of the underwriting solution 3420 maycreate, configure (such as using one or more templates or libraries),modify, set or otherwise handle (such as in a user interface of theunderwriting solution 3420 and/or RPA system 3442) various rules,thresholds, conditional procedures, workflows, model parameters, and thelike that determine, or recommend, a underwriting action or plan formanagement a set of loans of a given type or types based on one or moreevents, conditions, states, actions, or the like, where the underwritingplan may be based on various factors, such as the interest ratesavailable from various primary and secondary lenders or issuers,permitted attributes of borrowers (e.g., based on income, wealth,location, or the like), prevailing interest rates in a platformmarketplace or external marketplace, the status of the parties of a setof loans, the status or other attributes of collateral 4802 or assets4918, risk factors of the borrower, one or more guarantors, market riskfactors and the like (including predicted risk based on one or morepredictive models using artificial intelligence 3448), status of debt,condition of collateral 4802 or assets 4918 available to secure or backa set of loans, the state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties 4910 (such asnet worth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatingdebt preferences, payment preferences, or communication preferences),and many others. Underwriting may include management with respect toterms and conditions of sets of loans, selection of appropriate loans,communications relevant to underwriting processes, and the like. Inembodiments, the underwriting solution 3420 may automatically recommendor set rules, thresholds, actions, parameters, and the like (optionallyby learning to do so based on a training set of outcomes over time),resulting in a recommended underwriting plan, which may specify a seriesof actions required to accomplish a recommended or desired outcome ofunderwriting (such as within a range of acceptable outcomes), which maybe automated and may involve conditional execution of steps based onmonitored conditions and/or smart contract terms, which may be created,configured, and/or accounted for by the underwriting plan. Underwritingplans may be determined and executed based at least one part on marketfactors (such as competing interest rates offered by other issuers,property values, borrower behavior, demographic trends, payment trends,attributes of issuers, values of collateral or assets, and the like), aswell as regulatory and/or compliance factors. Underwriting plans may begenerated and/or executed for new loans, for secondary loans ortransactions to back loans, for collection, for consolidation, forforeclosure, for situations of bankruptcy or insolvency, formodifications of existing loans, for situations involving market changes(e.g., changes in prevailing interest rates or property values), forforeclosure activities, and others. In embodiments, adaptive intelligentsystems layers 3304, including artificial intelligence 3448, may betrained on a training set of underwriting activities by experts and/oron outcomes of underwriting actions to generate a set of predictions,classifications, control instructions, plans, models, or the like forautomated creation, management and/or execution of one or more aspectsof an underwriting plan. In embodiments, events and outcomes ofunderwriting may be recorded in a blockchain 3422, such as in adistributed ledger, for secure access and retrieval by authorized users.Adaptive intelligent systems layers 3304 may, such as using variousartificial intelligence 3448 or expert systems disclosed herein and inthe documented incorporated by reference herein, improve or automate oneor more aspects of underwriting, such as by training a model, a neuralnet, a deep learning system, or the like based on a training set ofexpert interactions and/or a training set of outcomes from underwritingactivities.

Referring to FIG. 66 , in embodiments, a lending platform is providedhaving a loan marketing system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for marketing a loan to a set of prospective parties.The system 4800 may enable one or more aspects of a loan marketingsolution 6702 that enables automated loan marketing and/or provides arecommendation or plan for a loan marketing activity relevant to a loantransaction, such as for personal loans, corporate loans, subsidizedloans, student loans, or other loans, including ones that may be backedby assets, collateral, or commitments of a borrower. The loan marketingsolution 6702 (which in embodiments may include or use an RPA system3442 configured for loan marketing) may include a set of interfaces,workflows, and models (which may include, use or be enabled by variousadaptive intelligent systems layers 3304) and other components that areconfigured to enable automation of one or more aspects of a loanmarketing action or a management process for a loan transaction, such asbased on a set of conditions, which may include smart contract 3431terms and conditions (which may be configured, e.g., for a marketed setof loans), available capital for lending, regulatory factors,marketplace conditions (of platform marketplaces and/or externalmarketplaces 3390), conditions monitored by monitoring system layers3306 and data collection systems 3318, and the like (such as of entities3330, including without limitation parties 4910, collateral 4802 andassets 4918, among others, as well as of interest rates, availablelenders, available terms and the like), and others. For example, a userof the loan marketing solution 6702 may create, configure (such as usingone or more templates or libraries), modify, set or otherwise handle(such as in a user interface of the loan marketing solution 6702 and/orRPA system 3442) various rules, thresholds, conditional procedures,workflows, model parameters, and the like that determine, or recommend,a loan marketing action or plan for management a set of loans of a giventype or types based on one or more events, conditions, states, actions,or the like, where the loan marketing plan may be based on variousfactors, such as the interest rates available from various primary andsecondary lenders or issuers, returns on the capital that is madeavailable for loans, permitted or desired attributes of borrowers (e.g.,based on income, wealth, location, or the like), prevailing interestrates in a platform marketplace or external marketplace, the status ofthe parties of a set of loans, the status or other attributes ofcollateral 4802 or assets 4918, risk factors of the borrower, one ormore guarantors, market risk factors and the like (including predictedrisk based on one or more predictive models using artificialintelligence 3448), status of debt, condition of collateral 4802 orassets 4918 available to secure or back a set of loans, the state of abusiness or business operation (e.g., receivables, payables, or thelike), conditions of parties 4910 (such as net worth, wealth, debt,location, and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating debt preferences, paymentpreferences, or communication preferences), and many others. Loanmarketing may include management with respect to terms and conditions ofsets of loans, selection of appropriate loans, communications relevantto loan marketing processes, and the like. In embodiments, the loanmarketing solution 6702 may automatically recommend or set rules,thresholds, actions, parameters, and the like (optionally by learning todo so based on a training set of outcomes over time), resulting in arecommended loan marketing plan, which may specify a series of actionsrequired to accomplish a recommended or desired outcome of loanmarketing (such as within a range of acceptable outcomes), which may beautomated and may involve conditional execution of steps based onmonitored conditions and/or smart contract terms, which may be created,configured, and/or accounted for by the loan marketing plan. Loanmarketing plans may be determined and executed based at least one parton market factors (such as competing interest rates offered by otherissuers, property values, borrower behavior, demographic trends, paymenttrends, attributes of issuers, values of collateral or assets, and thelike), as well as regulatory and/or compliance factors. Loan marketingplans may be generated and/or executed for new loans, for secondaryloans or transactions to back loans, for collection, for consolidation,for foreclosure situations (e.g., as an alternative to foreclosure), forsituations of bankruptcy or insolvency, for modifications of existingloans, for situations involving market changes (e.g., changes inprevailing interest rates, available capital, or property values), andothers. In embodiments, adaptive intelligent systems layers 3304,including artificial intelligence 3448, may be trained on a training setof loan marketing activities by experts and/or on outcomes of loanmarketing actions to generate a set of predictions, classifications,control instructions, plans, models, or the like for automated creation,management and/or execution of one or more aspects of a loan marketingplan. In embodiments, events and outcomes of loan marketing may berecorded in a blockchain 3422, such as in a distributed ledger, forsecure access and retrieval by authorized users. Adaptive intelligentsystems layers 3304 may, such as using various artificial intelligence3448 or expert systems disclosed herein and in the documentedincorporated by reference herein, improve or automate one or moreaspects of entity rating, such as by training a model, a neural net, adeep learning system, or the like based on a training set of expertinteractions and/or a training set of outcomes from loan marketingactivities.

Referring to FIG. 67 , in embodiments, a lending platform is providedhaving a rating system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services, and smart contract services for ratinga set of loan-related entities. The system 4800 may enable one or moreaspects of an entity rating solution 6801 that enables automated entityrating and/or provides a recommendation or plan for an entity ratingactivity relevant to a loan transaction, such as for personal loans,corporate loans, subsidized loans, student loans, or other loans,including ones that may be backed by assets, collateral, or commitmentsof a borrower. The entity rating solution 6801 (which in embodiments mayinclude or use an RPA system 3442 configured for entity rating) mayinclude a set of interfaces, workflows, and models (which may include,use or be enabled by various adaptive intelligent systems layers 3304)and other components that are configured to enable automation of one ormore aspects of an entity rating action or a rating process for a loantransaction, such as based on a set of conditions, attributes, events,or the like, which may include attributes of entities 3330 (such asvalue, quality, location, net worth, price, physical condition, healthcondition, security, safety, ownership and the like), smart contract3431 terms and conditions (which may be configured or populated, e.g.,based on ratings for a rated set of loans), regulatory factors,marketplace conditions (of platform marketplaces and/or externalmarketplaces 3390), conditions monitored by monitoring system layers3306 and data collection systems 3318, and the like (such as of entities3330, including without limitation parties 4910, collateral 4802 andassets 4918, among others, as well as of interest rates, availablelenders, available terms and the like), and others. For example, a userof the entity rating solution 4901 may create, configure (such as usingone or more templates or libraries), modify, set or otherwise handle(such as in a user interface of the entity rating solution 6801 and/orRPA system 3442) various rules, thresholds, conditional procedures,workflows, model parameters, and the like that determine, or recommend,an entity rating action or plan for rating a set of loans of a giventype or types based on one or more events, attributes, parameters,characteristics, conditions, states, actions, or the like, where theentity rating plan may be based on various factors (e.g., based onincome, wealth, location, or the like or parties 4910, relative toothers, or based on condition of collateral 4802 or assets 4918, or thelike), prevailing conditions of a platform marketplace or externalmarketplace, the status of the parties of a set of loans, the status orother attributes of collateral 4802 or assets 4918, risk factors of theborrower, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 3448), status of debt, condition of collateral4802 or assets 4918 available to secure or back a set of loans, thestate of a business or business operation (e.g., receivables, payables,or the like), conditions of parties 4910 (such as net worth, wealth,debt, location, and other conditions), behaviors of parties (such asbehaviors indicating preferences, behaviors indicating debt preferences,payment preferences, or communication preferences), and many others.Entity rating may include management with respect to terms andconditions of sets of loans, selection of appropriate loans,communications relevant to entity rating processes, and the like. Inembodiments, the entity rating solution 6801 may automatically recommendor set rules, thresholds, actions, parameters, and the like (optionallyby learning to do so based on a training set of outcomes over time),resulting in a recommended entity rating plan, which may specify aseries of actions required to accomplish a recommended or desiredoutcome of entity rating (such as within a range of acceptableoutcomes), which may be automated and may involve conditional executionof steps based on monitored conditions and/or smart contract terms,which may be created, configured, and/or accounted for by the entityrating plan. Entity rating plans may be determined and executed based atleast one part on market factors (such as competing interest ratesoffered by other issuers, property values, borrower behavior,demographic trends, payment trends, attributes of issuers, values ofcollateral or assets, and the like), as well as regulatory and/orcompliance factors. Entity rating plans may be generated and/or executedfor new loans, for secondary loans or transactions to back loans, forcollection, for consolidation, for foreclosure situations (e.g., as analternative to foreclosure), for situations of bankruptcy or insolvency,for modifications of existing loans, for situations involving marketchanges (e.g., changes in prevailing interest rates, available capital,or property values), and others. In embodiments, adaptive intelligentsystems layers 3304, including artificial intelligence 3448, may betrained on a training set of entity rating activities by experts and/oron outcomes of entity rating actions to generate a set of predictions,classifications, control instructions, plans, models, or the like forautomated creation, management, and/or execution of one or more aspectsof an entity rating plan. In embodiments, events and outcomes of entityrating may be recorded in a blockchain 3422, such as in a distributedledger, for secure access and retrieval by authorized users. Adaptiveintelligent systems layers 3304 may, such as using various artificialintelligence 3448 or expert systems disclosed herein and in thedocumented incorporated by reference herein, improve or automate one ormore aspects of entity rating, such as by training a model, a neuralnet, a deep learning system, or the like based on a training set ofexpert interactions and/or a training set of outcomes from entity ratingactivities.

Referring to FIG. 68 , in embodiments, a lending platform is providedhaving a regulatory and/or compliance system 3426 with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy that applies to alending transaction. The system 4800 may enable one or more aspects of aregulatory and compliance solution 3426 that enables automatedregulatory and compliance and/or provides a recommendation or plan for aregulatory and compliance activity relevant to a loan transaction, suchas for personal loans, corporate loans, subsidized loans, student loans,or other loans, including ones that may be backed by assets, collateral,or commitments of a borrower. The regulatory and compliance solution3426 (which in embodiments may include or use an RPA system 3442configured for automating regulatory and compliance activities based ona training set of interactions by experts in regulatory and/orcompliance activities) may include a set of interfaces, workflows, andmodels (which may include, use or be enabled by various adaptiveintelligent systems layers 3304) and other components that areconfigured to enable automation of one or more aspects of a regulatoryand compliance action or a regulatory and/or compliance process for aloan transaction, such as based on a set of policies, regulations, laws,requirements, specifications, conditions, attributes, events, or thelike, which may include attributes of or applicable to entities 3330involved in a lending transaction and/or the terms and conditions ofloans (including smart contract 3431 terms and conditions (which may beconfigured or populated, e.g., based on terms and conditions that arepermitted for a given set of loans)), as well as various marketplaceconditions (of platform marketplaces and/or external marketplaces 3390),conditions monitored by monitoring system layers 3306 and datacollection systems 3318, and the like (such as of entities 3330,including without limitation parties 4910, collateral 4802 and assets4918, among others, as well as of interest rates, available lenders,available terms and the like), and others. For example, a user of theregulatory and compliance solution 3426 may create, configure (such asusing one or more templates or libraries), modify, set or otherwisehandle (such as in a user interface of the regulatory and/or compliancesolution 3426 and/or RPA system 3442) various rules, thresholds,conditional procedures, workflows, model parameters, and the like thatdetermine, or recommend, a regulatory and compliance action or plan forgoverning a set of loans of a given type or types based on one or moreevents, attributes, parameters, characteristics, conditions, states,actions, or the like, where the regulatory and compliance plan may bebased on various factors (e.g., based on permitted interest rates,required notices (e.g., regarding annualized percentage rate reporting),permitted borrowers (e.g., students for federally subsidized studentloans), permitted lenders, permitted issuers, income (e.g., forlow-income loans), wealth (e.g., for loans that are permitted by policyto be provided only to adequately capitalized parties), location (e.g.,for geographically governed lending programs, such as for municipaldevelopment), conditions of a platform marketplace or externalmarketplace (such as where loans are required to have interest ratesthat do not exceed a threshold that is calculated based on prevailinginterest rates), the status of the parties of a set of loans, the statusor other attributes of collateral 4802 or assets 4918, risk factors ofthe borrower, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 3448), status of debt, condition of collateral4802 or assets 4918 available to secure or back a set of loans, thestate of a business or business operation (e.g., receivables, payables,or the like), conditions of parties 4910 (such as net worth, wealth,debt, location, and other conditions), behaviors of parties (such asbehaviors indicating preferences, behaviors indicating debt preferences,payment preferences, or communication preferences), and many others).Regulatory and compliance may include governance with respect to termsand conditions of sets of loans, selection of appropriate loans, noticesrequired to be provided, underwriting policies, communications relevantto regulatory and compliance processes, and the like. In embodiments,the regulatory and compliance solution 3426 may automatically recommendor set rules, thresholds, actions, parameters, and the like (optionallyby learning to do so based on a training set of outcomes over time),resulting in a recommended regulatory and compliance plan, which mayspecify a series of actions required to accomplish a recommended ordesired outcome of regulatory and compliance (such as within a range ofacceptable outcomes), which may be automated and may involve conditionalexecution of steps based on monitored conditions and/or smart contractterms, which may be created, configured, and/or accounted for by theregulatory and compliance plan. Regulatory and compliance plans may bedetermined and executed based at least one part on market factors (suchas competing interest rates offered by other issuers, property values,borrower behavior, demographic trends, payment trends, attributes ofissuers, values of collateral or assets, and the like), as well asregulatory and/or compliance factors. Regulatory and compliance plansmay be generated and/or executed for new loans, for secondary loans ortransactions to back loans, for collection, for consolidation, forforeclosure situations (e.g., as an alternative to foreclosure), forsituations of bankruptcy or insolvency, for modifications of existingloans, for situations involving market changes (e.g., changes inprevailing interest rates, available capital, or property values), andothers. In embodiments, adaptive intelligent systems layers 3304,including artificial intelligence 3448, may be trained on a training setof regulatory and compliance activities by experts and/or on outcomes ofregulatory and compliance actions to generate a set of predictions,classifications, control instructions, plans, models, or the like forautomated creation, management and/or execution of one or more aspectsof a regulatory and compliance plan. In embodiments, events and outcomesof regulatory and compliance may be recorded in a blockchain 3422, suchas in a distributed ledger, for secure access and retrieval byauthorized users. Adaptive intelligent systems layers 3304 may, such asusing various artificial intelligence 3448 or expert systems disclosedherein and in the documented incorporated by reference herein, improveor automate one or more aspects of regulatory and compliance, such as bytraining a model, a neural net, a deep learning system, or the likebased on a training set of expert interactions and/or a training set ofoutcomes from regulatory and compliance activities.

An example lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions. An example system includes anInternet of Things and sensor platform for monitoring at least one of aset of assets and a set of collateral for a loan, a bond, or a debttransaction. An example system includes a smart contract and distributedledger platform for managing at least one of ownership of a set ofcollateral and a set of events related to a set of collateral. Anexample system includes a smart contract system that automaticallyadjusts an interest rate for a loan based on information collected viaat least one of an Internet of Things system, a crowdsourcing system, aset of social network analytic services, and a set of data collectionand monitoring services. An example system includes a crowdsourcingsystem for obtaining information about at least one of a state of a setof collateral for a loan and a state of an entity relevant to aguarantee for a loan. An example system includes a smart contract thatautomatically adjusts an interest rate for a loan based on at least oneof a regulatory factor and a market factor for a specific jurisdiction.An example system includes a smart contract that automaticallyrestructures debt based on a monitored condition. An example systemincludes a social network monitoring system for validating thereliability of a guarantee for a loan. An example system includes anInternet of Things data collection and monitoring system for validatingreliability of a guarantee for a loan. An example system includes arobotic process automation system for negotiation of a set of terms andconditions for a loan. An example system includes a robotic processautomation system for loan collection. An example system includes arobotic process automation system for consolidating a set of loans. Anexample system includes a robotic process automation system for managinga factoring loan. An example system includes a robotic processautomation system for brokering a mortgage loan. An example systemincludes a crowdsourcing and automated classification system forvalidating condition of an issuer for a bond. An example system includesa social network monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes anInternet of Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored by the IoT. An example systemincludes a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored in a social network. An examplesystem includes a system that varies the terms and conditions of asubsidized loan based on a parameter monitored by crowdsourcing. Anexample system includes an automated blockchain custody service formanaging a set of custodial assets. An example system includes anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions. Anexample system includes a loan marketing system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for marketing a loan to a set of prospectiveparties. An example system includes a rating system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for rating a set of loan-related entities. Anexample system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having an Internet ofThings and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debttransaction. An example system includes a smart contract and distributedledger platform for managing at least one of ownership of a set ofcollateral and a set of events related to a set of collateral. Anexample system includes a smart contract system that automaticallyadjusts an interest rate for a loan based on information collected viaat least one of an Internet of Things system, a crowdsourcing system, aset of social network analytic services, and a set of data collectionand monitoring services. An example system includes a crowdsourcingsystem for obtaining information about at least one of a state of a setof collateral for a loan and a state of an entity relevant to aguarantee for a loan. An example system includes a smart contract thatautomatically adjusts an interest rate for a loan based on at least oneof a regulatory factor and a market factor for a specific jurisdiction.An example system includes a smart contract that automaticallyrestructures debt based on a monitored condition. An example systemincludes a social network monitoring system for validating thereliability of a guarantee for a loan. An example system includes anInternet of Things data collection and monitoring system for validatingreliability of a guarantee for a loan. An example system includes arobotic process automation system for one or more of negotiation of aset of terms and conditions for a loan, loan collection, consolidating aset of loans, managing a factoring loan, or brokering a mortgage loan.An example system includes a crowdsourcing and automated classificationsystem for validating condition of an issuer for a bond. An examplesystem includes a social network monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes an Internet of Things data collection and monitoring systemwith artificial intelligence for classifying a condition about a bond.

An example system includes a system that varies the terms and conditionsof a subsidized loan based on a parameter monitored by at least one ofthe IoT, a social network, or crowdsourcing.

An example system includes an automated blockchain custody service formanaging a set of custodial assets. An example system includes anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions. Anexample system includes a loan marketing system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for marketing a loan to a set of prospectiveparties. An example system includes a rating system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for rating a set of loan-related entities. Anexample system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a smart contractand distributed ledger platform for managing at least one of ownershipof a set of collateral and a set of events related to a set ofcollateral. An example system includes a smart contract system thatautomatically adjusts an interest rate for a loan based on informationcollected via at least one of an Internet of Things system, acrowdsourcing system, a set of social network analytic services, and aset of data collection and monitoring services. An example systemincludes a crowdsourcing system for obtaining information about at leastone of a state of a set of collateral for a loan and a state of anentity relevant to a guarantee for a loan. An example system includes asmart contract that automatically adjusts an interest rate for a loanbased on at least one of a regulatory factor and a market factor for aspecific jurisdiction. An example system includes a smart contract thatautomatically restructures debt based on a monitored condition. Anexample system includes a social network monitoring system forvalidating the reliability of a guarantee for a loan.

An example system includes an Internet of Things data collection andmonitoring system for validating reliability of a guarantee for a loan.An example system includes a robotic process automation system fornegotiation of a set of terms and conditions for a loan. An examplesystem includes a robotic process automation system for loan collection.An example system includes a robotic process automation system for atleast one of consolidating a set of loans, managing a factoring loan, orbrokering a mortgage loan. An example system includes a crowdsourcingand automated classification system for validating condition of anissuer for a bond. An example system includes a social networkmonitoring system with artificial intelligence for classifying acondition about a bond. An example system includes an Internet of Thingsdata collection and monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by at least one of the IoT, a social network,or crowdsourcing. An example system includes an automated blockchaincustody service for managing a set of custodial assets. An examplesystem includes an underwriting system for a loan with a set of dataintegrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a smart contractsystem that automatically adjusts an interest rate for a loan based oninformation collected via at least one of an Internet of Things system,a crowdsourcing system, a set of social network analytic services, and aset of data collection and monitoring services. An example systemincludes a crowdsourcing system for obtaining information about at leastone of a state of a set of collateral for a loan and a state of anentity relevant to a guarantee for a loan. An example system includes asmart contract that automatically adjusts an interest rate for a loanbased on at least one of a regulatory factor and a market factor for aspecific jurisdiction. An example system includes a smart contract thatautomatically restructures debt based on a monitored condition. Anexample system includes a social network monitoring system forvalidating the reliability of a guarantee for a loan. An example systemincludes an Internet of Things data collection and monitoring system forvalidating reliability of a guarantee for a loan. An example systemincludes a robotic process automation system for negotiation of a set ofterms and conditions for a loan. An example system includes a roboticprocess automation system for at least one of a loan collection,consolidating a set of loans, managing a factoring loan, or brokering amortgage loan. An example system includes a crowdsourcing and automatedclassification system for validating condition of an issuer for a bond.An example system includes a social network monitoring system withartificial intelligence for classifying a condition about a bond. Anexample system includes an Internet of Things data collection andmonitoring system with artificial intelligence for classifying acondition about a bond. An example system includes a system that variesthe terms and conditions of a subsidized loan based on a parametermonitored by at least one of the IoT, a social network, orcrowdsourcing. An example system includes an automated blockchaincustody service for managing a set of custodial assets. An examplesystem includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation and a policy, related to a lendingtransaction.

An example lending platform is provided herein having a crowdsourcingsystem for obtaining information about at least one of a state of a setof collateral for a loan and a state of an entity relevant to aguarantee for a loan. An example system includes a smart contract thatautomatically adjusts an interest rate for a loan based on at least oneof a regulatory factor and a market factor for a specific jurisdiction.An example system includes a smart contract that automaticallyrestructures debt based on a monitored condition. An example systemincludes a social network monitoring system for validating thereliability of a guarantee for a loan. An example system includes anInternet of Things data collection and monitoring system for validatingreliability of a guarantee for a loan. An example system includes arobotic process automation system for at least one of negotiation of aset of terms and conditions for a loan, loan collection, consolidating aset of loans, managing a factoring loan, or brokering a mortgage loan.An example system includes a crowdsourcing and automated classificationsystem for validating condition of an issuer for a bond. An examplesystem includes a social network monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes an Internet of Things data collection and monitoring systemwith artificial intelligence for classifying a condition about a bond.An example system includes a system that varies the terms and conditionsof a subsidized loan based on a parameter monitored by at least one ofthe IoT, a social network, or crowdsourcing. An example system includesan automated blockchain custody service for managing a set of custodialassets. An example system includes an underwriting system for a loanwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for underwriting lending entitiesand transactions. An example system includes a loan marketing systemwith a set of data integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a smart contractthat automatically adjusts an interest rate for a loan based on at leastone of a regulatory factor and a market factor for a specificjurisdiction. An example system includes a smart contract thatautomatically restructures debt based on a monitored condition. Anexample system includes a social network monitoring system forvalidating the reliability of a guarantee for a loan. An example systemincludes an Internet of Things data collection and monitoring system forvalidating reliability of a guarantee for a loan. An example systemincludes a robotic process automation system for at least one ofnegotiation of a set of terms and conditions for a loan, loancollection, consolidating a set of loans, managing a factoring loan, orbrokering a mortgage loan. An example system includes a crowdsourcingand automated classification system for validating condition of anissuer for a bond. An example system includes a social networkmonitoring system with artificial intelligence for classifying acondition about a bond.

An example system includes an Internet of Things data collection andmonitoring system with artificial intelligence for classifying acondition about a bond.

An example system includes a system that varies the terms and conditionsof a subsidized loan based on a parameter monitored by at least one ofthe IoT, a social network, or crowdsourcing.

An example system includes an automated blockchain custody service formanaging a set of custodial assets.

An example system includes an underwriting system for a loan with a setof data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for underwriting lending entitiesand transactions.

An example system includes a loan marketing system with a set of dataintegrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for marketing a loan to a set of prospectiveparties.

An example system includes a rating system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for rating a set of loan-related entities.

An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a smart contractthat automatically restructures debt based on a monitored condition. Anexample system includes a social network monitoring system forvalidating the reliability of a guarantee for a loan. An example systemincludes an Internet of Things data collection and monitoring system forvalidating reliability of a guarantee for a loan. An example systemincludes a robotic process automation system for at least one ofnegotiation of a set of terms and conditions for a loan, loancollection, consolidating a set of loans, managing a factoring loan, orbrokering a mortgage loan. An example system includes a crowdsourcingand automated classification system for validating condition of anissuer for a bond. An example system includes a social networkmonitoring system with artificial intelligence for classifying acondition about a bond. An example system includes an Internet of Thingsdata collection and monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by at least one of the IoT, a social network,or crowdsourcing. An example system includes an automated blockchaincustody service for managing a set of custodial assets. An examplesystem includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a social networkmonitoring system for validating the reliability of a guarantee for aloan. An example system includes an Internet of Things data collectionand monitoring system for validating reliability of a guarantee for aloan. An example system includes a robotic process automation system forat least one of negotiation of a set of terms and conditions for a loan,loan collection, consolidating a set of loans, managing a factoringloan, or brokering a mortgage loan. An example system includes acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond. An example system includes a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond. An example system includes an Internet of Thingsdata collection and monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by at least one of the IoT, a social network,or crowdsourcing. An example system includes an automated blockchaincustody service for managing a set of custodial assets. An examplesystem includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having an Internet ofThings data collection and monitoring system for validating reliabilityof a guarantee for a loan. An example system includes a robotic processautomation system for at least one of negotiation of a set of terms andconditions for a loan, loan collection, consolidating a set of loans,managing a factoring loan, or brokering a mortgage loan. An examplesystem includes a crowdsourcing and automated classification system forvalidating condition of an issuer for a bond. An example system includesa social network monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes anInternet of Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored by at least one of the IoT, a socialnetwork, or crowdsourcing. An example system includes an automatedblockchain custody service for managing a set of custodial assets. Anexample system includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a robotic processautomation system for negotiation of a set of terms and conditions for aloan. An example system includes a robotic process automation system forat least one of loan collection, consolidating a set of loans, managinga factoring loan, or brokering a mortgage loan. An example systemincludes a crowdsourcing and automated classification system forvalidating condition of an issuer for a bond. An example system includesa social network monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes anInternet of Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored by at least one of the IoT, a socialnetwork, or crowdsourcing. An example system includes an automatedblockchain custody service for managing a set of custodial assets. Anexample system includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a loan and having a compliancesystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for automaticallyfacilitating compliance with at least one of a law, a regulation, and apolicy related to a lending transaction.

An example lending platform is provided herein having a robotic processautomation system for loan collection. An example system includes arobotic process automation system for at least one of consolidating aset of loans, managing a factoring loan, or brokering a mortgage loan.An example system includes a crowdsourcing and automated classificationsystem for validating condition of an issuer for a bond. An examplesystem includes a social network monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes an Internet of Things data collection and monitoring systemwith artificial intelligence for classifying a condition about a bond.An example system includes a system that varies the terms and conditionsof a subsidized loan based on a parameter monitored by at least one ofthe IoT, a social network, or crowdsourcing. An example system includesan automated blockchain custody service for managing a set of custodialassets. An example system includes an underwriting system for a loanwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for underwriting lending entitiesand transactions. An example system includes a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a robotic processautomation system for consolidating a set of loans. An example systemincludes a robotic process automation system for at least one ofmanaging a factoring loan or brokering a mortgage loan. An examplesystem includes a crowdsourcing and automated classification system forvalidating condition of an issuer for a bond. An example system includesa social network monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes anInternet of Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored by at least one of the IoT, a socialnetwork, or crowdsourcing. An example system includes an automatedblockchain custody service for managing a set of custodial assets. Anexample system includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a robotic processautomation system for managing a factoring loan. An example systemincludes a robotic process automation system for brokering a mortgageloan. An example system includes a crowdsourcing and automatedclassification system for validating condition of an issuer for a bond.An example system includes a social network monitoring system withartificial intelligence for classifying a condition about a bond. Anexample system includes an Internet of Things data collection andmonitoring system with artificial intelligence for classifying acondition about a bond. An example system includes a system that variesthe terms and conditions of a subsidized loan based on a parametermonitored by at least one of the IoT, a social network, orcrowdsourcing. An example system includes an automated blockchaincustody service for managing a set of custodial assets. An examplesystem includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a robotic processautomation system for brokering a mortgage loan. An example systemincludes a crowdsourcing and automated classification system forvalidating condition of an issuer for a bond. An example system includesa social network monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes anInternet of Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored by at least one of the IoT, a socialnetwork, or crowdsourcing. An example system includes a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing. An example system includes anautomated blockchain custody service for managing a set of custodialassets. An example system includes an underwriting system for a loanwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for underwriting lending entitiesand transactions. An example system includes a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a crowdsourcingand automated classification system for validating condition of anissuer for a bond. An example system includes a social networkmonitoring system with artificial intelligence for classifying acondition about a bond. An example system includes an Internet of Thingsdata collection and monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by at least one of the IoT, a social network,or crowdsourcing. An example system includes an automated blockchaincustody service for managing a set of custodial assets. An examplesystem includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a social networkmonitoring system with artificial intelligence for classifying acondition about a bond. An example system includes an Internet of Thingsdata collection and monitoring system with artificial intelligence forclassifying a condition about a bond. An example system includes asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by at least one of the IoT, a social network,or crowdsourcing. An example system includes an automated blockchaincustody service for managing a set of custodial assets. An examplesystem includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having an Internet ofThings data collection and monitoring system with artificialintelligence for classifying a condition about a bond. An example systemincludes a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored by at least one of the IoT, a socialnetwork, or crowdsourcing. An example system includes an automatedblockchain custody service for managing a set of custodial assets. Anexample system includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored by the IoT. An example system includes a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored at least one of in a social network or bycrowdsourcing. An example system includes an automated blockchaincustody service for managing a set of custodial assets. An examplesystem includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored in a social network. An example system includes asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by crowdsourcing. An example system includes anautomated blockchain custody service for managing a set of custodialassets. An example system includes an underwriting system for a loanwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for underwriting lending entitiesand transactions. An example system includes a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing. An example system includes anautomated blockchain custody service for managing a set of custodialassets. An example system includes an underwriting system for a loanwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for underwriting lending entitiesand transactions. An example system includes a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having an automatedblockchain custody service for managing a set of custodial assets. Anexample system includes an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions. An example system includes a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for marketing a loan to a set ofprospective parties. An example system includes a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities. An example system includes a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation, and a policy related to a lendingtransaction.

An example lending platform is provided herein having an underwritingsystem for a loan with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions. An example system includes a loanmarketing system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for marketing a loanto a set of prospective parties. An example system includes a ratingsystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities. An example system includes having a compliancesystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for automaticallyfacilitating compliance with at least one of a law, a regulation, and apolicy related to a lending transaction.

An example lending platform is provided herein having a loan marketingsystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for marketing a loanto a set of prospective parties. An example system includes a ratingsystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities. An example system includes a compliance systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation, and a policyrelated to a lending transaction. In embodiments, a lending platform isprovided herein having a rating system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for rating a set of loan-related entities and having acompliance system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for automaticallyfacilitating compliance with at least one of a law, a regulation, and apolicy related to a lending transaction.

In embodiments, a database service may be provided herein that embodies,enables, or is associated with a blockchain, ledger, such as adistributed ledger, or the like, such as in connection with any of theembodiments described herein or in the document incorporated byreference that refer to them. In embodiments, the database service maycomprise a transparent, immutable, and cryptographically verifiableledger database service, such as the Amazon™ QLDB™ database service. Thedatabase service may be included within one or connected with one ormore of the layers or microservices of a system 3300, such as theadaptive intelligent systems layers 3304 or the data handling layer3308. The service may be used, for example, in connection with acentralized ledger that records all changes or transactions andmaintains an immutable record of these changes, such as by tracing anentity through various environments or processes, tracking the historyof debits and credits in a series of transactions, or validating factsrelevant to an underwriting process, a claim, or a legal or regulatoryproceeding. A ledger may be owned by a single trusted entity or set oftrusted entities and may be shared with any other entities, such as onesthat working together in a coordinated process, such as a transaction, aproduction process, a joint service, or many others. As compared to arelational database, the database service may provide immutable,cryptographically verifiable ledger entries, without the need for customaudit tables or trails. As compared to a blockchain framework, such adatabase service may include capabilities to perform queries, createtables, index data, and the like. The database service may optionallyomit requirements for many blockchain frameworks that slow performance,such as requirement of consensus before committing transactions, or thedatabase service may employ optional consensus features. In embodiments,the database service may comprise a transparent, immutable, andcryptographically verifiable ledger that users can use to buildapplications that act as a system of record, where multiple parties aretransacting within a centralized, trusted entity or set of entities. Thedatabase service may complement or substitute for the building auditfunctionality into a relational database or for using conventionaldistributed ledger capabilities in a blockchain framework. The databaseservice may use an immutable transactional log or journal, which maytrack each application data change and maintain a comprehensive andverifiable history of changes. In embodiments, transactions may beconfigured to comply with requirements of atomicity, consistency,isolation, and durability (ACID) to be logged in the log or journal,which is configured to prevent deletions or modifications. Changes maybe cryptographically chained, such that they are auditable andverifiable, such as in a history that users can query or analyze, suchas using conventional query types, such as SQL queries. In embodiments,the database service may be provided in a serverless form, such thatthere is no need to provision specific server capacity or to configureread/write limits. To initiate the database service, the user can createa ledger, define tables, and the like, and the database service willautomatically scale to support application demands. In contrast toblockchain-based ledgers, a database service may omit requirements for adistributed consensus, so it can execute more transactions in the sametime.

In embodiments of the present disclosure that refer to a blockchain ordistributed ledger, a managed blockchain service may be used, such asthe Amazon™ Managed Blockchain™, which may comprise a facility forconvenient creation and management of a scaled blockchain network. Themanaged blockchain service may be provided as part of a layered dataservices architecture as described in this disclosure. In situationswhere users want immutable and verifiable capability provided by ablockchain or ledger, they may also seek the ability to allow multipleparties to transact, execute contracts (such as in smart contractembodiments described herein), share data, and the like without atrusted central authority. As setting up conventional blockchainframeworks requires significant time and technical expertise, where eachparticipant in a permissioned network has to provision hardware, installsoftware, create, and manage certificates for access control, andconfigure network settings. As a given blockchain application grows,there is also activity required to scale the network, monitor resourcesacross blockchain nodes, add or remove hardware and manage networkavailability. In embodiments, a managed blockchain service may providefor management of each of these requirements and enabling capabilities.This may include supporting open source blockchain frameworks andenabling selection, setup, and deployment of a selected framework in adashboard, console, or other user interface, wherein users may choosetheir preferred framework, add network members, and configure membernodes that will process transaction requests. The managed blockchainservice may then automatically create a blockchain network, such as onethat can span multiple accounts with multiple nodes per member, andconfigure software, security, and network settings. The managedblockchain service may secure and manage network certificates, such aswith a key management service, which may allow customer management ofthe keys. In embodiments, the managed blockchain service may include oneor more APIs, such as a voting API, such as one that allows networkmembers to vote, such as to vote to add or remove members. Asapplication usage grows for a given application (such as any of thenoted applications described in connection with the platform 3300),users can add more capacity to the blockchain network, such as with asimple API call. In embodiments, the managed blockchain service may beprovided with a range of combinations of compute and memory capacity,such as to give users the ability to choose the right mix of resourcesfor a given blockchain-based application.

Referring to FIG. 69 , a system for automated loan management isdepicted. A variety of entities/parties 6938 may have a connection to aloan 6924 including a borrower 6940, a lender 6942, 3rd parties 6944such as a neutral 3rd party (e.g. such as an assessor, or an interested3rd party (e.g., a regulator, company employees, and the like)). A loan6924 may be subject to a smart lending contract 6990 includinginformation such as loan terms and conditions 6929, loan actions 6930,loan events 6932, lender priorities 6928, and the like. The smartlending contract 6990 may be recording in loan entry 6941 in adistributed ledger 6963. The smart lending contract 6990 may be storedas blockchain data 6934.

In an illustrative example, controller 6922 may receive collateral data6974 such as collateral related events 6908, collateral attributes 6910,environmental data 6912 about an environment in which the collateral6902 is situated, sensor data 6914 where the senor 6904 may be affixedto an item of collateral, to a case containing an item of collateral orin proximity to an item of collateral. In embodiments, collateral datamay be acquired by an Internet of Things Circuit 6920, a camera system,a networked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

The controller 6922 may also monitor and/or receive data from a socialnetwork information 6958 from which a financial condition 6992 may beinferred such as a rating of a party, a tax status of a party, a creditreport of the party, a credit rating of a party, a website rating of aparty, a set of customer reviews for a product of a party, a socialnetwork rating of a party, a set of credentials of a party, a set ofreferrals of a party, a set of testimonials for a party, a set ofbehavior of a party, and the like. The controller 6922 may also receivemarketplace information 6948 such as pricing 6950, financial data 6954such as a publicly stated valuation of the party, a set of propertyowned by the party as indicated by public records, a valuation of a setof property owned by the party, a bankruptcy condition of the party, aforeclosure status of the entity, a contractual default status of theentity, a regulatory violation status of the entity, a criminal statusof the entity, an export controls status of the entity, an embargostatus of the entity, a tariff status of the entity, a tax status of theentity, a credit report of the entity, a credit rating of the entity,and the like.

In embodiments, artificial intelligence systems 6962 may be part of acontroller 6922 or on remote systems. The AI systems 6962 may include avaluation circuit 6964 structured to determine a value for an item ofcollateral based on collateral data 6974 and a valuation model and avalue model improvement circuit 6966 to improve the valuation model onthe basis of a first set of received collateral data 6974 and theoutcome of loans for which collateral associated with that first set ofreceived collateral data acted as security. The AI systems 6962 mayinclude an automated agent circuit 6970 that takes action based oncollateral events, loan-events, and the like. Actions may includeloan-related actions such as offering the loan, accepting the loan,underwriting the loan, setting an interest rate for the loan, deferringa payment requirement, modifying an interest rate for the loan,validating title for collateral, recording a change in title, assessinga value of collateral, initiating inspection of collateral, calling theloan, closing the loan, setting terms and conditions for the loan,providing notices required to be provided to a borrower, foreclosing onproperty subject to the loan, modifying terms and conditions for theloan, and the like. Actions may include collateral-related actions suchas validating title for the one of the assigned set of items ofcollateral, recording a change in title for the one of the assigned setof items of collateral, assessing the value of the one of the assignedset of items of collateral, initiating inspection of the one of theassigned set of items of collateral, initiating maintenance of the oneof the assigned set of items of collateral, initiating security for theone of the assigned set of items of collateral, modifying terms andconditions for the one of the assigned set of items of collateral, andthe like. The AI systems 6962 may include a cluster circuit 6972 tocreate groups of items of collateral based on a common attribute. Thecluster circuit 6972 may also determine a group of off-set items ofcollateral where the off-set items of collateral share a commonattribute with one or more items of collateral. Data may be gathered onthe off-set items of collateral and use it as representative of theitems of collateral. A smart contract circuit 6968 may create a smartlending contract 6990 as described elsewhere herein.

Referring to FIG. 70 , a controller may include a blockchain servicecircuit 7044 structured to interpret a plurality of access controlfeatures 7048 such as corresponding to parties associated with a loan7030 and associated with blockchain data 7040. The system may include adata collection circuit 7012 structured to interpret entity information7002, collateral data 7004, and the like, such as corresponding toentities related to a lending transaction corresponding to the loan,collateral conditions, and the like. The system may include a smartcontract circuit 7022 structured to specify loan terms and conditions7024, contracts 7028, and the like, relating to the loan. The system mayinclude a loan management circuit 7032 structured to interpret loanrelated actions 7034 and/or events 7038 in response to the entityinformation, the plurality of access control features, and the loanterms and conditions, where the loan related events are associated withthe loan; implement loan related activities in response to the entityinformation, the plurality of access control features, and the loanterms and conditions, wherein the loan related activities are associatedwith the loan; and where each of the blockchain service circuit, thedata collection circuit, the smart contract circuit, and the loanmanagement circuit further comprise a corresponding applicationprogramming interface (API) component structured to facilitatecommunication among the circuits of the system. For example, a lender7008 may interface with the controller through secure access controlinterface 7052 (e.g., through access control instructions 7054)structured to interface to the controller through a secure accesscontrol circuit 7050. The data collection circuit 7012 may be structuredto receive collateral data 7004 and entity information 7002 such asinformation about parties to the loan such as a lender, a borrower, or athird party, an item of collateral, a machine or property associatedwith a party to the loan, a product of a party to the loan, and thelike. Collateral data 7004 may include a type of the item of collateral,a category of the item of collateral, a value of the item of collateral,a price of a type of the item of collateral, a value of a type of theitem of collateral, a specification of the item of collateral, a productfeature set of the item of collateral, a model of the item ofcollateral, a brand of the item of collateral, a manufacturer of theitem of collateral, an age of the item of collateral, a liquidity of theitem of collateral, a shelf-life of the item of collateral, a usefullife of the item of collateral, a condition of the item of collateral, avaluation of the item of collateral, a status of the item of collateral,a context of the item of collateral, a state of the item of collateral,a storage location of the item of collateral, a history of the item ofcollateral, an ownership of the item of collateral, a caretaker of theitem of collateral, a security of the item of collateral, a condition ofan owner of the item of collateral, a lien on the item of collateral, astorage condition of the item of collateral, a maintenance history ofthe item of collateral, a usage history of the item of collateral, anaccident history of the item of collateral, a fault history of the itemof collateral, a history of ownership of the item of collateral, anassessment of the item of collateral, a geolocation of the item ofcollateral, a jurisdictional location of the item of collateral, and thelike. The data collection circuit 7012 may determine a collateralcondition based on the received data. The received data 7002, 7004 andthe collateral condition 7010 may be provided to AI circuits 7042, whichmay include an automated agent circuit 7014 (e.g., processing events7018, 7020), a smart contract services circuit 7022, and a loanmanagement circuit 7032.

Referring to FIG. 71 , an illustrative and non-limiting example methodfor handling a loan 7100 is depicted. The example method may includeinterpreting a plurality of access control features (step 7102);interpreting entity information (step 7104); specifying loan terms andconditions (step 7108); performing a contract related event in responseto entity information (step 7110); interpreting an event relevant to theloan (step 7112); performing a loan action in response to the event(step 7114); providing a user interface (step 7118); creating a smartlending contract (step 7120); and recording the smart lending contractas blockchain data (step 7122).

Referring to FIG. 72 , a system 7200 for adaptive intelligence androbotic process automation capabilities of a transactional, financial,and marketplace enablement is illustrated. The system 7200 may include acontroller 7223, which may include a data collection circuit 7202, whichreceives collateral data 7201 and determines collateral condition 7204.The controller 7223 may further include a plurality of AI circuits 7254.The plurality of AI circuits 7254 may include a valuation circuit 7208,which may include a valuation model improvement circuit 7210 and acluster circuit 7212. The plurality of AI circuits 7254 may include asmart contract services circuit 7214 including smart lending contracts7216 for loans 7225. The plurality of AI circuits 7254 may include anautomated agent circuit 7218, which takes loan-related actions 7220. Thecontroller 7223 may further include a reporting circuit 7222 and amarket value monitoring circuit 7224, which also determines collateralcondition 7204. The controller 7223 may further include a secure accessuser interface 7228, which receives access control instructions 7230from lenders 7242. The access control instructions 7230 are provided toa secure access control circuit 7232, which provides instructions toblockchain service circuit 7234, which interprets access controlfeatures 7238 and provides access to a lender 7242 or other party. Theblockchain service circuit 7234 all stores the collateral data and aunique collateral ID as blockchain data 7235.

Referring to FIG. 73 , a method 7300 for automated smart contractcreation and collateral assignment is depicted. The method 7300 mayinclude receiving first and second collateral data regarding an item ofcollateral 7302, creating a smart lending contract 7304, associating thecollateral data with a unique identifier for the item of collateral7308, and storing the unique identifier and the collateral in ablockchain structure 7310. The method may further include interpreting acondition of the collateral based on the collateral data 7312,identifying a collateral event 7314, reporting a collateral event 7318,and performing an action in response to the collateral 7320. The method7300 may further include identifying a group of off-set items ofcollateral 7322, accessing marketplace information relevant to theoff-set items of collateral or the identified item of collateral 7314,and modifying a term or condition of the loan based on the marketplaceinformation 7328. The method 7300 may further include receiving accesscontrol instructions 7330, interpreting a plurality of access controlfeatures 7332, and providing access to the collateral date 7334.

Referring to FIG. 74 , an illustrative and non-limiting example systemfor handling a loan 7400 is depicted. The example system may include acontroller 7401. The controller 7401 may include a data collectioncircuit 7412, a valuation circuit 7444, a user interface 7454 (e.g., forinterface with a user 7406), a blockchain service circuit 7458, andseveral artificial intelligence circuits 7442 including a smart contractservices circuit 7422, a loan management circuit 7492, a clusteringcircuit 7432, and an automated agent circuit 7414 (e.g., for processingloan related events 7439 and loan actions 7438).

The blockchain service circuit 7458 may be structured to interface witha distributed ledger 7440. The data collection circuit 7412 may bestructured to receive data related to a plurality of items of collateral7404 or data related to environments of the plurality of items ofcollateral 7402. The valuation circuit 7444 may be structured todetermine a value for each of the plurality of items of collateral basedon a valuation model 7452 and the received data. The smart contractservices circuit 7422 may be structured to interpret a smart lendingcontract 7431 for a loan and to modify the smart lending contract 7431by assigning, based on the determined value for each of the plurality ofitems of collateral, at least a portion of the plurality of items ofcollateral 7428 as security for the loan such that the determined valueof the plurality of items of collateral is sufficient to providesecurity for the loan. The blockchain service circuit 7458 may befurther structured to record the assigned at least a portion of items ofcollateral 7428 to an entry in the distributed ledger 7440, wherein theentry is used to record events relevant to the loan. Each of theblockchain service circuit, the data collection circuit, the valuationcircuit, and the smart contract circuit may further include acorresponding application programming interface (API) componentstructured to facilitate communication among the circuits of the system.

Modifying the smart lending contract 7431 may further include specifyingterms and conditions 7424 that govern an item selected from the listconsisting of: a loan term, a loan condition, a loan-related event, anda loan-related activity. The terms and conditions 7424 may each includeat least one member selected from the group consisting of: a principalamount of the loan, a balance of the loan, a fixed interest rate, avariable interest rate description, a payment amount, a paymentschedule, a balloon payment schedule, a collateral specification, acollateral substitution description, a description of at least one ofthe parties, a guarantee description, a guarantor description, asecurity description, a personal guarantee, a lien, a foreclosurecondition, a default condition, a consequence of default, a covenantrelated to any one of the foregoing, and a duration of any one of theforegoing.

The loan 7430 may include at least one loan type selected from the loantypes consisting of: an auto loan, an inventory loan, a capitalequipment loan, a bond for performance, a capital improvement loan, abuilding loan, a loan backed by an account receivable, an invoicefinance arrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The item of collateral may include at least one item selected from theitems consisting of: a vehicle, a ship, a plane, a building, a home, areal estate property, an undeveloped land property, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, a tool, an item of machinery, and an item ofpersonal property.

The data collection circuit 7412 may be further structured to receiveoutcome data 7410 related to the loan 7430 and a corresponding item ofcollateral, and wherein the valuation circuit 7444 comprises anartificial intelligent circuit structured to iteratively improve 7450the valuation model 7452 based on the outcome data 7410.

The valuation circuit 7444 may further include a market value datacollection circuit 7448 structured to monitor and report marketplaceinformation relevant to the value of at least one of the plurality ofitems of collateral. The market value data collection circuit 7448 maybe further structured to monitor pricing or financial data for itemsthat are similar to the item of collateral in at least one publicmarketplace.

The clustering circuit 7432 may be structured to identify a set ofoffset items 7434 for use in valuing the item of collateral based onsimilarity to an attribute of the collateral.

The attribute of the collateral may be selected from among a list ofattributes consisting of: a category of the collateral, an age of thecollateral, a condition of the collateral, a history of the collateral,a storage condition of the collateral, and a geolocation of thecollateral.

The data collection circuit 7412 may be further structured to interpreta condition 7411 of the item of collateral.

The data collection circuit may further include at least one systemselected from the systems consisting of: an Internet of Things system, acamera system, a networked monitoring system, an internet monitoringsystem, a mobile device system, a wearable device system, a userinterface system, and an interactive crowdsourcing system.

The loan includes at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

A loan management circuit 7492 may be structured to interpret an eventrelevant to the loan 7439 and to perform an action 7438 related to theloan in response to the event relevant to the loan.

The event relevant to the loan may include an event relevant to at leastone of: a value of the loan, a condition of collateral of the loan, oran ownership of collateral of the loan.

The action related to the loan may include at least one of: modifyingthe terms and conditions for the loan, providing a notice to one of theparties, providing a required notice to a borrower of the loan, andforeclosing on a property subject to the loan.

The corresponding API components of the circuits may further includeuser interfaces structured to interact with a plurality of users of thesystem.

The plurality of users may each include: one of the plurality ofparties, one of the plurality of entities, or a representative of anyone of the foregoing. At least one of the plurality of users mayinclude: a prospective party, a prospective entity, or a representativeof any one of the foregoing.

Referring to FIG. 75 , an illustrative and non-limiting example methodfor handling a loan 7500 is depicted. The example method may includereceiving data related to a plurality of items of collateral (step7502); setting a value for each of the plurality of items of collateral(step 7504); assigning at least a portion of the plurality of items ofcollateral as security for a loan (step 7508); and recording theassigned at least a portion of the plurality of items of collateral toan entry in a distributed ledger, wherein the entry is used to recordevents relevant to the loan (step 7510). A smart lending contract may bemodified for the loan (step 7512).

Terms and conditions may be specified for the loan (step 7514). Theterms and conditions are each selected from the list consisting of: aprincipal amount of debt, a balance of debt, a fixed interest rate, avariable interest rate, a payment amount, a payment schedule, a balloonpayment schedule, a party, a guarantee, a guarantor, a security, apersonal guarantee, a lien, a duration, a covenant, a foreclosecondition, a default condition, and a consequence of default.

Outcome data related to the loan may be received (step 7518). Avaluation model may be iteratively improved based on the outcome dataand corresponding collateral (step 7520). Marketplace informationrelevant to the value of at least one of the plurality of items ofcollateral may be monitored (step 7522).

A set of items similar to one of the plurality of items of collateralmay be identified based on similarity to an attribute of the one of theplurality of items of collateral (step 7524).

A condition of the one of the plurality of items of collateral may beinterpreted (step 7528).

Events related to a value of the one of the plurality of items ofcollateral, a condition of the one of the plurality of items ofcollateral, or an ownership of the one of the items of collateral may bereported (step 7530).

An event relevant to: a value of one of the plurality of items ofcollateral, a condition of one of the plurality of items of collateral,or an ownership of one of the plurality of items of collateral may beinterpreted (step 7532); and an action related to the secured loan inresponse to the event relevant to the one of the plurality of items ofcollateral for said secured loan may be performed (step 7534).

The loan-related action may be selected from among the actionsconsisting of: offering a loan, accepting a loan, underwriting a loan,setting an interest rate for a loan, deferring a payment requirement,modifying an interest rate for a loan, validating title for collateral,recording a change in title, assessing the value of collateral,initiating inspection of collateral, calling a loan, closing a loan,setting terms and conditions for a loan, providing notices required tobe provided to a borrower, foreclosing on property subject to a loan,and modifying terms and conditions for a loan.

Referring to FIG. 76 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities7600 is depicted. The example system may include a controller 7601. Thecontroller may include a data collection circuit 7628 which may collectdata such as collateral data 7632, environmental data 7634 related tothe collateral, and the like from a variety of sources and systems suchas: an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, and aninteractive crowdsourcing system. Based on the received data 7632, 7634the data collection circuit 7628 may identify a collateral event 7630.

The controller 7601 may also include a variety of AI circuits 7644,including a valuation circuit 7602 which may, based in part on thereceived data 7632, 7634, determine a value for an item of collateral.The valuation circuit 7602 may include a market value monitoring circuit7606 structured to determine market data regarding an item of collateralor an off-set item of collateral, where the market data may contributeto the valuation for the item of collateral. The AI circuits may alsoinclude a smart contract services circuit 7610 to facilitate servicesrelated to a loan 7629 such as creating a smart contract 7622,identifying terms and conditions 7624 for the smart contract 7622,identifying lender priorities and tracking apportionment of value 7626among lenders. The smart contract services circuit 7610 may provide datato a block chain service circuit 7636, which is able to create andmodify a loan entry 7627 on a distributed ledger 7625 where the loanentry 7627 may include terms and conditions, data regarding items ofcollateral used to secure the loan, lender priority and apportionment ofvalue, and the like. The AI circuits 7644 may also include a collateralclassification circuit 7640, which creates groups of off-set items ofcollateral 7604, which share at least one attribute with one of theitems of collateral, where the common attribute may be a category of theitems, an age of the items, a condition of the items, a history of theitems, an ownership of the items, a caretaker of the items, a securityof the items, a condition of an owner of the items, a lien on the items,a storage condition of the items, a geolocation of the items, ajurisdictional location of the items, and the like. The use of off-setitems of collateral 7642 may facilitate the market value monitoringcircuit 7606 in obtaining relevant market data and in the overalldetermination of value for an item of collateral.

The data collection circuit 7628 may utilize the received data and adetermination of value for an item of collateral to identify acollateral event 7630. Based on the collateral event 7630, an automatedagent circuit 7646 may take an action 7648. The action 7648 may be aloan-related action such as offering the loan, accepting the loan,underwriting the loan, setting an interest rate for a loan, deferring apayment requirement, modifying the interest rate for the loan, callingthe loan, closing the loan, setting terms and conditions for the loan,providing notices required to be provided to a borrower, foreclosing onproperty subject to the loan, modifying terms and conditions for theloan, and the like. The action 7648 may be a collateral-related actionsuch as validating title for the one of a set of items of collateral,recording a change in title for one of a set of items of collateral,assessing the value of the one of a set of items of collateral,initiating inspection of one of a set of items of collateral, initiatingmaintenance of one of a set of items of collateral, initiating securityfor one of a set of items of collateral, modifying terms and conditionsfor one of a set of items of collateral, and the like.

Referring to FIG. 77 , an illustrative and non-limiting example method7700 for loan creation and management is depicted. The example method7700 may include receiving data related to a set of items of collateral(step 7702) that provide security for a loan and receiving data relatedto an environment of one of a set of items of collateral (step 7704). Asmart lending contract for the loan may be created (step 7706) and theset of items of collateral may be recorded in the smart lending contract(step 7708). A loan-entry may be recoded in a distributed ledger (step7770) where the loan entry includes the smart lending contract or areference to the smart contract.

The value for each of the set of items of collateral may be determined(7772) and the value of the items of collateral may be apportioned amonglenders (step 7776) based on the priority of the different lenders. Thevaluation model may be modified (step 7774) based on a learning setincluding a set of valuation determinations of a set of items ofcollateral and the outcomes of loans having those items of collateral assecurity and the valuation of those items of collateral.

A collateral event may be determined (step 7778) based on received dataor a valuation of one of the items of collateral. A loan-related actionmay be performed in response to the determined collateral event (step7780) where the loan-related action includes offering the loan,accepting the loan, underwriting the loan, setting an interest rate fora loan, deferring a payment requirement, modifying the interest rate forthe loan, calling the loan, closing the loan, setting terms andconditions for the loan, providing notices required to be provided to aborrower, foreclosing on property subject to the loan, modifying termsand conditions for the loan, or the like.

A collateral-related action may be performed in response to thedetermined collateral event (step 7782), where the collateral-relatedaction includes validating title for the one of the set of items ofcollateral, recording a change in title for the one of the set of itemsof collateral, assessing the value of the one of the set of items ofcollateral, initiating inspection of the one of the set of items ofcollateral, initiating maintenance of the one of the set of items ofcollateral, initiating security for the one of the set of items ofcollateral, modifying terms and conditions for the one of the set ofitems of collateral, or the like.

One or more group of off-set items of collateral may be identified (step7784) where each item in a group of off-set items of collateral shares acommon attribute with at least one of the items of collateral.Marketplace information may then be monitored for data related tooff-set items of collateral (step 7786). The monitored marketplaceinformation regarding one or more off-set items of collateral may beused to update a value of an item of collateral (step 7788). Theloan-entry in the distributed ledger may be updated (7730) with theupdated value of the item of collateral.

Referring to FIG. 78 , an example system 7800 for adaptive intelligenceand robotic process automation capabilities of a transactional,financial, and marketplace enablement is depicted. The system 7800 mayinclude a controller 7801, which may include a plurality of AI circuits7820. The plurality of AI circuits 7820 may include a smart contractservices circuit 7810 to create and modify a smart lending contract 7812for a loan 7818. Smart lending contracts 7812 may include the terms andconditions 7814 for the loan 7818, a covenant specifying a requiredvalue of collateral, information regarding a loan 7818, items ofcollateral, and information on lenders, including lender prioritiesincluding apportionment 7816 of the value of items of collateral amongthe lenders.

The plurality of AI circuits 7820 may include a valuation circuit 7802structured to determine one or more values 7808 for items of collateralbased on a valuation model 7809 and collateral data 7840. The valuationcircuit 7802 may include a collateral classification circuit 7803 toidentify items of off-set collateral 7807 based on common attributeswith items of collateral used to secure a loan 7818. A market valuemonitoring circuit 7806 may receive marketplace information 7842regarding items of collateral and off-set items of collateral 7807. Themarketplace information 7842 may be used by the valuation model 7809 indetermining values 7808 for items of collateral. The valuation circuit7802 may further include a valuation model improvement circuit 7804 toimprove the valuation model 7809 used to determine values 7808. Thevaluation model improvement circuit 7804 may utilize a training setincluding previously determined values 7808 for items of collateral anddata regarding the outcome of loans for which those items of collateralacted as security.

The plurality of AI circuits 7820 may include a loan management circuit7822, which may include a value comparison circuit 7828 to compare avalue 7808 of an item of collateral with a required value of the item ofcollateral as specified in a covenant of the loan, determining acollateral satisfaction value 7830. The smart contract services circuit7810 may determine, in response to the collateral satisfaction value7830, a term or a condition 7814 for a loan 7818, where the term ofconditions 7814 is related to a loan component such as a loan party, aloan collateral, a loan-related event, and a loan-related activity forthe smart lending contract 7812, and the like. The term of condition maybe a principal amount of the loan, a balance of the loan, a fixedinterest rate, a variable interest rate description, a payment amount, apayment schedule, a balloon payment schedule, a collateralspecification, a collateral substitution description, a description of aparty, a guarantee description, a guarantor description, a securitydescription, a personal guarantee, a lien, a foreclosure condition, adefault condition, a consequence of default, a covenant related to anyone of the foregoing, a duration of any one of the foregoing, and thelike. The term of condition may be a principal amount of debt, a balanceof debt, a fixed interest rate, a variable interest rate, a paymentamount, a payment schedule, a balloon payment schedule, a party, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, aconsequence of default, and the like. The smart contract servicescircuit 7810 may modify the smart lending contract 7812 to include newterms or conditions 7814, such as those determined in response to thecollateral satisfaction value 7830.

The loan management circuit 7822 may also include an automated agentcircuit 7824 to take an action 7826 based on the collateral satisfactionvalue 7830. The action 7826 may be a collateral-related action such asvalidating title for the item of collateral, recording a change in titlefor the item of collateral, assessing the value of the item ofcollateral, initiating inspection of the item of collateral, initiatingmaintenance of the item of collateral, initiating security for the itemof collateral, modifying terms and conditions for the item ofcollateral, and the like. The action 7826 may be a loan-related actionsuch as offering the loan, accepting the loan, underwriting the loan,setting an interest rate for a loan, deferring a payment requirement,modifying the interest rate for the loan, calling the loan, closing theloan, setting terms and conditions for the loan, providing noticesrequired to be provided to a borrower, foreclosing on property subjectto the loan, modifying terms and conditions for the loan, and the like.

The controller 7801 may also include a data collection circuit 7832 toreceive collateral data 7840 and determine a collateral event 7834. Thecollateral event 7834 and collateral data 7840 may then be reported by areporting circuit 7836. A blockchain service circuit 7838 may create andupdate blockchain data 7825 where a copy of the smart lending contract7812 is stored.

Referring to FIG. 79 , an illustrative and non-limiting method forrobotic process automation of transactional, financial, and marketplaceactivities is depicted. An example method may include receiving datarelated to an item or set of items of collateral (step 7902) where theitem(s) of collateral are acting as security for a loan. A value for theitem of collateral is determined (step 7904) based on received data anda valuation model. A smart lending contract is created (step 7906),which specifies information about the loan including a covenantspecifying a required value of collateral needed to secure the loan.

The value of the item(s) of collateral may be compared to the value ofcollateral specified in the covenant (step 7908) and a collateralsatisfaction value determined (step 7910), where the collateralsatisfaction value may be positive if the value of the collateralexceeds the required value of collateral or negative if the value ofcollateral is less than the required value of collateral. A loan-relatedaction may be implemented in response to the collateral satisfactionvalue (step 7912). A term or condition may be determined in response tothe collateral satisfaction value (step 7914) and the smart lendingcontract modified (step 7916).

The valuation model may be modified (step 7918) based on a first set ofvaluation determinations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security, using a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, a hybrid system of at least two of any ofthe foregoing, and the like.

A group of off-set items of collateral may be identified (step 7920)based on common attributes with the collateral such as a category of theitem of collateral, an age of the item of collateral, a condition of theitem of collateral, a history of the item of collateral, an ownership ofthe item of collateral, a caretaker of the item of collateral, asecurity of the item of collateral, a condition of an owner of the itemof collateral, a lien on the item of collateral, a storage condition ofthe item of collateral, a geolocation of the item of collateral, and ajurisdictional location of the item of collateral. Marketplaceinformation such as may be monitored for data related to the off-setcollateral (step 7922) such as pricing or financial data and the smartlending contract modified in response to the marketplace information(step 7924). An action may be automatically initiated (step 7926) basedon the marketplace information. The action may include modifying a termof the loan, issuing a notice of default, initiating a foreclosureaction modifying a condition of the loan, providing a notice to a partyof the loan, providing a required notice to a borrower of the loan,foreclosing on a property subject to the loan, validating title for theitem of collateral, recording a change in title for the item ofcollateral, assessing the value of the item of collateral, initiatinginspection of the item of collateral, initiating maintenance of the itemof collateral, initiating security for the item of collateral, andmodifying terms and conditions for the item of collateral, and the like.

Referring to FIG. 80 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities8000 is depicted. The example system may include a controller 8001including a data collection circuit 8028 structured to receivecollateral data 8032 regarding a plurality of items of collateral usedto secure a set of loans 8018. The data collection circuit 8028 mayinclude an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, aninteractive crowdsourcing system, and the like. The items of collateralmay include a vehicle, a ship, a plane, a building, a home, a realestate property, an undeveloped land property, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, a tool, an item of machinery, an item of personalproperty, and the like. The set of loans may include an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, a subsidizedloan, and the like. The set of loans 8018 may be distributed among aplurality of borrowers as means of diversifying the risk of the loans.

The controller 8001 may also include a plurality of AI circuits 8044,including a collateral classification circuit 8020, to identify, fromamong the items of collateral, a group of collateral 8022, which relatedby sharing a common attribute, wherein the common attribute is among thereceived collateral data 8032, such as a type of the item of collateral,a category of the item of collateral, a value of the item of collateral,a price of a type of the item of collateral, a value of a type of theitem of collateral, a specification of the item of collateral, a productfeature set of the item of collateral, a model of the item ofcollateral, a brand of the item of collateral, a manufacturer of theitem of collateral, an age of the item of collateral, a liquidity of theitem of collateral, a shelf-life of the item of collateral, a usefullife of the item of collateral, a condition of the item of collateral, avaluation of the item of collateral, a status of the item of collateral,a context of the item of collateral, a state of the item of collateral,a storage location of the item of collateral, a history of the item ofcollateral, an ownership of the item of collateral, a caretaker of theitem of collateral, a security of the item of collateral, a condition ofan owner of the item of collateral, a lien on the item of collateral, astorage condition of the item of collateral, a maintenance history ofthe item of collateral, a usage history of the item of collateral, anaccident history of the item of collateral, a fault history of the itemof collateral, a history of ownership of the item of collateral, anassessment of the item of collateral, a geolocation of the item ofcollateral, a jurisdictional location of the item of collateral, and thelike. The collateral classification circuit 8020 may also identifyoff-set collateral 8023 where items of off-set collateral 8023 and theitems of collateral share a common attribute.

The reporting circuit 8034 may also report a collateral event 8030 basedon the collateral data 8032. An automated agent circuit 8008 mayautomatically perform an action 8009 based on the collateral event 8030.The action 8009 may be a collateral-related action such as validatingtitle for one of the plurality of items of collateral, recording achange in title for one of the plurality of items of collateral,assessing the value of one of the plurality of items of collateral,initiating inspection of one of the plurality of items of collateral,initiating maintenance of the one of the plurality of items ofcollateral, initiating security for one of the plurality of items ofcollateral, modifying terms and conditions for one of the plurality ofitems of collateral, and the like. The action 8009 may be a loan-relatedaction such as offering the loan, accepting the loan, underwriting theloan, setting an interest rate for a loan, deferring a paymentrequirement, modifying the interest rate for the loan, calling the loan,closing the loan, setting terms and conditions for the loan, providingnotices required to be provided to a borrower, foreclosing on propertysubject to the loan, modifying terms and conditions for the loan, andthe like.

The controller 8001 may also include a smart contract services circuit8010 to create a smart lending contract 8012 for an individual loan or aset of loans 8018 where the smart lending contract 8012 identifies asubset of collateral 8016 selected from the group of related items ofcollateral 8022 sharing a common attribute to act as security for theset of loans 8018. The smart contract services circuit 8010 may alsoredefine the subset of collateral 8016 based on an updated value for anitem of collateral, thus rebalancing the items of collateral used for aset of loans based on the values of the collateral items. Theidentification of the subset of collateral 8016 may be identified inreal-time when the common attribute changes in real time (e.g. a statusof an item of collateral or whether collateral is in transit during adefined time period). Further, the smart contract services circuit 8010may determine a term or condition 8014 for the loan based on a value ofone of the items of collateral, where the term or the condition 8014 isrelated to a loan component such as a loan party, a loan collateral, aloan-related event, and a loan-related activity. The term or condition8014 may be a principal amount of the loan, a balance of the loan, afixed interest rate, a variable interest rate description, a paymentamount, a payment schedule, a balloon payment schedule, a collateralspecification, a collateral substitution description, a description of aparty, a guarantee description, a guarantor description, a securitydescription, a personal guarantee, a lien, a foreclosure condition, adefault condition, a consequence of default, a covenant related to anyone of the foregoing, a duration of any one of the foregoing, and thelike.

The controller may also include a valuation circuit 8002 to determine avalue 8040 for each item of collateral in the subset of items ofcollateral based on the received data and a valuation model 8042. Avaluation model improvement circuit 8004 may modify the valuation model8042 based on a first set of valuation determinations for a first set ofitems of collateral and a corresponding set of loan outcomes having thefirst set of items of collateral as security. The valuation modelimprovement circuit 8004 may include a machine learning system, amodel-based system, a rule-based system, a deep learning system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, a simulation system, a hybrid systemincluding at least two of the foregoing, or the like. The valuationcircuit 8002 may also include a market value data collection circuit8006 to monitor and report marketplace information 8038 such as pricingor financial data relevant to off-set collateral 8023 or a group ofcollateral 8022.

Referring to FIG. 81 , a method 8100 for automated transactional,financial, and marketplace activities is depicted. A method may includereceiving data related to an item of collateral (step 8102), identifyinga group of items of collateral (step 8104) where the items in the groupshare a common attribute or feature, identifying a subset of the groupas security for a set of loans (8108), and creating a smart lendingcontract (step 8110) for the set of loans where the smart lendingcontract identifies the subset of group acting as security. The commonattribute shared by the group of items of collateral may be in thereceived data.

The value of each item of collateral may be determined (8112) using thereceived data and a valuation model. The subset of collateral used assecurity may then be redefined based on the value of the different itemsof collateral (8114). A term of condition for at least one of the smartlending contracts may be determined (8118) based on the value for atleast one of the items of collateral in the subset of the group and thesmart lending contract modified to include the determined term orcondition (8120). Further, in some embodiments, the valuation model maybe modified (8122) based on a first set of valuation determinations fora first set of items of collateral and a corresponding set of loanoutcomes having the first set of items of collateral as security.

A group of off-set items of collateral may be identified (step 8124)where each member of the group of off-set items of collateral and thegroup of the plurality of items share a common attribute. An informationmarketplace may be monitored and marketplace information reported (step8126) for the group of off-set items of collateral.

FIG. 82 depicts a system 8200 including a data collection circuit 8224structured to receive data 8202 related to a set of parties to a loan8212. The data collection circuit may be structured to receivecollateral-related data 8208 related to a set of items of collateral8214 acting as security for the loan and determine a condition of theset of items of collateral, where the change in the interest rate may bebased on a condition of the set of items of collateral. The item ofcollateral may be a vehicle, a ship, a plane, a building, a home, a realestate property, an undeveloped land property, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, a tool, an item of machinery, an item of personalproperty, and the like. The received data may include an attribute ofthe set of parties to the loan, where the change in the interest ratemay be based in part on the attribute. The data collection circuit mayinclude a system such as an Internet of Things circuit, an image capturedevice, a networked monitoring circuit, an internet monitoring circuit,a mobile device, a wearable device, a user interface circuit, aninteractive crowdsourcing circuit, and the like. For instance, the datacollection circuit may include an Internet of Things circuit 8254structured to monitor attributes of the set of parties to the loan. Thedata collection circuit may include a wearable device 8206 associatedwith at least one of the set of parties, where the wearable device isstructured to acquire human-related data 8204, and where the receiveddata includes at least a portion of the human-related data. The datacollection circuit may include a user interface circuit 8226 structuredto receive data from the parties of the loan and provide the data fromat least one of the parties of the loan as a portion of the receiveddata. The data collection circuit may include an interactivecrowdsourcing circuit 8238 structured to solicit data regarding at leastone of the set of parties of the loan, receive solicited data, andprovide at least a subset of the solicited data as a portion of thereceived data. The data collection circuit may include an internetmonitoring circuit 8240 structured to retrieve data related to theparties of the loan from at least one publicly available informationsite 8222. The system may include a smart contract circuit 8232structured to create a smart lending contract 8234 for the loan 8216.The loan may be a type selected from among loan types such as aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, a subsidizedloan, and the like. The smart contract circuit may be structured todetermine a term or a condition 8218 for the smart lending contractbased on the attribute and modify the smart lending contract to includethe term or the condition. The term or condition may be related to aloan component, such as a loan party, a loan collateral, a loan-relatedevent, a loan-related activity, and the like. The term or condition maybe a principal amount of the loan, a balance of the loan, a fixedinterest rate, a variable interest rate description, a payment amount, apayment schedule, a balloon payment schedule, a collateralspecification, a collateral substitution description, a description of aparty, a guarantee description, a guarantor description, a securitydescription, a personal guarantee, a lien, a foreclosure condition, adefault condition, a consequence of default, a covenant related to anyone of the foregoing, a duration of any one of the foregoing, and thelike. The system may include an automated agent circuit 8236 structuredto automatically perform a loan-related action 8220 in response to thereceived data, where the loan-related action is a change in an interestrate for the loan, and where the smart contract circuit may be furtherstructured to update the smart lending contract with the changedinterest rate. The system may include a valuation circuit 8228structured to determine, such as based on the received data and avaluation model 8230, a value for the at least one of the set of itemsof collateral. The smart contract circuit may be structured to determinea term or a condition for the smart lending contract based on the valuefor the at least one of the set of items of collateral and modify thesmart lending contract to include the term or the condition. The term orthe condition may be related to a loan component, such as a loan party,a loan collateral, a loan-related event, a loan-related activity, andthe like. The term or the condition may be a principal amount of theloan, a balance of the loan, a fixed interest rate, a variable interestrate description, a payment amount, a payment schedule, a balloonpayment schedule, a collateral specification, a collateral substitutiondescription, a description of a party, a guarantee description, aguarantor description, a security description, a personal guarantee, alien, a foreclosure condition, a default condition, a consequence ofdefault, a covenant related to any one of the foregoing, a duration ofany one of the foregoing, and the like. The valuation circuit mayinclude a valuation model improvement circuit 8242, where the valuationmodel improvement circuit may modify the valuation model, such as basedon a first set of valuation determinations 8244 for a first set of itemsof collateral and a corresponding set of loan outcomes having the firstset of items of collateral as security. The valuation model improvementcircuit may include a system such as a machine learning system, amodel-based system, a rule-based system, a deep learning system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, a simulation system, a hybrid systemincluding at least two of the foregoing, and the like. The change in theinterest rate may be further based on the value for the at least one ofthe set of items of collateral. The valuation circuit may include amarket value data collection circuit 8246 structured to monitor andreport marketplace information 8248 for offset items of collateralrelevant to the value of the item of collateral. The market value datacollection circuit may be structured to monitor one of pricing orfinancial data for the offset items of collateral in at least one publicmarketplace and report the monitored one of pricing or financial data.The system may include a collateral classification circuit 8250structured to identify a group of off-set items of collateral 8252,where each member of the group of off-set items of collateral and atleast one of the set of items of collateral share a common attribute.The common attribute may be a category of the item, an age of the item,a condition of the item, a history of the item, an ownership of theitem, a caretaker of the item, a security of the item, a condition of anowner of the item, a lien on the item, a storage condition of the item,a geolocation of the item, a jurisdictional location of the item, andthe like.

FIG. 83 depicts a method 8300 including receiving data related to atleast one of a set of parties to a loan 8302, creating a smart lendingcontract for the loan 8304, performing a loan-related action in responseto the received data, wherein the loan-related action is a change in aninterest rate for the loan 8308, and updating the smart lending contractwith the changed interest rate 8310. The method may further includereceiving data related to a set of items of collateral acting assecurity for the loan 8314, determining a condition of the set of itemsof collateral 8318, and performing a loan-related action in response tothe condition of the set of items of collateral, where the loan-relatedaction may be a change in interest rate for the loan 8320. The methodmay further include receiving data related to a set of items ofcollateral acting as security for the loan 8322, determining a conditionof at least one of the set of items of collateral 8324, determining aterm or a condition for the smart lending contract based on thecondition of the at least one of the set of items of collateral 8328,and modifying the smart lending contract to include the term or thecondition 8330. The method may include identifying a group of off-setitems of collateral wherein each member of the group of off-set items ofcollateral and at least one of the set of items of collateral share acommon attribute and monitoring the group of offset items of collateralin a public marketplace, and further may report the monitored data. Themethod may include changing, such as based on the monitored group ofoff-set items of collateral, the interest rate of the loan secured by atleast one of the set of items of collateral.

FIG. 84 depicts a system 8400 including a data collection circuit 8418structured to acquire data 8402 from public sources of information 8404(e.g., a website, a news article, a social network, crowdsourcedinformation, and the like) related to at least one party of a set ofparties 8406 to a loan 8408 (e.g., primary lender, a secondary lender, alending syndicate, a corporate lender, a government lender, a banklender, a secured lender, bond issuer, a bond purchaser, an unsecuredlender, a guarantor, a provider of security, a borrower, a debtor, anunderwriter, an inspector, an assessor, an auditor, a valuationprofessional, a government official, an accountant, and the like). Thedata collection circuit may be further structured to receivecollateral-related data 8410 related to a set of items of collateral8412 acting as security for the loan and to determine a condition of atleast one of the set of items of collateral, wherein the change in theinterest rate is further based on the condition of the at least one ofthe set of items of collateral. The acquired data may include afinancial condition of the at least one party of the set of parties tothe loan. The financial condition may be determined based on at leastone attribute of the at least one party of the set of parties to theloan, the attribute selected from among the list of attributesconsisting of: a publicly stated valuation of the party, a set ofproperty owned by the party as indicated by public records, a valuationof a set of property owned by the party, a bankruptcy condition of theparty, a foreclosure status of the party, a contractual default statusof the party, a regulatory violation status of the party, a criminalstatus of the party, an export controls status of the party, an embargostatus of the party, a tariff status of the party, a tax status of theparty, a credit report of the party, a credit rating of the party, a website rating of the party, a set of customer reviews for a product of theparty, a social network rating of the party, a set of credentials of theparty, a set of referrals of the party, a set of testimonials for theparty, a set of behavior of the party, a location of the party, ageolocation of the party, a judicial location of the party, and thelike. The system may include a smart contract circuit 8424 structured tocreate a smart lending contract 8426 for the loan 8408. The smartcontract circuit may be structured to specify terms and conditions inthe smart lending contract, wherein one of a term or a condition in thesmart lending contract governs one of loan-related events orloan-related activities. The system may include an automated agentcircuit 8428 structured to automatically perform a loan-related action8416 in response to the acquired data, wherein the loan-related actionis a change in an interest rate for the loan, and wherein the smartcontract circuit is further structured to update the smart lendingcontract with the changed interest rate. The automated agent circuit maybe structured to identify an event relevant to the loan (e.g., a valueof the loan, a condition of collateral of the loan, or an ownership ofcollateral of the loan), based, at least in part, on the received data.The automated agent circuit may be structured to perform, in response tothe event relevant to the loan, an action selected from the list ofactions, such as offering the loan, accepting the loan, underwriting theloan, setting an interest rate for the loan, deferring a paymentrequirement, modifying an interest rate for the loan, validating titlefor at least one of the set of items of collateral, assessing the valueof at least one of the set of items of collateral, initiating inspectionof at least one of the set of items of collateral, setting or modifyingterms and conditions 8414 for the loan (e.g., a principal amount ofdebt, a balance of debt, a fixed interest rate, a variable interestrate, a payment amount, a payment schedule, a balloon payment schedule,a party, a guarantee, a guarantor, a security, a personal guarantee, alien, a duration, a covenant, a foreclose condition, a defaultcondition, and a consequence of default), providing a notice to one ofthe parties, providing a required notice to a borrower of the loan,foreclosing on a property subject to the loan, and the like. The loanmay include a loan type, such as an auto loan, an inventory loan, acapital equipment loan, a bond for performance, a capital improvementloan, a building loan, a loan backed by an account receivable, aninvoice finance arrangement, a factoring arrangement, a pay day loan, arefund anticipation loan, a student loan, a syndicated loan, a titleloan, a home loan, a venture debt loan, a loan of intellectual property,a loan of a contractual claim, a working capital loan, a small businessloan, a farm loan, a municipal bond, a subsidized loan, and the like.The acquired data may be related to the set of items of collateral suchas a vehicle, a ship, a plane, a building, a home, a real estateproperty, an undeveloped land property, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,a tool, an item of machinery, an item of personal property, and thelike. The system may include a valuation circuit 8420 structured todetermine, based on the acquired data and a valuation model 8422, avalue for at least one of the set of items of collateral. The valuationcircuit may include a valuation model improvement circuit 8430, wherethe valuation model improvement circuit modifies the valuation modelbased on a first set of valuation determinations 8432 for a first set ofitems of collateral and a corresponding set of loan outcomes having thefirst set of items of collateral as security. The valuation modelimprovement circuit may include a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, a hybrid system including at least two ofthe foregoing, and the like. The smart contract circuit may be furtherstructured to determine a term or a condition for the smart lendingcontract based on the value for the at least one of the set of items ofcollateral and modify the smart lending contract to include the term orthe condition, modify a term or condition of the loan based on themarketplace information for offset items of collateral relevant to thevalue of the item of collateral, and the like. The system may include acollateral classification circuit 8438 structured to identify a group ofoff-set items of collateral, wherein each member of the group of off-setitems 8440 of collateral and at least one of the set of items ofcollateral share a common attribute (e.g., a category of the item, anage of the item, a condition of the item, a history of the item, anownership of the item, a caretaker of the item, a security of the item,a condition of an owner of the item, a lien on the item, a storagecondition of the item, a geolocation of the item, a jurisdictionallocation of the item, and the like). The valuation circuit may furtherinclude a market value data collection circuit 8434 structured tomonitor and report marketplace information 8436 for offset items ofcollateral relevant to the value of the item of collateral, monitorpricing or financial data for the offset items of collateral in a publicmarketplace, and the like, and report the monitored pricing or financialdata.

FIG. 85 depicts a method 8500 including acquiring data, from publicsources, related to at least one of a set of parties to a loan, wherethe public sources of information may be selected from the list ofinformation sources consisting of a website, a news article, a socialnetwork, and crowdsourced information 8502. The method may includecreating a smart lending contract 8504. The method may includeperforming a loan-related action in response to the acquired data,wherein the loan-related action is a change in an interest rate for theloan 8506. The method may include updating the smart lending contractwith the changed interest rate 8508. The method may include receivingcollateral-related data related to a set of items of collateral actingas security for the loan 8510 and determining a condition of at leastone of the set of items of collateral, wherein the change in theinterest rate is further based on the condition of the at least one ofthe set of items of collateral 8512. The method may include identifyingan event relevant to the loan based, at least in part, on thecollateral-related data 8514 and performing, in response the eventrelevant to the loan, an action 8518, such as offering the loan,accepting the loan, underwriting the loan, setting an interest rate forthe loan, deferring a payment requirement, modifying an interest ratefor the loan, validating title for at least one of the set of items ofcollateral, assessing a value of at least one of the set of items ofcollateral, initiating inspection of at least one of the set of items ofcollateral, setting or modifying terms and conditions for the loan,providing a notice to one of the parties, providing a required notice toa borrower of the loan, foreclosing on a property subject to the loan,and the like. The method may include determining, based on at least oneof the collateral-related data or the acquired data and a valuationmodel, a value for at least one of the set of items of collateral. Themethod may include determining at least one of a term or a condition forthe smart lending contract based on the value for the at least one ofthe set of items of collateral. The method may include modifying thesmart lending contract to include the at least one of the term or thecondition. The method may include modifying the valuation model based ona first set of valuation determinations for a first set of items ofcollateral and a corresponding set of loan outcomes having the first setof items of collateral as security. The method may include identifying agroup of off-set items of collateral, wherein each member of the groupof off-set items of collateral and at least one of the set of items ofcollateral share a common attribute 8520, monitoring one of pricing dataor financial data for least one of the group off-set items of collateralin at least one public marketplace 8522, reporting the monitored datafor the at least one of the group off-set items of collateral 8524, andmodifying a term or condition of the loan based the reported monitoreddata 8528.

FIG. 86 depicts a system 8600 including a data collection circuit 8620structured to receive data 8602 relating to a status 8604 of a loan 8612and data relating to a set of items of collateral 8606 acting assecurity for the loan. The data collection circuit may monitor one ormore of the loan entities with a system such as an Internet of Thingssystem, a camera system, a networked monitoring system, an internetmonitoring system, a mobile device system, a wearable device system, auser interface system, and an interactive crowdsourcing system 8632. Forinstance, an interactive crowdsourcing system may include a userinterface 8634, the user interface configured to solicit informationrelated to one or more of the loan entities from a crowdsourcing site8618, and where the user interface is structured to allow one or more ofthe loan entities to input information on one or more of the loanentities. In another instance, a networked monitoring system may includea network search circuit 8621 structured to search publicly availableinformation sites for information related to one or more of the loanentities. The system may include a blockchain service circuit 8644structured to maintain a secure historical ledger 8646 of events relatedto the loan, such as to interpret a plurality of access control features8608 corresponding to a plurality of parties 8610 associated with theloan. The system may include a loan evaluation circuit 8648 structuredto determine a loan status based on the received data. The datacollection circuit may receive data related to one or more loan entities8614, where the loan evaluation circuit may determine compliance with acovenant based on the data related to the one or more of the loanentities. The loan evaluation circuit may be structured to determine astate of performance for a condition of the loan based on the receiveddata and a status of the one or more of the loan entities, and whereinthe determination of the loan status is determined based in part on thestatus of the at least one or more of the loan entities and the state ofperformance of the condition for the loan. For instance, the conditionof the loan may relate to at least one of a payment performance and asatisfaction on a covenant. The data collection circuit may include amarket data collection circuit 8636 structured to receive financial data8638 regarding at least one of the plurality of parties associated withthe loan. The loan evaluation circuit may be structured to determine afinancial condition of the at least one of the plurality of partiesassociated with the loan based on the received financial data, where theat least one of the plurality of parties may be a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, a bond issuer, a bondpurchaser, an unsecured lender, a guarantor, a provider of security, aborrower, a debtor, an underwriter, an inspector, an assessor, anauditor, a valuation professional, a government official, an accountant,and the like. The received financial data may relate to an attribute ofthe entity for one of the plurality of parties, such as a publiclystated valuation of the party, a set of property owned by the party asindicated by public records, a valuation of a set of property owned bythe party, a bankruptcy condition of the party, a foreclosure status ofthe entity, a contractual default status of the entity, a regulatoryviolation status of the entity, a criminal status of the entity, anexport controls status of the entity, an embargo status of the entity, atariff status of the entity, a tax status of the entity, a credit reportof the entity, a credit rating of the entity, a web site rating of theentity, a set of customer reviews for a product of the entity, a socialnetwork rating of the entity, a set of credentials of the entity, a setof referrals of the entity, a set of testimonials for the entity, a setof behavior of the entity, a location of the entity, a geolocation ofthe entity, and the like. The system may include a smart contractcircuit 8626 structured to create a smart lending contract 8628 for theloan. The smart contract circuit may be structured to determine a termor a condition for the smart lending contract based on the value for theat least one of the set of items of collateral and modify the smartlending contract to include the term or the condition, where the termsand conditions may be a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, a consequence ofdefault, and the like. The system may include an automated agent circuit8630 structured to perform a loan-action 8616 based on the loan status,where the blockchain service circuit may be structured to update thehistorical ledger of events with the loan action. The system may includea valuation circuit 8622 structured to determine, based on the receiveddata and a valuation model 8624, a value for at least one of the set ofitems of collateral. The valuation circuit may include a valuation modelimprovement circuit 8640, where the valuation model improvement circuitmodifies the valuation model based on a first set of valuationdeterminations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security. The valuation model improvement circuit mayinclude a machine learning system, a model-based system, a rule-basedsystem, a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system. The valuation circuit may include amarket value data collection circuit 8642 structured to monitor andreport marketplace information for offset items of collateral relevantto the value of the item of collateral. The market value data collectioncircuit may be further structured to monitor pricing or financial datafor the offset items of collateral in a public marketplace, such as toreport the monitored pricing or financial data. The smart contractcircuit may be further structured to modify a term or condition of theloan based on the marketplace information for offset items of collateralrelevant to the value of the item of collateral. The system may includea collateral classification circuit 8650 structured to identify a groupof off-set items of collateral 8652, where each member of the group ofoff-set items of collateral and at least one of the set of items ofcollateral may share a common attribute. The common attribute may be acategory of the item of collateral, an age of the item of collateral, acondition of the item of collateral, a history of the item ofcollateral, an ownership of the item of collateral, a caretaker of theitem of collateral, a security of the item of collateral, a condition ofan owner of the item of collateral, a lien on the item of collateral, astorage condition of the item of collateral, a geolocation of the itemof collateral, a jurisdictional location of the item of collateral, andthe like.

FIG. 87 depicts a method 8700 including maintaining a secure historicalledger of events related to a loan 8702, receiving data relating to astatus of the loan 8704, receiving data related to a set of items ofcollateral acting as security of the loan 8708, determining a status ofthe loan 8710, performing a loan-action based on the loan status 8712,and updating the historical ledger of events related to the loan 8714.The method may further include receiving data related to one or moreloan entities 8718 and determining compliance with a covenant of theloan based on the data received 8720. The method may further includedetermining a state of performance for a condition of the loan, wherethe determination of the loan status is based on part on the state ofperformance of the condition of the loan. The method may further includereceiving financial data related to at least one party to the loan. Themethod may further include determining a financial condition of the atleast one party to the loan based on the financial data. The method mayfurther include determining a value for at least one set of items ofcollateral based on the received data and a valuation model. The methodmay further include determining at least one of a term or a conditionfor the loan based on the value of the at least one of the items ofcollateral 8722 and modifying a smart lending contract to include the atleast one of the term or the condition 8724. The method may includeidentifying a group of off-set items of collateral, where each member ofthe group of off-set items of collateral and at least one of the set ofitems of collateral share a common attribute 8728, receiving datarelated to the group of off-set items of collateral, wherein thedetermination of the value for the at least one set of items ofcollateral is partially based on the received data related to the groupof off-set items of collateral 8730.

Referring to FIG. 88 , an illustrative and non-limiting example smartcontract system for managing collateral for a loan 8800 is depicted. Theexample system may include a controller 8801. The controller 8801 mayinclude a data collection circuit 8812 structured to monitor a status ofa loan 8830 and of a collateral 8828 for the loan, and severalartificial intelligence circuits including a smart contract circuit 8822structured to process information from the data collection circuit 8812and automatically initiate at least one of a substitution, a removal, oran addition of one or items from the collateral for the loan based onthe information and a smart lending contract 8831 in response to atleast one of the status of the loan or the status of the collateral forthe loan; and a blockchain service circuit 8858 structured to interpreta plurality of access control features 8880 corresponding to at leastone party associated with the loan and record the at least onesubstitution, removal, or addition in a distributed ledger 8840 for theloan. The data collection circuit may further include at least one othersystem 8862 selected from the systems consisting of: an Internet ofThings system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem.

A status of the loan 8830 may be determined based on the status of atleast one of an entity (e.g. user 8806) related to the loan and a stateof a performance of a condition for the loan. State of the performanceof the condition may relate to at least one of a payment performance ora satisfaction of a covenant for the loan. The status of the loan may bedetermined based on a status of at least one entity related to the loanand a state of performance of a condition for the loan, and theperformance of the condition may relate to at least one of a paymentperformance or a satisfaction of a covenant for the loan. The datacollection circuit 8812 may be further structured to determinecompliance with the covenant by monitoring the at least one entity. Whenthe at least one entity is a party to the loan, the data collectioncircuit 8812 may monitor a financial condition of at least one entitythat is a party to the loan. The condition for the loan may include afinancial condition for the loan, and wherein the state of performanceof the financial condition may be determined based on an attributeselected from the attributes consisting of: a publicly stated valuationof the at least one entity, a property owned by the at least one entityas indicated by public records, a valuation of a property owned by theat least one entity, a bankruptcy condition of the at least one entity,a foreclosure status of the at least one entity, a contractual defaultstatus of the at least one entity, a regulatory violation status of theat least one entity, a criminal status of the at least one entity, anexport controls status of the at least one entity, an embargo status ofthe at least one entity, a tariff status of the at least one entity, atax status of the at least one entity, a credit report of the at leastone entity, a credit rating of the at least one entity, a web siterating of the at least one entity, a plurality of customer reviews for aproduct of the at least one entity, a social network rating of the atleast one entity, a plurality of credentials of the at least one entity,a plurality of referrals of the at least one entity, a plurality oftestimonials for the at least one entity, a behavior of the at least oneentity, a location of the at least one entity, a geolocation of the atleast one entity, and a relevant jurisdiction for the at least oneentity.

The party to the loan may be selected from the parties consisting of: aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, a bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,and an accountant.

The data collection circuit 8812 may be further structured to monitorthe status of the collateral of the loan based on at least one attributeof the collateral selected from the attributes consisting of: a categoryof the collateral, an age of the collateral, a condition of thecollateral, a history of the collateral, a storage condition of thecollateral, and a geolocation of the collateral.

The controller 88101 may include a valuation circuit 8844 which may bestructured to use a valuation model 8852 to determine a value for thecollateral based on the status of the collateral for the loan. The smartcontract circuit 8822 may initiate the at least one substitution,removal, or addition of one or more items from the collateral for theloan to maintain a value of collateral within a predetermined range.

The valuation circuit 8844 may further include a transactions outcomeprocessing circuit 8864 structured to interpret outcome data 8810relating to a transaction in collateral and iteratively improve 8850 thevaluation model in response to the outcome data.

The valuation circuit 8844 may further include a market value datacollection circuit 8848 structured to monitor and report on marketplaceinformation relevant to a value of collateral. The market value datacollection circuit 8848 may monitor pricing data or financial data foran offset collateral item 8834 in at least one public marketplace.

The market value data collection circuit 8848 is further structured toconstruct a set of offset collateral items 8834 used to value an item ofcollateral that may be constructed using a clustering circuit 8832 ofthe controller 88101 based on an attribute of the collateral. Theattributes may be selected from among a category of the collateral, anage of the collateral, a condition of the collateral, a history of thecollateral, a storage condition of the collateral, and a geolocation ofthe collateral.

Terms and conditions 8824 for the loan may include at least one memberselected from the group consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

The smart contract circuit may further include or be in communicationwith a loan management circuit 8860 structured to specify terms andconditions of the smart lending contract 8831 that governs at least oneof loan terms and conditions, a loan-related event 8839, or aloan-related activity or action 8838.

Referring to FIG. 89 , an example smart contract method for managingcollateral for a loan is depicted. The example method may includemonitoring a status of a loan and of a collateral for the loan (step8902); automatically initiating at least one of a substitution, aremoval, or an addition of one or more items from the collateral for theloan based on the information (step 8908); interpreting a plurality ofaccess control features corresponding to at least one party associatedwith the loan (step 8910); and recording the at least one substitution,removal, or addition in a distributed ledger for the loan (step 8912). Astatus of the loan may be determined based on the status of at least oneof an entity related to the loan and a state of a performance of acondition for the loan.

The method may further include interpreting information from themonitoring (step 8914) and determining a value with a valuation modelfor a set of collateral based on at least one of the status of the loanor the collateral for the loan (step 8918). The at least onesubstitution, removal, or addition may be to maintain a value ofcollateral within a predetermined range. The method may further includeinterpreting outcome data relating to a transaction of one of thecollateral or an offset collateral (step 8920) and iteratively improvingthe valuation model in response to the outcome data (step 8922). Themethod may further include monitoring and reporting on marketplaceinformation relevant to a value of collateral (step 8924).

The method may further include monitoring pricing data or financial datafor an offset collateral item in at least one public marketplace (step8928).

The method may further include specifying terms and conditions of asmart contract that governs at least one of terms and conditions for theloan, a loan-related event, or a loan-related activity (step 8930).

Referring to FIG. 90 , an illustrative and non-limiting example of acrowdsourcing system for validating conditions of collateral or aguarantor for a loan 9000 is depicted. The example system may include acontroller 9001. The controller 9001 may include a data collectioncircuit 9012, a user interface 9054, and several artificial intelligencecircuits including a smart contract circuit 9022, robotic processautomation circuit 9074, a crowdsourcing request circuit 9060, acrowdsourcing communications circuit 9062, a crowdsourcing publishingcircuit 9064, and a blockchain service circuit 9058.

The crowdsourcing request circuit 9060 may be structured to configure atleast one parameter of a crowdsourcing request 9068 related to obtaininginformation 9004 on a condition of a collateral 9011 for a collateral9002 for a loan 9030 or a condition of a guarantor for the loan 9096. Itmay also enable a workflow by which a human user enters the at least oneparameter to establish the crowdsourcing request. The at least oneparameter may include a type of requested information, the reward, and acondition for receiving the reward. The reward may be selected from therewards consisting of: a financial reward, a token, a ticket, acontractual right, a cryptocurrency, a plurality of reward points, acurrency, a discount on a product or service, and an access right.

The crowdsourcing publishing circuit 9064 may be configured to publishthe crowdsourcing request 9068 to a group of information suppliers.

The crowdsourcing communications circuit 9062 may be structured tocollect and process at least one response 9072 from the group ofinformation suppliers 9070 and to provide a reward 9080 to at least oneof the group of information suppliers in response to a successfulinformation supply event 9098.

The crowdsourcing communications circuit 9062 further includes a smartcontract circuit 9022 structured to manage the reward 9080 bydetermining the successful information supply event 9098 in response tothe at least one parameter configured for the crowdsourcing request 9068and to automatically allocate the reward 9080 to the at least one of thegroups of information suppliers 9070 in response to the successfulinformation supply event 9098. It may also be structured to process theat least one response 9072 and, in response, automatically undertake anaction related to the loan. The action may be at least one of aforeclosure action, a lien administration action, an interest-ratesetting action, a default initiation action, a substitution ofcollateral, or a calling of the loan.

The loan 9030 may include at least one loan type selected from the loantypes consisting of: an auto loan, an inventory loan, a capitalequipment loan, a bond for performance, a capital improvement loan, abuilding loan, a loan backed by an account receivable, an invoicefinance arrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The crowdsourcing request circuit 9060 may be further structured toconfigure at least one further parameter of the crowdsourcing request9068 to obtain information on a condition of a collateral 9011 for theloan.

The collateral 9002 may include at least one item selected from theitems consisting of: a vehicle, a ship, a plane, a building, a home,real estate property, undeveloped land, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

The condition of collateral 9011 may be determined based on an attributeselected from the attributes consisting of: a quality of the collateral,a condition of the collateral, a status of a title to the collateral, astatus of a possession of the collateral, and a status of a lien on thecollateral. When the collateral is an item, the condition may bedetermined based on an attribute selected from the attributes consistingof: a new or used status of the item, a type of the item, a category ofthe item, a specification of the item, a product feature set of theitem, a model of the item, a brand of the item, a manufacturer of theitem, a status of the item, a context of the item, a state of the item,a value of the item, a storage location of the item, a geolocation ofthe item, an age of the item, a maintenance history of the item, a usagehistory of the item, an accident history of the item, a fault history ofthe item, an ownership of the item, an ownership history of the item, aprice of a type of the item, a value of a type of the item, anassessment of the item, and a valuation of the item.

The blockchain service circuit 9058 may be structured to recordidentifying information and the at least one parameter of thecrowdsourcing request, the at least one response to the crowdsourcingrequest, and a reward description in a distributed ledger 9040.

The robotic process automation circuit 9074 may be structured, based ontraining on a training data set 9078 comprising human user interactionswith at least one of the crowdsourcing request circuit or thecrowdsourcing communications circuit, to configure the crowdsourcingrequest based on at least one attribute of the loan. The at least oneattribute of the loan may be obtained from a smart contract circuit 9022that manages the loan. The training data set 9078 may further includeoutcomes from a plurality of crowdsourcing requests.

The robotic process automation circuit 9074 may be further structured todetermine a reward 9080.

The robotic process automation circuit 9074 may be further structured todetermine at least one domain to which the crowdsourcing publishingcircuit 9064 publishes the crowdsourcing request 9068.

Referring to FIG. 91 , provided herein is a crowdsourcing method forvalidating conditions of collateral or a guarantor for a loan. At leastone parameter of a crowdsourcing request may be configured to obtaininformation on a condition of a collateral for a loan or a condition ofa guarantor for the loan (step 9102). The crowdsourcing request may bepublished to a group of information suppliers (step 9104). At least oneresponse to the crowdsourcing request may be collected and processed(step 9108). A reward may be provided to at least one successfulinformation supplier of the group of information suppliers in responseto a successful information supply event (step 9110). A rewarddescription may be published to at least a portion of the group ofinformation suppliers in response to the successful information supplyevent (step 9112). The reward may be automatically allocated to at leastone of the group of information suppliers in response to the successfulinformation supply event (step 9130). The method may further includerecording identifying information and the at least one parameter of thecrowdsourcing request, the at least one response to the crowdsourcingrequest, and a reward description in a distributed ledger for thecrowdsourcing request (step 9114). A graphical user interface may beconfigured to enable a workflow by which a human user enters the atleast one parameter to establish the crowdsourcing request (step 9118).An action related to the loan may be automatically undertaken inresponse to the successful information supply event (step 9120). Arobotic process automation circuit may be trained on a training data setcomprising a plurality of outcomes corresponding to a plurality of thecrowdsourcing requests and operating the robotic process automationcircuit to iteratively improve the crowdsourcing request (step 9122). Atleast one attribute of the loan may be provided to the robotic processautomation circuit in order to configure the crowdsourcing request (step9124). Configuring the crowdsourcing request may include determining areward. At least one attribute of the loan may be provided to therobotic process automation circuit in order to determine at least onedomain to which to publish the crowdsourcing request (step 9128).

Referring to FIG. 92 , an illustrative and non-limiting example smartcontract system for modifying a loan 9200 is depicted. The examplesystem may include a controller 9201. The controller 9201 may include adata collection circuit 9212, a valuation circuit 9244, and severalartificial intelligence circuits 9242 including a smart contract circuit9222, a clustering circuit 9232, a jurisdiction definition circuit 9298,and a loan management circuit 9260. The data collection circuit 9212 maybe structured to determine location information corresponding to eachone of a plurality of entities involved in a loan. The jurisdictiondefinition circuit 9298 may be structured to determine a jurisdictionfor at least one of the plurality of entities in response to thelocation information. The smart contract circuit 9222 may be structuredto automatically undertake a loan-related action 9238 for the loan basedat least in part on the jurisdiction for at least one of the pluralityof entities.

The smart contract circuit 9222 may be further structured toautomatically undertake the loan-related action in response to a firstone of the plurality of entities being in a first jurisdiction, and asecond one of the plurality of entities being in a second jurisdiction.

The smart contract circuit 9222 may be further structured toautomatically undertake the loan-related action in response to one ofthe plurality of entities moving from a first jurisdiction to a secondjurisdiction.

The loan-related action 9238 may include at least one loan-relatedaction selected from the loan-related actions consisting of: offeringthe loan, accepting the loan, underwriting the loan, setting an interestrate for the loan, deferring a payment requirement, modifying aninterest rate for the loan, validating title for collateral, recording achange in title, assessing a value of collateral, initiating inspectionof collateral, calling the loan, closing the loan, setting terms andconditions for the loan, providing notices required to be provided to aborrower, foreclosing on property subject to the loan, and modifyingterms and conditions for the loan.

The smart contract circuit 9222 may be further structured to process aplurality of jurisdiction-specific regulatory requirements 9268, such asrequirements related to notice, and to provide an appropriate notice toa borrower based on a jurisdiction corresponding to at least one entityselected from the entities consisting of a lender, a borrower, fundsprovided via the loan, a repayment of the loan, or a collateral for theloan.

The smart contract circuit 9222 may be further structured to process aplurality of jurisdiction-specific regulatory requirements 9268, such asrequirement related to foreclosure, and to provide an appropriateforeclosure notice to a borrower based on a jurisdiction of at least oneof a lender, a borrower, funds provided via the loan, a repayment of theloan, and a collateral for the loan.

The smart contract circuit 9222 may be further structured to process aplurality of jurisdiction-specific rules 9270 for setting terms andconditions 9224 of the loan and to configure a smart contract 9231 basedon a jurisdiction corresponding to at least one entity selected from theentities consisting of: a borrower, funds provided via the loan, arepayment of the loan, and a collateral for the loan.

The smart contract circuit 9222 may be further structured to determinean interest rate for the loan to cause the loan to comply with a maximuminterest rate limitation applicable in a jurisdiction corresponding to aselected one of the plurality of entities.

The data collection circuit 9212 may be further structured to monitor acondition of a collateral for the loan, wherein the smart contractcircuit is further structured to determine the interest rate for theloan in response to the condition of the collateral for the loan.

The data collection circuit 9212 may be further structured to monitor anattribute of at least one of the plurality of entities that are party tothe loan, wherein the smart contract circuit is further structured todetermine the interest rate for the loan in response to the attribute.

The smart contract circuit 9222 may further include a loan managementcircuit 9260 for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions 9224, loan-relatedevents 9239 or loan-related activities 9272.

The loan may include at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring management, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

Terms and conditions for the loan may each include at least one memberselected from the group consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

The data collection circuit 9212 may further include at least one othersystem 9262 selected from the systems consisting of: an Internet ofThings system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem.

The valuation circuit 9244 may be structured to use a valuation model9252 to determine a value for a collateral for the loan based on thejurisdiction corresponding to at least one of the plurality of entities.The valuation model 9252 may be a jurisdiction-specific valuation model,and wherein the jurisdiction corresponding to at least one of theplurality of entities comprises a jurisdiction corresponding to at leastone entity selected from the entities consisting of: a lender, aborrower, funds provided pursuant to the loan, a delivery location offunds provided pursuant to the loan, a payment of the loan, and acollateral for the loan.

At least one of the terms and conditions for the loan may be based onthe value of the collateral for the loan.

The collateral may include at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty.

The valuation circuit 9244 may further include a transactions outcomeprocessing circuit 9264 structured to interpret outcome data relating toa transaction in collateral and iteratively improve 9250 the valuationmodel in response to the outcome data.

The valuation circuit 9244 may further include a market value datacollection circuit 9248 structured to monitor and report on marketplaceinformation relevant to a value of collateral. The market value datacollection circuit may monitor pricing or financial data for an offsetcollateral item in at least one public marketplace. A set of offsetcollateral items 9234 for valuing an item of collateral may beconstructed using the clustering circuit 9232 based on an attribute ofthe collateral. The attribute may be selected from among a category ofthe collateral, an age of the collateral, a condition of the collateral,a history of the collateral, a storage condition of the collateral, anda geolocation of the collateral.

Referring to FIG. 93 , provided herein is a smart contract method 9300for modifying a loan. An example method may include monitoring locationinformation corresponding to each one of a plurality of entitiesinvolved in a loan (step 9302); processing a location information aboutthe entities and automatically undertaking a loan-related action for theloan based at least in part on the location information (step 9304). Theexample method includes processing a number of jurisdiction-specificregulatory notice requirements and providing an appropriate notice to aborrower based on a location of the lender, a borrower, funds providedvia the loan, a repayment of the loan, and/or a collateral for the loan(step 9308). The example method includes processing a number ofjurisdiction-specific rules for setting terms and conditions of the loanand configuring a smart contract based on a location of the lender, aborrower, funds provided via the loan, a repayment of the loan, and/or acollateral for the loan (step 9310). The example method further includesdetermining an interest rate of the loan to cause the loan to complywith a maximum interest rate limitation applicable in a jurisdiction(step 9312). The example method includes monitoring at least one of acondition of a number of collateral items for the loan or an attributeof one of the entities that are a party to the loan, where the conditionor the attribute is used to determine an interest rate (step 9314). Theexample method includes specifying terms and conditions of smartcontract(s) that govern at least one of the terms and conditions,loan-related events, or loan-related activities (step 9318). The examplemethod includes interpreting the location information and using avaluation model to determine a value for a number of collateral itemsfor the loan based on the location information (step 9320). The examplemethod includes interpreting outcome data relating to a transaction incollateral and iteratively improving the valuation model in response tothe outcome data (step 9322). The example method includes monitoring andreporting on marketplace information relevant to a value of collateral(step 9324).

A plurality of jurisdiction-specific requirements based on ajurisdiction of a relevant one of the plurality of entities may beprocessed and performing at least one operation may be selected from theoperations consisting of: providing an appropriate notice to a borrowerin response to the plurality of jurisdiction-specific requirementscomprising regulatory notice requirements; setting specific rules forsetting terms and conditions of the loan in response to the plurality ofjurisdiction-specific requirements comprising jurisdiction-specificrules for terms and conditions of the loan; determining an interest ratefor the loan to cause the loan to comply with a maximum interest ratelimitation in response to the plurality of jurisdiction-specificrequirements comprising a maximum interest rate limitation; and whereinthe relevant one of the plurality of entities comprises at least oneentity selected from the entities consisting of: a lender, a borrower,funds provided pursuant to the loan, a repayment of the loan, and acollateral for the loan (step 9308).

At least one of a condition of a plurality of collateral for the loan oran attribute of at least one of the plurality of entities that are partyto the loan may be monitored, wherein the condition or the attribute isused to determine an interest rate (step 9314).

A valuation model may be operated to determine a value for a collateralfor the loan based on the jurisdiction for at least one of the pluralityof entities (step 9320).

Outcome data relating to a transaction in collateral may be interpretedand the valuation model may be iteratively improved in response to theoutcome data (step 9322).

Referring now to FIG. 94 , an illustrative and non-limiting examplesmart contract system for modifying a loan 9400 is depicted. The examplesystem may include a controller 9401. The controller 94101 may include adata collection circuit 9412, a valuation circuit 9444, and severalartificial intelligence circuits 9442 including a smart contract circuit9422, a clustering circuit 9432, and a loan management circuit 9460.

The data collection circuit 9412 may be structured to monitor andcollect information about at least one entity 9498 involved in a loan9430. The smart contract circuit 9422 may be structured to automaticallyrestructure a debt related to the loan based on the monitored andcollected information about the at least one entity involved in theloan. The monitored and collected information may include a condition ofa collateral 9411 for the loan, or according to at least one rule thatis based on a covenant of the loan and wherein the restructuring occursupon an event that is determined with respect to the at least one entitythat relates to the covenant, or restructuring may be based on anattribute 9494 of the at least one entity that is monitored by the datacollection circuit. The event may be a failure of collateral for theloan to exceed a required fractional value of a remaining balance of theloan or a default of a buyer with respect to the covenant.

The smart contract circuit 9422 may be further structured to determinethe occurrence of an event based on a covenant of the loan and themonitored and collected information about the at least one entityinvolved in the loan and to automatically restructure the debt inresponse to the occurrence of the event.

The smart contract circuit 9422 may further include a loan managementcircuit 9460 which may be structured to specify terms and conditions ofa smart contract that governs at least one of loan terms and conditions9424, a loan-related event 9439, or a loan-related activity 9472.

The loan may include at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

Terms and conditions for the loan may include at least one memberselected from the group consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

The data collection circuit 9412 may further include at least one othersystem 9462 selected from the systems consisting of: an Internet ofThings system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem.

The valuation circuit 9444 may be structured to use a valuation model9452 to determine a value for a collateral based on the monitored andcollected information about the at least one entity involved in theloan. The smart contract circuit may be further structured toautomatically restructure the debt based on the value for thecollateral.

The collateral may be at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty.

The valuation circuit 9444 may further include a transactions outcomeprocessing circuit 9464 structured to interpret outcome data 9410relating to a transaction in collateral and iteratively improve 9450 thevaluation model in response to the outcome data.

The valuation circuit 9444 may further include a market value datacollection circuit 9448 structured to monitor and report on marketplaceinformation relevant to a value of collateral. The market value datacollection circuit 9448 monitors pricing or financial data for an offsetcollateral item 9434 in at least one public marketplace. A set of offsetcollateral items 9434 for valuing an item of collateral may beconstructed using a clustering circuit 9432 based on an attribute of thecollateral. The attribute may be selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral, and ageolocation of the collateral.

Referring now to FIG. 95 , an illustrative and non-limiting examplesmart contract method for modifying a loan 9500 is depicted. The methodincludes monitoring and collecting information about at least one entityinvolved in a loan (step 9502); processing information from themonitoring of the at least one entity (step 9504); and automaticallyrestructuring a debt related to the loan based on the monitored andcollected information about the at least one entity (step 9508).Determining the occurrence of an event may be based on a covenant of theloan and the monitored and collected information about the at least oneentity involved in the loan and automatically restructuring the debt inresponse to the occurrence of the event (step 9509).

Terms and conditions of a smart contract that governs at least one ofloan terms and conditions, a loan-related event, and a loan-relatedactivity may be specified (step 9510). Operating a valuation model todetermine a value for a collateral based on the monitored and collectedinformation about the at least one entity involved in the loan may beperformed (step 9512). Outcome data relating to a transaction incollateral may be interpreted and the valuation model may be iterativelyimproved in response to the outcome data (step 9514). The method mayfurther include monitoring and reporting on marketplace informationrelevant to a value of collateral (step 9518). Pricing or financial datafor an offset collateral item may be monitored in at least one publicmarketplace (step 9520). A set of offset collateral items for valuing anitem of collateral may be constructed using a similarity clusteringalgorithm based on an attribute of the collateral (step 9522).

Referring now to FIG. 96 , an illustrative and non-limiting examplesmart contract system for modifying a loan 9600 is depicted. The examplesystem may include a controller 9601. The controller 9601 may include adata collection circuit 9612, a social networking input circuit 9644, asocial network data collection circuit 9632, and several artificialintelligence circuits 9642 including a smart contract circuit 9622, aguarantee validation circuit 9698, and a robotic process automationcircuit 9648.

The social network data collection circuit 9632 may be structured tocollect data using a plurality of algorithms that are configured tomonitor social network information about an entity 9664 involved in aloan 9630 in response to the loan guarantee parameter. The socialnetworking input circuit 9644 may be structured to interpret a loanguarantee parameter. The guarantee validation circuit 9698 may bestructured to validate a guarantee for the loan in response to themonitored social network information.

The loan guarantee parameter may include a financial condition of theentity, wherein the entity is a guarantor for the loan.

The guarantee validation circuit 9698 may be further structured todetermine the financial condition, which may be determined based on atleast one attribute selected from the attributes consisting of: apublicly stated valuation of the entity, a property owned by the entityas indicated by public records, a valuation of a property owned by theentity, a bankruptcy condition of the entity, a foreclosure status ofthe entity, a contractual default status of the entity, a regulatoryviolation status of the entity, a criminal status of the entity, anexport controls status of the entity, an embargo status of the entity, atariff status of the entity, a tax status of the entity, a credit reportof the entity, a credit rating of the entity, a web site rating of theentity, a plurality of customer reviews for a product of the entity, asocial network rating of the entity, a plurality of credentials of theentity, a plurality of referrals of the entity, a plurality oftestimonials for the entity, a plurality of behaviors of the entity, alocation of the entity, a jurisdiction of the entity, and a geolocationof the entity.

The loan may include at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The data collection circuit 9612 may be structured to obtain informationabout a condition 9611 of a collateral for the loan, wherein thecollateral comprises at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, an item of personalproperty, and wherein the guarantee validation circuit is furtherstructured to validate the guarantee of the loan in response to thecondition of the collateral for the loan.

The condition 9611 of collateral may include a condition attributeselected from the group consisting of a quality of the collateral, astatus of title to the collateral, a status of possession of thecollateral, a status of a lien on the collateral, a new or used status,a type, a category, a specification, a product feature set, a model, abrand, a manufacturer, a status, a context, a state, a value, a storagelocation, a geolocation, an age, a maintenance history, a usage history,an accident history, a fault history, an ownership, an ownershiphistory, a price, an assessment, and a valuation. Conditions may bestored as collateral data 9604.

The social networking input circuit 9644 may be further structured toenable a workflow by which a human user enters the loan guaranteeparameter to establish a social network data collection and monitoringrequest.

The smart contract circuit 9622 may be structured to automaticallyundertake an action related to the loan in response to the validation ofthe loan. The action related to the loan may be in response to the loanguarantee not being validated, wherein the action comprises at least oneaction selected from the actions consisting of: a foreclosure action, alien administration action, an interest-rate adjustment action, adefault initiation action, a substitution of collateral, a calling ofthe loan, and providing an alert to a second entity involved in theloan.

The robotic process automation circuit 9648 may be structured to, basedon iteratively training on a training data set 9646 comprising humanuser interactions with the social network data collection circuit,configure the loan guarantee parameter based on at least one attributeof the loan. The at least one attribute of the loan 9630 may be obtainedfrom a smart contract circuit that manages the loan.

The training data set 9646 may further include outcomes from a pluralityof social network data collection and monitoring requests performed bythe social network data collection circuit.

The robotic process automation circuit 9648 may be further structured todetermine at least one domain to which the social network datacollection circuit will apply.

Training may include training the robotic process automation circuit9648 to configure the plurality of algorithms.

Referring now to FIG. 97 , an illustrative and non-limiting example of asmart contract method for modifying a loan 9700 is depicted. A loanguarantee parameter may be interpreted (step 9701). Data may becollected using a plurality of algorithms that are configured to monitorsocial network information about an entity involved in a loan inresponse to the loan guarantee parameter (step 9702). A guarantee forthe loan may be validated in response to the monitored social networkinformation (step 9704). A workflow may be enabled by which a human userenters the loan guarantee parameter to establish a social network datacollection and monitoring request (step 9708). In response to thevalidation of the loan, an action related to the loan may be undertakenautomatically (step 9710). A robotic process automation circuit may beiteratively trained to configure a data collection and monitoring actionbased on at least one attribute of the loan, wherein the robotic processautomation circuit is trained on a training data set comprising at leastone of outcomes from or human user interactions with the plurality ofalgorithms (step 9712). At least one domain to which the plurality ofalgorithms will apply may be determined (step 9714).

Referring to FIG. 98 , an illustrative and non-limiting example of amonitoring system for validating conditions of a guarantee for a loan9800 is depicted. The example system may include a controller 9801. Thecontroller 9801 may include an Internet of Things data collection inputcircuit 9844, Internet of Things data collection circuit 9832, andseveral artificial intelligence circuits 9842 including a smart contractcircuit 9822, a guarantee validation circuit 9898, and a robotic processautomation circuit 9848.

The Internet of Things data collection input circuit 9844 may bestructured to interpret a loan guarantee parameter 9892. The Internet ofThings data collection circuit 9832 may be structured to collect datausing at least one algorithm that is configured to monitor Internet ofThings information collected from and about an entity 9864 involved in aloan 9830 in response to the loan guarantee parameter. The guaranteevalidation circuit 9898 may be structured to validate a guarantee forthe loan in response to the monitored IoT information.

The loan guarantee parameter 9892 may include a financial condition ofthe entity, wherein the entity is a guarantor for the loan. MonitoredIoT information includes at least one of a publicly stated valuation ofthe entity, a property owned by the entity as indicated by publicrecords, a valuation of a property owned by the entity, a bankruptcycondition of the entity, a foreclosure status of the entity, acontractual default status of the entity, a regulatory violation statusof the entity, a criminal status of an entity, an export controls statusof the entity, an embargo status of the entity, a tariff status of theentity, a tax status of the entity, a credit report of the entity, acredit rating of the entity, a website rating of the entity, a pluralityof customer reviews for a product of the entity, a social network ratingof the entity, a plurality of credentials of the entity, a plurality ofreferrals of the entity, a plurality of testimonials for the entity, aplurality of behaviors of the entity, a location of the entity, ajurisdiction of the entity, and a geolocation of the entity.

The loan may include at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The Internet of Things data collection circuit 9832 may be furtherstructured to obtain information about a condition of a collateral forthe loan, wherein the collateral comprises at least one item selectedfrom the items consisting of a vehicle, a ship, a plane, a building, ahome, a real estate property, an undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, and wherein the guaranteevalidation circuit 9898 is further structured to validate the guaranteeof the loan in response to the condition of the collateral for the loan.

The condition 9811 of collateral may include a condition attributeselected from the group consisting of a quality of the collateral, astatus of title to the collateral, a status of possession of thecollateral, a status of a lien on the collateral, a new or used status,a type, a category, a specification, a product feature set, a model, abrand, a manufacturer, a status, a context, a state, a value, a storagelocation, a geolocation, an age, a maintenance history, a usage history,an accident history, a fault history, an ownership, an ownershiphistory, a price, an assessment, and a valuation.

The Internet of Things data collection input circuit 9844 may be furtherstructured to enable a workflow by which a human user enters the loanguarantee parameter 9892 to establish an Internet of Things datacollection request.

The smart contract circuit 9822 may be structured to automaticallyundertake an action related to the loan in response to the validation ofthe loan. The action related to the loan may be in response to the loanguarantee not being validated and wherein the action comprises at leastone action selected from the actions consisting of: a foreclosureaction, a lien administration action, an interest-rate adjustmentaction, a default initiation action, a substitution of collateral, acalling of the loan, and providing an alert to second entity involved inthe loan.

The robotic process automation circuit 9848 may be structured to, basedon iteratively training on a training data set comprising human userinteractions with the Internet of Things data collection circuit,configure the loan guarantee parameter based on at least one attributeof the loan. The at least one attribute of the loan is obtained from asmart contract circuit that manages the loan. The training data set 9846may further include outcomes from a plurality of Internet of Things datacollection and monitoring requests performed by the Internet of Thingsdata collection circuit.

The robotic process automation circuit 9848 may be further structured todetermine at least one domain to which the Internet of Things datacollection circuit will apply.

Training may include training the robotic process automation circuit9848 to configure the at least one algorithm.

Referring to FIG. 99 , an illustrative and non-limiting examplemonitoring method for validating conditions of a guarantee for a loan9900 is depicted. The example method may include interpreting a loanguarantee parameter (step 9902); collecting data using a plurality ofalgorithms that are configured to monitor Internet of Things (IoT)information collected from and about an entity involved in a loan inresponse to the loan guarantee parameter (step 9904); and validating aguarantee for the loan in response to the monitored IoT information(step 9905).

The loan guarantee parameter may be configured to obtain informationabout a financial condition of the entity, wherein the entity is aguarantor for the loan (step 9908). The at least one algorithm may beconfigured to obtain information about a condition of a collateral forthe loan (step 9910), wherein the collateral comprises at least one itemselected from the items consisting of a vehicle, a ship, a plane, abuilding, a home, a real estate property, an undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property; and validatingthe guarantee for the loan further in response to the condition of thecollateral for the loan.

A workflow by which a human user enters the loan guarantee parameter toestablish an Internet of Things data collection request may be enabled(step 9912).

An action related to the loan may be undertaken automatically inresponse to the validation (step 9914).

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a foreclosureaction.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a lienadministration action.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises an interest-rateadjustment action.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a defaultinitiation action.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a substitution ofcollateral.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a calling of theloan.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises providing an alertto a second entity involved in the loan.

A robotic process automation circuit may be iteratively trained toconfigure an Internet of Things data collection and monitoring actionbased on at least one attribute of the loan, wherein the robotic processautomation circuit is trained on a training data set comprising at leastone of outcomes from or human user interactions with the plurality ofalgorithms (step 9918).

At least one domain to which the at least one algorithm will apply maybe determined (step 9920). Training may include training the roboticprocess automation circuit to configure the plurality of algorithms.

The training data set may further include outcomes from a set of IoTdata collection and monitoring requests.

Referring now to FIG. 100 , an illustrative and non-limiting example ofa robotic process automation system for negotiating a loan 10000 isdepicted. The example system may include a controller 10001. Thecontroller 10001 may include a data collection circuit 10012, avaluation circuit 10044, and several artificial intelligence circuits10042 including an automated loan classification circuit 10032, arobotic process automation circuit 10060, a smart contract circuit10084, and a clustering circuit 10082.

The data collection circuit 10012 may be structured to collect atraining set of interactions 10010 from at least one entity 10078related to at least one loan transaction. An automated loanclassification circuit 10032 may be trained on the training set ofinteractions 10010 to classify an at least one loan negotiation action.The robotic process automation circuit 10060 may be trained on atraining set of a plurality of loan negotiation actions 10074 classifiedby the automated loan classification circuit 10032 and a plurality ofloan transaction outcomes 10039 to negotiate a terms and conditions10024 of a new loan 10030 on behalf of a party to the new loan.

The data collection circuit may further include at least one othersystem 10062 selected from the systems consisting of: an Internet ofThings system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem. The at least one entity may be a party to the at least one loantransaction and may be selected from the entities consisting of: aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, a bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,and an accountant.

The automated loan classification circuit 10032 may include a systemselected from the systems consisting of: a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizingmap, a fuzzy logic system, a random walk system, a random forest system,a probabilistic system, a Bayesian system, and a simulation system.

The robotic process automation circuit 10060 may be further trained on aplurality of interactions of parties with a plurality of user interfacesinvolved in a plurality of lending processes.

The smart contract circuit 10084 may be structured to automaticallyconfigure a smart contract for the new loan 10030 based on an outcome ofthe negotiation.

A distributed ledger 10080 may be associated with the new loan 10030,wherein the distributed ledger 10080 is structured to record at leastone of an outcome and a negotiating event of the negotiation.

The new loan may include at least one loan type selected from the loantypes consisting of: an auto loan, an inventory loan, a capitalequipment loan, a bond for performance, a capital improvement loan, abuilding loan, a loan backed by an account receivable, an invoicefinance arrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The valuation circuit 10044 may be structured to use a valuation model10052 to determine a value for a collateral for the new loan. Thecollateral may include at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty.

The valuation circuit may further include a market value data collectioncircuit 10048 structured to monitor and report on marketplaceinformation relevant to a value of the collateral. The market value datacollection circuit 10048 may monitor pricing or financial data for anoffset collateral item 10034 in at least one public marketplace. A setof offset collateral items 10034 for valuing the collateral may beconstructed using a clustering circuit 10082 based on an attribute ofthe collateral. The attribute may be selected from among a category ofthe collateral, an age of the collateral, a condition of the collateral,a history of the collateral, a storage condition of the collateral, anda geolocation of the collateral. The terms and conditions 10024 for thenew loan may include at least one member selected from the groupconsisting of: a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

Referring now to FIG. 101 , an illustrative and non-limiting example ofa robotic process automation method for negotiating a loan 10000 isdepicted. The example method may include collecting a training set ofinteractions from at least one entity related to at least one loantransaction (step 10102); training an automated loan classificationcircuit on the training set of interactions to classify an at least oneloan negotiation action (step 10104); and training a robotic processautomation circuit on a training set of a plurality of loan negotiationactions classified by the automated loan classification circuit and aplurality of loan transaction outcomes to negotiate a terms andconditions of a new loan on behalf of a party to the new loan (step10108).

The robotic process automation circuit may be trained on a plurality ofinteractions of parties with a plurality of user interfaces involved ina plurality of lending processes (step 10110).

A smart contract for the new loan may be configured based on an outcomeof the negotiation (step 10112).

At least one of an outcome and a negotiating event of the negotiationmay be recorded in a distributed ledger associated with the new loan(step 10114).

A value for a collateral for the new loan may be determined using avaluation model (step 10118).

An example method may further include monitoring and reporting onmarketplace information relevant to a value of the collateral (step10120).

A set of offset collateral items for valuing the collateral may beconstructed using a similarity clustering algorithm based on anattribute of the collateral (step 10122).

Referring to FIG. 102 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities10200 is depicted. The example system may include a data collectioncircuit 10206 which may collect data such as loan collection outcomes10203, training set of loan interactions 10204, which may includecollection of payments 10205, and the like. The data may be collectedfrom loan transactions 10219, loan data 10201, entity information 10202,and the like. The data may be collected from a variety of sources andsystems such as: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system. The loan collection outcomes 10203 mayinclude at least one outcome such as a response to a collection contactevent, a payment of a loan, a default of a borrower on a loan, abankruptcy of a borrower of a loan, an outcome of a collectionlitigation, a financial yield of a set of collection actions, a returnon investment on collection, a measure of reputation of a party involvedin collection, and the like.

The system may also include an artificial intelligence circuit 10210that may be structured to classify a set of loan collection actions10209 based at least in part on the training set of loan interactions10204. The artificial intelligence circuit 10210 may include at leastone system such as a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network a self-organizing map, a fuzzy logic system, arandom walk system, a random forest system, a probabilistic system, aBayesian system, a simulation system, and the like.

The system may also include a robotic process automation circuit 10213structured to perform at least one loan collection action 10211 onbehalf of a party to a loan 10212 based at least in part on the trainingset of loan interactions 10204 and the set of loan collection outcomes10203. The loan collection action 10211 undertaken by the roboticprocess automation circuit 10213 may be at least one of a referral of aloan to an agent for collection, configuration of a collectioncommunication, scheduling of a collection communication, configurationof content for a collection communication, configuration of an offer tosettle a loan, termination of a collection action, deferral of acollection action, configuration of an offer for an alternative paymentschedule, initiation of a litigation, initiation of a foreclosure,initiation of a bankruptcy process, a repossession process, placement ofa lien on collateral, and the like. The party to a loan 10212 mayinclude at least one party such as a primary lender, a secondary lender,a lending syndicate, a corporate lender, a government lender, a banklender, a secured lender, a bond issuer, a bond purchaser, an unsecuredlender, a guarantor, a provider of security, a borrower, a debtor, anunderwriter, an inspector, an assessor, an auditor, a valuationprofessional, a government official, an accountant, and the like. Loans10201 may include at least one auto loan, an inventory loan, a capitalequipment loan, a bond for performance, a capital improvement loan, abuilding loan, a loan backed by an account receivable, an invoicefinance arrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, a subsidized loan and the like.

The system may further include an interface circuit 10208 structured toreceive interactions 10207 from one or more of the entities 10202. Insome embodiments, the robotic process automation circuit 10213 may betrained on the interactions 10207. The system may further include asmart contract circuit 10218 structured to determine completion of anegotiation of the loan collection action 10211 and modify a contract10216 based on an outcome of the negotiation 10217.

The system may further include a distributed ledger circuit 10215structured to determine at least one of a collection outcome 10220 or anevent 10221 associated with the loan collection action 10211. Thedistributed ledger circuit 10215 may be structured to record, in adistributed ledger 10214 associated with the loan, the event 10221and/or the collection outcome 10220.

Referring to FIG. 103 , an illustrative and non-limiting example method10300 is depicted. The example method 10300 may include step 10301 forcollecting a training set of loan interactions and a set of loancollection outcomes among entities for a set of loan transactions,wherein the training set of loan interactions comprises a collection ofa set of payments for a set of loans. A set of loan collection actionsbased at least in part the training set of loan interactions may beclassified (step 10302). The method may further include the step 10303of specifying a loan collection action on behalf of a party to a loanbased at least in part on the training set of loan interactions and theset of loan collection outcomes.

The method 10300 may further include the step 10304 of determiningcompletion of a negotiation of the loan collection action. Based on theoutcome of the negotiations, a smart contract may be modified in step10305. The method may also include the step 10306 of determining atleast one of a collection outcome or an event associated with the loancollection action. The at least one of the collection outcome or theevent may be recorded in a distributed ledger associate with the loan instep 10307.

Referring to FIG. 104 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities10400 is depicted. The example system may include a data collectioncircuit 10406 structured to collect a training set of loan interactionsbetween entities 10402, wherein the training set of loan interactionsmay include a set of loan refinancing activities 10403 and a set of loanrefinancing outcomes 10404. The system may include an artificialintelligence circuit 10410 structured to classify the set of loanrefinancing activities, wherein the artificial intelligence circuit istrained on the training set of loan interactions. The system may includea robotic process automation circuit 10413 structured to perform asecond loan refinancing activity 10411 on behalf of a party to a secondloan 10412, wherein the robotic process automation circuit is trained onthe set of loan refinancing activities and the set of loan refinancingoutcomes. The example system may include a data collection circuit10406, which may collect data such as a training set of loaninteractions between entities 10402. Data related to the set of loaninteractions between entities 10402 may include data related to loanrefinancing activities 10403 and loan refinancing outcomes 10404. Thedata may be collected from loan data 10401, information about entities10402, and the like. The data may be collected from a variety of sourcesand systems such as: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system. The loan refinancing activity 10403may include at least one activity such as initiating an offer torefinance, initiating a request to refinance, configuring a refinancinginterest rate, configuring a refinancing payment schedule, configuring arefinancing balance, configuring collateral for a refinancing, managinguse of proceeds of a refinancing, removing or placing a lien associatedwith a refinancing, verifying title for a refinancing, managing aninspection process, populating an application, negotiating terms andconditions for a refinancing, closing a refinancing, and the like.

The system may also include an artificial intelligence circuit 10410that may be structured to classify the set of loan refinancingactivities 10409 based at least in part on the training set of loaninteractions 10405. The artificial intelligence circuit 10410 mayinclude at least one system such as a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network a self-organizing map,a fuzzy logic system, a random walk system, a random forest system, aprobabilistic system, a Bayesian system, a simulation system, and thelike.

The system may also include a robotic process automation circuit 10413structured to perform a second loan refinancing activity 10411 on behalfof a party to a second loan 10412 based at least in part on the set ofloan refinancing activities 10403 and the set of loan refinancingoutcomes 10404. The party to a second loan 10412 may include least onesuch as a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,bond issuer, a bond purchaser, an unsecured lender, a guarantor, aprovider of security, a borrower, a debtor, an underwriter, aninspector, an assessor, an auditor, a valuation professional, agovernment official, an accountant, and the like.

The second loan 10419 may include at least one auto loan, an inventoryloan, a capital equipment loan, a bond for performance, a capitalimprovement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, a subsidizedloan and the like.

The system may further include an interface circuit 10408 structured toreceive interactions 10407 from one or more of the entities 10402. Insome embodiments, the robotic process automation circuit 10413 may betrained on the interactions 10407. The system may further include asmart contract circuit 10418 structured to determine completion of thesecond loan refinancing activity 10411 and modify a smart refinancecontract 10417 based on an outcome of the second loan refinancingactivity 10411.

The system may further include a distributed ledger circuit 10416structured to determine an event 10415 associated with the second loanrefinancing activity 10411. The distributed ledger circuit 10416 may bestructured to record, in a distributed ledger 10414 associated with thesecond loan 10419, the event 10415 associated with the second loanrefinancing activity 10411.

Referring to FIG. 105 , an illustrative and non-limiting example method10500 is depicted. The example method 10500 may include step 10501 forcollecting a training set of loan interactions between entities, whereinthe training set of loan interactions comprises a set of loanrefinancing activities and a set of loan refinancing outcomes. A set ofloan refinancing activities based at least in part on the training setof loan interactions may be classified (step 10502). The method mayfurther include the step 10503 of specifying a second loan refinancingactivity on behalf of a party to a second loan based at least in part onthe set of loan refinancing activities and the set of loan refinancingoutcomes.

The method 10500 may further include the step 10504 of determiningcompletion of the second loan refinancing activity. Based on the outcomeof the second loan refinancing activity, a smart refinance contract maybe modified in step 10505. The method may also include the step 10506 ofdetermining an event associated with the second loan refinancingactivity. The event associated with the second loan refinancing activitymay be recorded in a distributed ledger associate with the second loanin step 10507.

Referring to FIG. 106 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities10600 is depicted. The example system may include a data collectioncircuit 10605, which may collect data such as a training set of loaninteractions 10604 between entities, which may include a set of loanconsolidation transactions 10603 and the like. The data may be collectedfrom loan data 10601, information regarding entities 10602, and thelike. The data may be collected from a variety of sources and systemssuch as: an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, and acrowdsourcing system.

The system may also include an artificial intelligence circuit 10610that may be structured to classify a set of loans as candidates forconsolidation 10608 based at least in part on the training set of loaninteractions 10604. The artificial intelligence circuit 10610 mayinclude at least one system such as a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network a self-organizing map,a fuzzy logic system, a random walk system, a random forest system, aprobabilistic system, a Bayesian system, a simulation system, and thelike.

The system may also include a robotic process automation circuit 10613structured to manage a consolidation of at least a subset of the set ofloans 10611 on behalf of a party to the loan consolidation 10612 basedat least in part on the training set of loan consolidation transactions10603. Managing the consolidation may include identification of loansfrom a set of candidate loans, preparation of a consolidation offer,preparation of a consolidation plan, preparation of contentcommunicating a consolidation offer, scheduling a consolidation offer,communicating a consolidation offer, negotiating a modification of aconsolidation offer, preparing a consolidation agreement, executing aconsolidation agreement, modifying collateral for a set of loans,handling an application workflow for consolidation, managing aninspection, managing an assessment, setting an interest rate, deferringa payment requirement, setting a payment schedule, or closing aconsolidation agreement.

The artificial intelligence circuit may further include a model 10609that may be used to classify loans as candidates for consolidation10608. The model 10609 may process attributes of entities; theattributes may include identity of a party, interest rate, paymentbalance, payment terms, payment schedule, type of loan, type ofcollateral, financial condition of party, payment status, condition ofcollateral, value of collateral, and the like.

The party to a loan consolidation 10612 may include at least one such asa primary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, a bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,an accountant, and the like.

Loans 10601 may include at least one auto loan, an inventory loan, acapital equipment loan, a bond for performance, a capital improvementloan, a building loan, a loan backed by an account receivable, aninvoice finance arrangement, a factoring arrangement, a pay day loan, arefund anticipation loan, a student loan, a syndicated loan, a titleloan, a home loan, a venture debt loan, a loan of intellectual property,a loan of a contractual claim, a working capital loan, a small businessloan, a farm loan, a municipal bond, a subsidized loan and the like.

The system may further include an interface circuit 10607 structured toreceive interactions 10606 from one or more of the entities 10602. Insome embodiments, the robotic process automation circuit 10613 may betrained on the interactions 10606. The system may further include asmart contract circuit 10620 structured to determine a completion of anegotiations of the consolidation and modify a contract 10618 based onan outcome of the negotiation 10619.

The system may further include a distributed ledger circuit 10617structured to determine at least one of an outcome 10615 or anegotiation event 10616 associated with the consolidation. Thedistributed ledger circuit 10617 may be structured to record, in adistributed ledger 10614 associated with the loan, the event 10616and/or the outcome 10615.

Referring to FIG. 107 , an illustrative and non-limiting example method10700 is depicted. The example method 10700 may include step 10701 ofcollecting a training set of loan interactions between entities, whereinthe training set of loan interactions comprises a set of loanconsolidation transactions. A set of loans as candidates forconsolidation based at least in part on the training set of loaninteractions may be classified (step 10702). The method may furtherinclude the step 10703 of managing a consolidation of at least a subsetof the set of loans on behalf of a party to the consolidation based atleast in part on the set of loan consolidation transactions.

The method 10700 may further include the step 10704 of determiningcompletion of a negotiation of the consolidation of at least one loanfrom the subset of the set of loans. Based on the outcome of thenegotiations, a smart contract may be modified in step 10705. The methodmay also include the step 10706 of determining at least one of anoutcome and a negotiation event associated with the consolidation of atleast the subset of the set of loans. The at least one of the outcomeand the negotiation event may be recorded in a distributed ledgerassociated with the consolidation in step 10707.

Referring to FIG. 108 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities10800 is depicted. The example system may include a data collectioncircuit 10805, which may collect data information about entities 10802involved in a set of factoring loans 10801 and a training set ofinteractions 10804 between entities for a set of factoring loantransactions 10803. The data may be collected from a variety of sourcesand systems such as: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and acrowdsourcing system.

The system may also include an artificial intelligence circuit 10811that may be structured to classify entities 10808 involved in the set offactoring loans based at least in part on the training set ofinteractions 10804. The artificial intelligence circuit 10811 mayinclude at least one system such as a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network a self-organizing map,a fuzzy logic system, a random walk system, a random forest system, aprobabilistic system, a Bayesian system, a simulation system, and thelike.

The system may also include a robotic process automation circuit 10813structured to manage a factoring loan 10812 based at least in part onthe factoring loan transactions 10803. Managing the factoring loan mayinclude managing at least one of a set of assets for factoring,identification of loans for factoring from a set of candidate loans,preparation of a factoring offer, preparation of a factoring plan,preparation of content communicating a factoring offer, scheduling afactoring offer, communicating a factoring offer, negotiating amodification of a factoring offer, preparing a factoring agreement,executing a factoring agreement, modifying collateral for a set offactoring loans, handing transfer of a set of accounts receivable,handling an application workflow for factoring, managing an inspection,managing an assessment of a set of assets to be factored, setting aninterest rate, deferring a payment requirement, setting a paymentschedule, or dosing a factoring agreement.

The artificial intelligence circuit 10811 may further include a model10809 that may be used to process attributes of entities involved in theset of factoring loans; the attributes may include assets used forfactoring, identity of a party, interest rate, payment balance, paymentterms, payment schedule, type of loan, type of collateral, financialcondition of party, payment status, condition of collateral, or value ofcollateral. The assets used for factoring may include a set of accountsreceivable 10810. At least one entity of the entities 10802 may be aparty to at least one factoring loan transactions 10803. The party mayinclude least one such as a primary lender, a secondary lender, alending syndicate, a corporate lender, a government lender, a banklender, a secured lender, a bond issuer, a bond purchaser, an unsecuredlender, a guarantor, a provider of security, a borrower, a debtor, anunderwriter, an inspector, an assessor, an auditor, a valuationprofessional, a government official, an accountant, and the like.

The system may further include an interface circuit 10807 structured toreceive interactions 10806 from one or more of the entities 10802. Insome embodiments, the robotic process automation circuit 10813 may betrained on the interactions 10806.

The system may further include a smart contract circuit 10820 structuredto determine a completion of a negotiation of the factoring loan andmodify a contract 10818 based on an outcome of the negotiation 10819.

The system may further include a distributed ledger circuit 10817structured to determine at least one of an outcome 10815 or anegotiation event 10816 associated with the negotiation of the factoringloan. The distributed ledger circuit 10817 may be structured to record,in a distributed ledger 10814 associated with the factoring loan, theevent 10816 and/or the outcome 10815.

Referring to FIG. 109 , an illustrative and non-limiting example method10900 is depicted. The example method 10900 may include step 10901 ofcollecting information about entities involved in a set of factoringloans and a training set of interactions between entities for a set offactoring loan transactions. Entities involved in the set of factoringloans may be classified based at least in part on the training set ofloan interactions (step 10902). The method may further include the step10903 of managing a factoring loan based at least in part on the set offactoring loan interactions.

The method 10900 may further include the step 10904 of determiningcompletion of a negotiation of the factoring loan. Based on the outcomeof the negotiations, a smart contract may be modified in step 10905. Themethod may also include the step 10906 of determining at least one of anoutcome and a negotiation event associated with the negotiation of thefactoring loan. The at least one of the outcome and the negotiationevent may be recorded in a distributed ledger associate with thefactoring loan in step 10907.

Referring to FIG. 110 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities11000 is depicted. The example system may include a data collectioncircuit 11006, which may collect data information about entities 11002involved in a set of mortgage loan activities 11005 and a training setof interactions 11004 between entities for a set of mortgage loantransactions 11003. The data may be collected from a variety of sourcesand systems such as: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and acrowdsourcing system.

The system may also include an artificial intelligence circuit 11010that may be structured to classify entities 11009 involved in the set ofmortgage loan activities based at least in part on the training set ofinteractions 11004. The artificial intelligence circuit 11010 mayinclude at least one system such as a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network a self-organizing map,a fuzzy logic system, a random walk system, a random forest system, aprobabilistic system, a Bayesian system, a simulation system, and thelike.

The system may also include a robotic process automation circuit 11012structured to broker a mortgage loan 11011 based at least in part on atleast one of the set of mortgage loan activities 11005 and the trainingset of interactions 11004. The set of mortgage loan activities 11005and/or the set of mortgage loan transactions 11003 may includeactivities selected from a group consisting of: among marketingactivity, identification of a set of prospective borrowers,identification of property, identification of collateral, qualificationof borrower, title search, title verification, property assessment,property inspection, property valuation, income verification, borrowerdemographic analysis, identification of capital providers, determinationof available interest rates, determination of available payment termsand conditions, analysis of existing mortgage, comparative analysis ofexisting and new mortgage terms, completion of application workflow,population of fields of application, preparation of mortgage agreement,completion of schedule to mortgage agreement, negotiation of mortgageterms and conditions with capital provider, negotiation of mortgageterms and conditions with borrower, transfer of title, placement oflien, or closing of mortgage agreement.

The artificial intelligence circuit 11010 may further include a modelthat may be used to process attributes of entities involved in the setof mortgage loan activities; the attributes may properties that aresubject to mortgages, assets used for collateral, identity of a party,interest rate, payment balance, payment terms, payment schedule, type ofmortgage, type of property, financial condition of party, paymentstatus, condition of property, or value of property. In embodiments,brokering the mortgage loan comprises at least one activity such asmanaging at least one of a property that is subject to a mortgage,identification of candidate mortgages from a set of borrower situations,preparation of a mortgage offer, preparation of content communicating amortgage offer, scheduling a mortgage offer, communicating a mortgageoffer, negotiating a modification of a mortgage offer, preparing amortgage agreement, executing a mortgage agreement, modifying collateralfor a set of mortgage loans, handing transfer of a lien, handling anapplication workflow, managing an inspection, managing an assessment ofa set of assets to be subject to a mortgage, setting an interest rate,deferring a payment requirement, setting a payment schedule, closing amortgage agreement, and the like

In embodiments, at least one entity of the entities 11002 may be a partyto at least one mortgage loan transactions of the set of mortgage loantransactions 11003. The party may include least one such as a primarylender, a secondary lender, a lending syndicate, a corporate lender, agovernment lender, a bank lender, a secured lender, a bond issuer, abond purchaser, an unsecured lender, a guarantor, a provider ofsecurity, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,an accountant, and the like.

The system may further include an interface circuit 11008 structured toreceive interactions 11007 from one or more of the entities 11002. Insome embodiments, the robotic process automation circuit 11012 may betrained on the interactions 11007.

The system may further include a smart contract circuit 11019 structuredto determine a completion of a negotiations of the mortgage loan andmodify a smart contract 11017 based on an outcome of the negotiation11018.

The system may further include a distributed ledger circuit 11016structured to determine at least one of an outcome 11014 or anegotiation event 11015 associated with the negotiation of the mortgageloan. The distributed ledger circuit 11016 may be structured to record,in a distributed ledger 11013 associated with the mortgage loan, theevent 11015 and/or the outcome 11014.

Referring to FIG. 111 , an illustrative and non-limiting example method11100 is depicted. The example method 11100 may include step 11101 ofcollecting information about entities involved in a set of mortgage loanactivities and a training set of interactions between entities for a setof mortgage loan transactions. Entities involved in the set of factoringloans may be classified based at least in part on the training set ofloan interactions (step 11102). The method may further include the step11103 of brokering a mortgage loan based at least in part on at leastone of the set of mortgage loan activities and the training set ofinteractions.

The method 11100 may further include the step 11104 of determiningcompletion of a negotiation of the mortgage loan. Based on the outcomeof the negotiations, a smart contract may be modified in step 11105. Themethod may also include the step 11106 of determining at least one of anoutcome and a negotiation event associated with the negotiation of themortgage loan. The at least one of the outcome and the negotiation eventmay be recorded in a distributed ledger associate with the mortgage loanin step 11107.

Referring to FIG. 112 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities11200 is depicted. The example system may include a data collectioncircuit 11208, which may collect data about entities 11205 involved in aset of debt transactions 11201, training data set of outcomes 11206related to the entities, and a training set of debt managementactivities 11207. The data may be collected from a variety of sourcesand systems such as: Internet of Things devices, a set of environmentalcondition sensors, a set of crowdsourcing services, a set of socialnetwork analytic services, or a set of algorithms for querying networkdomains, and the like.

The system may also include a condition classifying circuit 11214 thatmay be structured to classify a condition 11211 of at least one entityof the entities 11205. The condition classifying circuit 11214 mayinclude a model 11212 and a set of artificial intelligence circuits11213. The model 11212 may be trained using the training data set ofoutcomes 11206 related to the entities. The artificial intelligencecircuits 11213 may include at least one system such as machine learningsystem, a model-based system, a rule-based system, a deep learningsystem, a hybrid system, a neural network, a convolutional neuralnetwork, a feed forward neural network, a feedback neural network, aself-organizing map, a fuzzy logic system, a random walk system, arandom forest system, a probabilistic system, a Bayesian system, or asimulation system.

The system may also include an automated debt management circuit 11216structured to manage an action related to a debt 11215. The automateddebt management circuit 11216 may be trained on the training set of debtmanagement activities 11207.

In embodiments, at least one debt transaction of the set of debttransactions 11201 may be include an auto loan, an inventory loan, acapital equipment loan, a bond for performance, a capital improvementloan, a building loan, a loan backed by an account receivable, aninvoice finance arrangement, a factoring arrangement, a pay day loan, arefund anticipation loan, a student loan, a syndicated loan, a titleloan, a home loan, a venture debt loan, a loan of intellectual property,a loan of a contractual claim, a working capital loan, a small businessloan, a farm loan, a municipal bond, a subsidized loan, and the like.

In embodiments, the entities 11205 involved in the set of debttransactions may include at least one of set of parties 11202 and a setof assets 11204. The assets 11204 may include a municipal asset, avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, or an item of personal property. The system mayfurther include a set of sensors 11203 positioned on at least one asset11204 from the set of assets, on a container for least one asset fromthe set of assets, and on a package for at least one asset from the setof assets, wherein the set of sensors configured to associate sensorinformation sensed by the set of sensors with a unique identifier forthe at least one asset from the set of assets. The sensors 11203 mayinclude image, temperature, pressure, humidity, velocity, acceleration,rotational, torque, weight, chemical, magnetic field, electrical field,or position sensors.

In embodiments, the system may further include a set of block chaincircuits 11224 structured to receive information from the datacollection circuit 11208 and the set of sensors 11203 and store theinformation in a blockchain 11226. The access to the blockchain 11226may be provided via a secure access control interface circuit 11223.

An automated agent circuit 11225 may be structured to process eventsrelevant to at least one of a value, a condition, and an ownership of atleast one asset of the set of assets and further structured to undertakea set of actions related to a debt transaction to which the asset isrelated.

The system may further include an interface circuit 11210 structured toreceive interactions 11209 from at least one of the entities 11205. Inembodiments, the automated debt management circuit 11216 may be trainedon the interactions 11209. In some embodiments, the system may furtherinclude a market value data collection circuit 11218 structured tomonitor and report marketplace information 11217 relevant to a value ofa of at least one asset of a set of assets 11204. The market value datacollection circuit 11218 may be further structured to monitor at leastone pricing and financial data for items that are similar to at leastone asset in the set of assets in at least one public marketplace. A setof similar items for valuing at least one asset from the set of assetsmay be constructed using a similarity clustering algorithm based onattributes of the assets. In embodiments, at least one attribute of theattributes of the assets may include a category of assets, asset age,asset condition, asset history, asset storage, geolocation of assets,and the like.

In embodiments, the system may further include a smart contract circuit11222 structured to manage a smart contract 11219 for a debt transaction11221. The smart contract circuit 11222 may be further structured toestablish a set of terms and conditions 11220 for the debt transaction11221. At least one of the terms and conditions may include a principalamount of debt, a balance of debt, a fixed interest rate, a variableinterest rate, a payment amount, a payment schedule, a balloon paymentschedule, a specification of collateral, a specification ofsubstitutability of collateral, a party, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, a consequence of default, andthe like.

In embodiments, at least one action related to a debt 11215 may includeoffering a debt transaction, underwriting a debt transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating debt, or consolidating debt. At least one debt managementactivity from the training set of debt management activities 11207 mayinclude offering a debt transaction, underwriting a debt transaction,setting an interest rate, deferring a payment requirement, modifying aninterest rate, validating title, managing inspection, recording a changein title, assessing a value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating debt, or consolidating debt.

Referring to FIG. 113 , an illustrative and non-limiting example method11300 is depicted. The example method 11300 may include step 11301 ofcollecting information about entities involved in a set of debttransactions, training data set of outcomes related to the entities, anda training set of debt management activities. The example method mayfurther include classifying a condition of at least one entity of theentities based at least in part the training data set of outcomesrelated to the entities (step 11302). The example method may furtherinclude managing an action related to a debt based at least in part onthe training set of debt management activities (step 11303). The examplemethod may further include receiving information from a set of sensorspositioned on at least one asset (step 11304). The example method mayfurther include storing the information in a blockchain, wherein accessto the blockchain is provided via a secure access control interface fora party for a debt transaction involving the at least one asset from theset of assets (step 11305). In step 11306 the method may includeprocessing events relevant to at least one of a value, a condition, oran ownership of at least one asset of the set of assets. In step 11307the method may include processing a set of actions related to a debttransaction to which the asset is related. In embodiments, the methodmay further include receiving interactions from at least one of theentities (step 11308), monitoring and reporting marketplace informationrelevant to a value of a of at least one asset of a set of assets (step11309), constructing using a similarity clustering algorithm based onattributes of the assets a set of similar items for valuing at least oneasset from the set of assets (step 11310), managing a smart contract fora debt transaction (step 11311), and establishing a set of terms andconditions for the smart contract for the debt transaction (step 11312).

Referring to FIG. 114 , an illustrative and non-limiting example systemfor adaptive intelligence and robotic process automation capabilities11400 is depicted.

The example system may include a crowdsourcing data collection circuit11405 structured to collect information about entities 11403 involved ina set of bond transactions 11402 and a training data set of outcomesrelated to the entities 11403. The system may further include acondition classifying circuit 11411 structured to classify a conditionof a set of issuers 11408 using the information from the crowdsourcingdata collection circuit 11405 and a model 11409. The model 11409 may betrained using the training data set of outcomes 11404 related to the setof issuers. The example system may further include an automated agentcircuit 11419 structured to perform an action related to a debttransaction in response to the classified condition of at least oneissuer of the set of issuers. In embodiments, at least one entity 11403may include a set of issuers, a set of bonds, a set of parties, or a setof assets. At least one issuer may include a municipality, acorporation, a contractor, a government entity, a non-governmentalentity, or a non-profit entity. At least one bond may include amunicipal bond, a government bond, a treasury bond, an asset-backedbond, or a corporate bond.

In embodiments, the condition classified 11408 by the conditionclassifying circuit 11411 may include a default condition, a foreclosurecondition, a condition indicating violation of a covenant, a financialrisk condition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition, an entity health condition,or the like. The crowdsourcing data collection circuit 11411 may bestructured to enable a user interface 11407 by which a user mayconfigure a crowdsourcing request 11406 for information relevant to thecondition about the set of issuers.

The system may further include a configurable data collection andmonitoring circuit 11413 structured to monitor at least one issuer fromthe set of issuers 11412. The configurable data collection andmonitoring circuit 11413 may include a system such as: Internet ofThings devices, a set of environmental condition sensors, a set ofsocial network analytic services, or a set of algorithms for queryingnetwork domains. The configurable data collection and monitoring circuit11413 may be structured to monitor an at least one environment such as:a municipal environment, a corporate environment, a securities tradingenvironment, a real property environment, a commercial facility, awarehousing facility, a transportation environment, a manufacturingenvironment, a storage environment, a home, or a vehicle.

In embodiments, a set of bonds associated with the set of bondtransactions 11402 may be backed by a set of assets 11401. At least oneasset 11401 may include a municipal asset, a vehicle, a ship, a plane, abuilding, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, or the like.

In embodiments, the system may further include an automated agentcircuit 11419 structured to process events relevant to at least one of avalue, a condition, or an ownership of at least one asset of the atleast one issuer of the set of issuers and to perform the action relatedto the debt transaction in response to at least one of the processedevents.

The action 11418 may include offering a debt transaction, underwriting adebt transaction, setting an interest rate, deferring a paymentrequirement, modifying an interest rate, validating title, managinginspection, recording a change in title, assessing the value of anasset, calling a loan, closing a transaction, setting terms andconditions for a transaction, providing notices required to be provided,foreclosing on a set of assets, modifying terms and conditions, settinga rating for an entity, syndicating debt, consolidating debt, and thelike. The condition classifying circuit 11411 may include a system suchas: a machine learning system, a model-based system, a rule-basedsystem, a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, or a simulation system.

In embodiments, the system may further include an automated bondmanagement circuit 11427 configured to manage an action related to thebond 11424 related to the at least one issuer of the set of issuers. Theautomated bond management circuit 11427 may be trained on a training setof bond management activities 11426. The automated bond managementcircuit 11427 may be further trained on a set of interactions of parties11425 with a set of user interfaces involved in a set of bondtransaction activities. At least one bond transaction may include a debttransaction, underwriting a debt transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating debt,consolidating debt, or the like.

In embodiments, the system may further include a market value datacollection circuit 11417 structured to monitor and report on marketplaceinformation 11414 relevant to a value of at least one of the issuer or aset of assets. Reporting may include reporting on: a municipal asset, avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, or an item of personal property. The market valuedata collection circuit 11417 may be structured to monitor pricing 11416or financial data 11415 for items that are similar to the assets in atleast one public marketplace. The market value data collection circuit11417 may be further structured to construct a set of similar items forvaluing the assets using a similarity clustering algorithm based onattributes of the assets. At least one attribute from the attributes maybe selected from: a category of the assets, asset age, asset condition,asset history, asset storage, or geolocation of assets.

In embodiments, the system may further include a smart contract circuit11423 structured for managing a smart contract 11420 for a bondtransaction 11422 in response to the classified condition of the atleast one issuer of the set of issuers. The smart contract circuit 11423may be structured to determine terms and conditions 11421 for the bond.At least one term and condition 11421 may include a principal amount ofdebt, a balance of debt, a fixed interest rate, a variable interestrate, a payment amount, a payment schedule, a balloon payment schedule,a specification of assets that back the bond, a specification ofsubstitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, aconsequence of default, and the like.

Referring to FIG. 115 , an illustrative and non-limiting example method11500 is depicted. The example method 11500 may include step 11501 ofcollecting information about entities involved in a set of bondtransactions of a set of bonds and a training data set of outcomesrelated to the entities. The method may further include the step 11502of classifying a condition of a set of issuers using the collectedinformation and a model, wherein the model is trained using the trainingdata set of outcomes related to the set of issuers. The method mayfurther include processing events relevant to at least one of a value, acondition, or an ownership of at least one asset of the set of assets(step 11503). The method may further include the steps 11504 ofperforming an action related to a debt transaction to which the asset isrelated, 11505 managing an action related to the bond based at least inpart on a training set of bond management activities, 11506 monitoringand reporting on marketplace information relevant to a value of at leastone of the issuer and a set of assets, 11507 managing a smart contractfor a bond transaction, and 11508 determining terms and conditions forthe smart contract for at least one bond.

Referring now to FIG. 116 , an illustrative and non-limiting examplesystem for monitoring a condition of an issuer for a bond 11600 isdepicted. The example system may include a controller 11601. Thecontroller 11601 may include a data collection circuit 11612, a marketvalue data collection circuit 11656, a social networking input circuit11644, a social network data collection circuit 11632, and severalartificial intelligence circuits 11642 including a smart contractcircuit 11622, an automated bond management circuit 11650, a conditionclassifying circuit 11646, a clustering circuit 11662, and an eventprocessing circuit 11652.

The social network data collection circuit 11632 may be structured tocollect information about at least one entity 11664 involved in at leastone transaction 11630 comprising at least one bond, and a conditionclassifying circuit 11646 may be structured to classify a condition ofthe at least one entity in accordance with a model 11674 and based oninformation from the social network data collection circuit, wherein themodel is trained using a training data set 11654 of a plurality ofoutcomes related to the at least one entity. The at least one entity maybe selected from the entities consisting of: a bond issuer, a bond, aparty, and an asset. The bond issuer may be selected from the bondissuers consisting of: a municipality, a corporation, a contractor, agovernment entity, a non-governmental entity, and a non-profit entity.The bond may be selected from the entities consisting of: a municipalbond, a government bond, a treasury bond, an asset-backed bond, and acorporate bond.

The condition classified by the condition classifying circuit 11648 maybe at least one of a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition, or an entity healthcondition.

The social network data collection circuit 11632 may further include asocial networking input circuit 11644 which may be structured to receiveinput from a user used to configure a query for information about the atleast one entity.

The data collection circuit 11612 may be structured to monitor at leastone of an Internet of Things device, an environmental condition sensor,a crowdsourcing request circuit, a crowdsourcing communication circuit,a crowdsourcing publishing circuit, and an algorithm for queryingnetwork domains.

The data collection circuit 11612 may be further structured to monitoran environment selected from the group consisting of: a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle.

The at least one bond is backed by at least one asset. The at least oneasset may be selected from the assets consisting of: a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

The event processing circuit 11652 may be structured to process an eventrelevant to at least one of a value, a condition, and an ownership ofthe at least one asset and undertake an action related to the at leastone transaction. The action may be selected from the actions consistingof: a bond transaction, underwriting a bond transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating bonds, and consolidating bonds.

The condition classifying circuit 11648 may further include a systemselected from the systems consisting of: a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizingmap, a fuzzy logic system, a random walk system, a random forest system,a probabilistic system, a Bayesian system, and a simulation system.

The automated bond management circuit 11650 may be structured to managean action related to the at least one bond, wherein the automated bondmanagement circuit is trained on a training data set of a plurality ofbond management activities.

The automated bond management circuit 11650 may be trained on aplurality of interactions of parties with a plurality of user interfacesinvolved in a plurality of bond transaction activities. The plurality ofbond transaction activities may be selected from the bond transactionactivities consisting of: offering a bond transaction, underwriting abond transaction, setting an interest rate, deferring a paymentrequirement, modifying an interest rate, validating title, managinginspection, recording a change in title, assessing a value of an asset,calling a loan, closing a transaction, setting terms and conditions fora transaction, providing notices required to be provided, foreclosing ona set of assets, modifying terms and conditions, setting a rating for anentity, syndicating bonds, and consolidating bonds.

The market value data collection circuit 11656 may be structured tomonitor and report on marketplace information relevant to a value of atleast one of a bond issuer, the at least one bond, and an asset. Theasset may be selected from the assets consisting of: a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

The market value data collection circuit 11656 may be further structuredto monitor pricing or financial data for an offset asset item in atleast one public marketplace.

A set of offset asset items 11658 for valuing the asset may beconstructed using a clustering circuit 11662 based on an attribute ofthe asset. The attribute may be selected from the attributes consistingof: a category, an asset age, an asset condition, an asset history, anasset storage, and a geolocation.

The smart contract circuit 11622 may be structured to manage a smartcontract for the at least one transaction. The smart contract circuitmay be further structured to determine a terms and conditions for the atleast one bond.

The terms and conditions may be selected from the group consisting of: aprincipal amount of debt, a balance of debt, a fixed interest rate, avariable interest rate, a payment amount, a payment schedule, a balloonpayment schedule, a specification of assets that back the at least onebond, a specification of substitutability of assets, a party, an issuer,a purchaser, a guarantee, a guarantor, a security, a personal guarantee,a lien, a duration, a covenant, a foreclose condition, a defaultcondition, and a consequence of default.

Referring now to FIG. 117 , an illustrative and non-limiting examplemethod for monitoring a condition of an issuer for a bond 11700 isdepicted. An example method may include collecting social networkinformation about at least one entity involved in at least onetransaction comprising at least one bond 11702 and classifying acondition of the at least one entity in accordance with a model andbased on the social network information, wherein the model is trainedusing a training data set of a plurality of outcomes related to the atleast one entity 11704.

An event relevant to at least one of a value, a condition, and anownership of at least one asset may be processed 11708. An actionrelated to the at least one transaction may be undertaken in response tothe event 11710. An automated bond management circuit may be trained ona training set of a plurality of bond management activities to manage anaction related to the at least one bond 11712. An example method mayfurther include monitoring and reporting on marketplace informationrelevant to a value of at least one of a bond issuer, the at least onebond, and an asset 11714.

Referring now to FIG. 118 , an illustrative and non-limiting examplesystem for monitoring a condition of an issuer for a bond 11800 isdepicted. The example system may include a controller 11801. Thecontroller 11801 may include a data collection circuit 11812, a marketvalue data collection circuit 11856, an Internet of Things input circuit11844, an Internet of Things data collection circuit 11832, and severalartificial intelligence circuits 11842 including a smart contractcircuit 11822, an automated bond management circuit 11850, a conditionclassifying circuit 11846, a clustering circuit 11862, and an eventprocessing circuit 11852.

The Internet of Things data collection circuit 11832 may be structuredto collect information about at least one entity 11864 involved in atleast one transaction 11830 comprising at least one bond and a conditionclassifying circuit 11846 may be structured to classify a condition ofthe at least one entity in accordance with a model 11874 and based oninformation from the Internet of Things data collection circuit, whereinthe model is trained using a training data set 11854 of a plurality ofoutcomes related to the at least one entity. The at least one entity maybe selected from the entities consisting of: a bond issuer, a bond, aparty, and an asset. The bond issuer may be selected from the bondissuers consisting of: a municipality, a corporation, a contractor, agovernment entity, a non-governmental entity, and a non-profit entity.The bond may be selected from the entities consisting of: a municipalbond, a government bond, a treasury bond, an asset-backed bond, and acorporate bond.

The condition classified by the condition classifying circuit 11848 maybe at least one of a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition, or an entity healthcondition.

The Internet of Things data collection circuit 11832 may further includean Internet of Things input circuit 11844 which may be structured toreceive input from a user used to configure a query for informationabout the at least one entity.

The data collection circuit 11812 may be structured to monitor at leastone of an Internet of Things device, an environmental condition sensor,a crowdsourcing request circuit, a crowdsourcing communication circuit,a crowdsourcing publishing circuit, and an algorithm for queryingnetwork domains.

The data collection circuit 11812 may be further structured to monitoran environment selected from the group consisting of: a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle.

The at least one bond is backed by at least one asset. The at least oneasset may be selected from the assets consisting of: a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

The event processing circuit 11852 may be structured to process an eventrelevant to at least one of a value, a condition, and an ownership ofthe at least one asset and undertake an action related to the at leastone transaction. The action may be selected from the actions consistingof: a bond transaction, underwriting a bond transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating bonds, and consolidating bonds.

The condition classifying circuit 11848 may further include a systemselected from the systems consisting of: a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizingmap, a fuzzy logic system, a random walk system, a random forest system,a probabilistic system, a Bayesian system, and a simulation system.

The automated bond management circuit 11850 may be structured to managean action related to the at least one bond, wherein the automated bondmanagement circuit is trained on a training data set of a plurality ofbond management activities.

The automated bond management circuit 11850 may be trained on aplurality of interactions of parties with a plurality of user interfacesinvolved in a plurality of bond transaction activities. The plurality ofbond transaction activities may be selected from the bond transactionactivities consisting of: offering a bond transaction, underwriting abond transaction, setting an interest rate, deferring a paymentrequirement, modifying an interest rate, validating title, managinginspection, recording a change in title, assessing a value of an asset,calling a loan, closing a transaction, setting terms and conditions fora transaction, providing notices required to be provided, foreclosing ona set of assets, modifying terms and conditions, setting a rating for anentity, syndicating bonds, and consolidating bonds.

The market value data collection circuit 11856 may be structured tomonitor and report on marketplace information relevant to a value of atleast one of a bond issuer, the at least one bond, and an asset. Theasset may be selected from the assets consisting of: a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

The market value data collection circuit 11856 may be further structuredto monitor pricing or financial data for an offset asset item in atleast one public marketplace.

A set of offset asset items 11858 for valuing the asset may beconstructed using a clustering circuit 11862 based on an attribute ofthe asset. The attribute may be selected from the attributes consistingof: a category, an asset age, an asset condition, an asset history, anasset storage, and a geolocation.

The smart contract circuit 11822 may be structured to manage a smartcontract for the at least one transaction. The smart contract circuitmay be further structured to determine a terms and conditions for the atleast one bond.

The terms and conditions may be selected from the group consisting of: aprincipal amount of debt, a balance of debt, a fixed interest rate, avariable interest rate, a payment amount, a payment schedule, a balloonpayment schedule, a specification of assets that back the at least onebond, a specification of substitutability of assets, a party, an issuer,a purchaser, a guarantee, a guarantor, a security, a personal guarantee,a lien, a duration, a covenant, a foreclose condition, a defaultcondition, and a consequence of default.

Referring now to FIG. 119 , an illustrative and non-limiting examplemethod for monitoring a condition of an issuer for a bond 11900 isdepicted. An example method may include collecting Internet of Thingsinformation about at least one entity involved in at least onetransaction comprising at least one bond 11902 and classifying acondition of the at least one entity in accordance with a model andbased on the Internet of Things information, wherein the model istrained using a training data set of a plurality of outcomes related tothe at least one entity 11904.

An event relevant to at least one of a value, a condition, and anownership of at least one asset may be processed 11908. An actionrelated to the at least one transaction may be undertaken in response tothe event 11910. An automated bond management circuit may be trained ona training set of a plurality of bond management activities to manage anaction related to the at least one bond 11912. An example method mayfurther include monitoring and reporting on marketplace informationrelevant to a value of at least one of a bond issuer, the at least onebond, and an asset 11914.

FIG. 120 depicts a system 12000 including an Internet of Things datacollection circuit 12014 structured to collect information about anentity 12002 (e.g., where an entity may be a subsidized loan, a party, asubsidy, a guarantor, a subsidizing party, a collateral, and the like,where a party may be least one of a municipality, a corporation, acontractor, a government entity, a non-governmental entity, and anon-profit entity) involved in a subsidized loan transaction 12004. Inembodiments, the Internet of Things data collection circuit may includea user interface 12016 structured to enable a user to configure a queryfor information about the at least one entity. The system may include acondition classifying circuit 12018 that may include a model 12020structured to classify a parameter 12006 of a subsidized loan 12008(e.g., municipal subsidized loan, a government subsidized loan, astudent loan, an asset-backed subsidized loan, or a corporate subsidizedloan) involved in a subsidized loan transaction, such as based on theinformation from the Internet of Things data collection circuit. Inembodiments, the condition classifying circuit may include a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and the like. The subsidized loan may bebacked by an asset, such as a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, and the like. The conditionclassified by the condition classifying circuit may be a defaultcondition, a foreclosure condition, a condition indicating violation ofa covenant, a financial risk condition, a behavioral risk condition, acontractual performance condition, a policy risk condition, a financialhealth condition, a physical defect condition, a physical healthcondition, an entity risk condition, an entity health condition, and thelike. The model may be trained using a training data set of a pluralityof outcomes 12010 related to the subsidized loan. For instance, thesubsidized loan may be a student loan and the condition classifyingcircuit may classify a progress of a student toward a degree, aparticipation of a student in a non-profit activity, a participation ofa student in a public interest activity, and the like. The system mayinclude a smart contract circuit 12022 structured to automaticallymodify terms and conditions 12012 of the subsidized loan, such as basedon the classified parameter from the condition classifying circuit. Thesystem may include a configurable data collection and circuit 12024structured to monitor the entity, such as further including a socialnetwork analytic circuit 12030, an environmental condition circuit12032, a crowdsourcing circuit 12034, and an algorithm for querying anetwork domain 12036, where the configurable data collection and circuitmay monitor an environment selected from an environment, such as amunicipal environment, an educational environment, a corporateenvironment, a securities trading environment, a real propertyenvironment, a commercial facility, a warehousing facility, atransportation environment, a manufacturing environment, a storageenvironment, a home, a vehicle, and the like. The system may include anautomated agent 12026 structured to process an event relevant to avalue, a condition and an ownership of the asset, and undertake anaction related to the subsidized loan transaction to which the asset isrelated, wherein the action may be a subsidized loan transaction,underwriting a subsidized loan transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatinga title, managing an inspection, recording a change in a title,assessing the value of an asset, calling a loan, closing a transaction,setting terms and conditions for a transaction, providing noticesrequired to be provided, foreclosing on a set of assets, modifying termsand conditions, setting a rating for an entity, syndicating a subsidizedloan, consolidating a subsidized loan, and the like. The system mayinclude an automated subsidized loan management circuit 12038 structuredto manage an action related to the at least one subsidized loan, whereinthe automated subsidized loan management circuit is trained on atraining set of subsidized loan management activities. For instance, theautomated subsidized loan management circuit may be trained on aplurality of interactions of parties with a plurality of user interfacesinvolved in a plurality of subsidized loan transaction activities, wherethe plurality of subsidized loan transaction activities may be selectedfrom the activities consisting of offering a subsidized loantransaction, underwriting a subsidized loan transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating a title, managing an inspection, recording a change ina title, assessing a value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating a subsidized loan, and consolidating a subsidized loan. Thesystem may include a blockchain service circuit 12040 structured torecord the modified set of terms and conditions for a subsidized loan,such as in a distributed ledger 12042. The system may include a marketvalue data collection circuit 12028 structured to monitor and report onmarketplace information relevant to a value of an issuer, a subsidizedloan, an asset, and the like, where reporting may be on an assetselected from the assets consisting of a municipal asset, a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property. The market value datacollection circuit may be further structured to monitor pricing orfinancial data for an offset asset item in a public marketplace. A setof offset asset items for valuing the asset may be constructed using aclustering circuit based on an attribute of the asset, where theattribute may be a category, an asset age, an asset condition, an assethistory, an asset storage, a geolocation, and the like. The smartcontract circuit may be structured to manage a smart contract for asubsidized loan transaction, where the smart contract circuit may setterms and conditions for the subsidized loan, where the terms andconditions for the subsidized loan that are specified and managed by thesmart contract circuit may include a principal amount of debt, a balanceof debt, a fixed interest rate, a variable interest rate, a paymentamount, a payment schedule, a balloon payment schedule, a specificationof assets that back the at least one subsidized loan, a specification ofsubstitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, aconsequence of default, and the like.

FIG. 121 depicts a method 12100 including collecting information aboutan entity involved in a subsidized loan transaction 12102. The methodmay include classifying a parameter of a subsidized loan involved in thesubsidized loan transaction based on the information using a modeltrained on a training data set of a plurality of outcomes related to theat least one subsidized loan 12104. The method may include automaticallymodifying terms and conditions of the subsidized loan based on theclassified parameter 12108. The method may include processing an eventrelevant to a value, a condition, and an ownership of an asset andundertaking an action related to the subsidized loan transaction towhich the asset is related 12110. The method may include recording themodified set of terms and conditions for the subsidized loan in adistributed ledger 12112. The method may include monitoring andreporting on marketplace information relevant to a value of an issuer,the subsidized loan, the asset, and the like.

FIG. 122 depicts a system 12200 including a social network analytic datacollection circuit 12214 structured to collect social networkinformation about an entity 12202 (e.g., where an entity may be asubsidized loan, a party, a subsidy, a guarantor, a subsidizing party, acollateral, and the like, where a party may be least one of amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity) involved in asubsidized loan transaction 12204. In embodiments, the social networkanalytic data collection circuit may include a user interface 12216structured to enable a user to configure a query for information aboutthe at least one entity, wherein, in response to the query, the socialnetwork analytic data collection circuit may initiate at least onealgorithm that searches and retrieves data from at least one socialnetwork based on the query. The system may include a conditionclassifying circuit 12218 that may include a model 12220 structured toclassify a parameter 12206 of a subsidized loan 12208 (e.g., municipalsubsidized loan, a government subsidized loan, a student loan, anasset-backed subsidized loan, or a corporate subsidized loan) involvedin a subsidized loan transaction, such as based on the social networkinformation from the social network analytic data collection circuit. Inembodiments, the condition classifying circuit may include a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and the like. The subsidized loan may bebacked by an asset, such as a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, and the like. The parameterclassified by the condition classifying circuit may be a defaultcondition, a foreclosure condition, a condition indicating violation ofa covenant, a financial risk condition, a behavioral risk condition, acontractual performance condition, a policy risk condition, a financialhealth condition, a physical defect condition, a physical healthcondition, an entity risk condition, an entity health condition, and thelike. The model may be trained using a training data set of a pluralityof outcomes 12210 related to the subsidized loan. For instance, thesubsidized loan may be a student loan and the condition classifyingcircuit may classify a progress of a student toward a degree, aparticipation of a student in a non-profit activity, a participation ofa student in a public interest activity, and the like. The system mayinclude a smart contract circuit 12222 structured to automaticallymodify terms and conditions 12212 of the subsidized loan, such as basedon the classified parameter. The system may include a configurable datacollection and circuit 12224 structured to monitor the entity, such asfurther including a social network analytic circuit 12230, anenvironmental condition circuit 12232, a crowdsourcing circuit 12234,and an algorithm for querying a network domain 12236, where theconfigurable data collection and circuit may monitor an environmentselected from an environment, such as a municipal environment, aneducational environment, a corporate environment, a securities tradingenvironment, a real property environment, a commercial facility, awarehousing facility, a transportation environment, a manufacturingenvironment, a storage environment, a home, a vehicle, and the like. Thesystem may include an automated agent 12226 structured to process anevent relevant to a value, a condition, and an ownership of the assetand undertake an action related to the subsidized loan transaction towhich the asset is related, wherein the action may be a subsidized loantransaction, underwriting a subsidized loan transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating a title, managing an inspection, recording a change ina title, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating a subsidized loan, consolidating a subsidized loan, and thelike. The system may include an automated subsidized loan managementcircuit 12238 structured to manage an action related to the at least onesubsidized loan, wherein the automated subsidized loan managementcircuit is trained on a training set of subsidized loan managementactivities. For instance, the automated subsidized loan managementcircuit may be trained on a plurality of interactions of parties with aplurality of user interfaces involved in a plurality of subsidized loantransaction activities, where the plurality of subsidized loantransaction activities may be selected from the activities consisting ofoffering a subsidized loan transaction, underwriting a subsidized loantransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating a title, managing an inspection,recording a change in a title, assessing a value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating a subsidized loan, and consolidating a subsidizedloan. The system may include a blockchain service circuit 12240structured to record the modified set of terms and conditions for asubsidized loan, such as in a distributed ledger 12242. The system mayinclude a market value data collection circuit 12228 structured tomonitor and report on marketplace information relevant to a value of anissuer, a subsidized loan, an asset, and the like, where reporting maybe on an asset selected from the assets consisting of a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property. The market valuedata collection circuit may be further structured to monitor pricing orfinancial data for an offset asset item in a public marketplace. A setof offset asset items for valuing the asset may be constructed using aclustering circuit based on an attribute of the asset, where theattribute may be a category, an asset age, an asset condition, an assethistory, an asset storage, a geolocation, and the like. The smartcontract circuit may be structured to manage a smart contract for asubsidized loan transaction, where the smart contract circuit may setterms and conditions for the subsidized loan, where the terms andconditions for the subsidized loan that are specified and managed by thesmart contract circuit may include a principal amount of debt, a balanceof debt, a fixed interest rate, a variable interest rate, a paymentamount, a payment schedule, a balloon payment schedule, a specificationof assets that back the at least one subsidized loan, a specification ofsubstitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, aconsequence of default, and the like.

FIG. 123 depicts a method 12300 including collecting social networkinformation about an entity involved in a subsidized loan transaction12302. The method may include classifying a parameter of a subsidizedloan involved in the subsidized loan transaction based on the socialnetwork information using a model trained on a training data set of aplurality of outcomes related to the at least one subsidized loan 12304.The method may include automatically modifying terms and conditions ofthe subsidized loan based on the classified parameter 12308. The methodmay include processing an event relevant to a value, a condition, and anownership of an asset and undertaking an action related to thesubsidized loan transaction to which the asset is related 12310. Themethod may include recording the modified set of terms and conditionsfor the subsidized loan in a distributed ledger 12312. The method mayinclude monitoring and reporting on marketplace information relevant toa value of an issuer, the subsidized loan, the asset, and the like.

FIG. 124 depicts a system 12400 for automating handling of a subsidizedloan including a crowdsourcing services circuit 12425 structured tocollect information related to a set of entities 12402 involved in a setof subsidized loan transactions 12404. The set of entities may includeentities such as a set of subsidized loans, a set of parties 12416, aset of subsidies, a set of guarantors, a set of subsidizing parties, aset of collateral, and the like. A set of subsidizing parties mayinclude a municipality, a corporation, a contractor, a governmententity, a non-governmental entity, and a non-profit entity, and thelike. The loan may be a student loan and the condition classifyingcircuit classifies at least one of the progress of a student toward adegree, the participation of a student in a non-profit activity, theparticipation of the student in a public interest activity, and thelike. The crowdsourcing services circuit may be further structured witha user interface 12420 by which a user may configure a query forinformation about the set of entities and the crowdsourcing servicescircuit automatically configures a crowdsourcing request based on thequery. The set of subsidized loans may be backed by a set of assets12412, such as a municipal asset, a vehicle, a ship, a plane, abuilding, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, and the like. An example systemmay include a condition classifying circuit 12422 including a model12424 and an artificial intelligence services circuit 12436 structuredto classify a set of parameters 12406 of the set of subsidized loans12410 involved in the transactions based on information fromcrowdsourcing services circuit, where the model may be trained using atraining data set of outcomes 12414 related to subsidized loans. The setof subsidized loans may include at least one of a municipal subsidizedloan, a government subsidized loan, a student loan, an asset-backedsubsidized loan, and a corporate subsidized loan. The conditionclassified by the condition classifying circuit may be a defaultcondition, a foreclosure condition, a condition indicating violation ofa covenant, a financial risk condition, a behavioral risk condition, acontractual performance condition, a policy risk condition, a financialhealth condition, a physical defect condition, a physical healthcondition, an entity risk condition, an entity health condition, and thelike. The artificial intelligence services circuit may include a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and the like. An example system may includea smart contract circuit 12426 for automatically modifying the terms andconditions 12418 of a subsidized loan based on the classified set ofparameters from the condition classifying circuit. The smart contractservices circuit may be utilized for managing a smart contract for thesubsidized loan transaction, set terms and conditions for the subsidizedloan, and the like. In embodiments, the set of terms and conditions forthe debt transaction that are specified and managed by the smartcontract services circuit may be selected from among a principal amountof debt, a balance of debt, a fixed interest rate, a variable interestrate, a payment amount, a payment schedule, a balloon payment schedule,a specification of assets that back the subsidized loan, a specificationof substitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default. An example system may include a configurabledata collection and monitoring services circuit 12428 for monitoring theentities such as a set of Internet of Things services, a set ofenvironmental condition sensors, a set of social network analyticservices, a set of algorithms for querying network domains, and thelike. The configurable data collection and monitoring services circuitmay be further structured to monitor an environment such as a municipalenvironment, an educational environment, a corporate environment, asecurities trading environment, a real property environment, acommercial facility, a warehousing facility, a transportationenvironment, a manufacturing environment, a storage environment, a home,a vehicle, and the like. An example system may include an automatedagent circuit 12430 structured to process events relevant to at leastone of the value, the condition, and the ownership of the assets andundertakes an action related to a subsidized loan transaction to whichthe asset is related, such as where the action may be a subsidized loantransaction, underwriting a subsidized loan transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating subsidized loans, consolidating subsidized loans, and thelike. An example system may include an automated subsidized loanmanagement circuit 12438 structured to manage an action related to thesubsidized loan, where the automated subsidized loan management circuitmay be trained on a training set of subsidized loan managementactivities. The automated subsidized loan management circuit may betrained on a set of interactions of parties with a set of userinterfaces involved in a set of subsidized loan transaction activities,such as offering a subsidized loan transaction, underwriting asubsidized loan transaction, setting an interest rate, deferring apayment requirement, modifying an interest rate, validating title,managing inspection, recording a change in title, assessing the value ofan asset, calling a loan, closing a transaction, setting terms andconditions for a transaction providing notices required to be provided,foreclosing on a set of assets, modifying terms and conditions, settinga rating for an entity, syndicating subsidized loans, consolidatingsubsidized loans, and the like. An example system may include ablockchain services circuit 12440 structured to record the modified setof terms and conditions for the set of subsidized loans in a distributedledger. An example system may include a market value data collectionservice circuit 12432 structured to monitor and report on marketplaceinformation 12434 relevant to the value of at least one of a party, aset of subsidized loans, and a set of assets, where reporting may be ona set of assets such as one of a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property. The market value datacollection service circuit may be further structured to monitor pricingor financial data for items that are similar to the assets in at leastone public marketplace. In embodiments, a set of similar items forvaluing the assets may be constructed using a similarity clusteringalgorithm 12442 based on the attributes of the assets, such as fromamong a category of the assets, asset age, asset condition, assethistory, asset storage, geolocation of assets, and the like.

FIG. 125 depicts a method 12500 for automating handling of a subsidizedloan including collecting information related to a set of entitiesinvolved in a set of subsidized loan transactions 12502, classifying aset of parameters of the set of subsidized loans involved in thetransactions based on an artificial intelligence service, a model, andinformation from a crowdsourcing service, where the model is trainedusing a training data set of outcomes related to subsidized loans 12504;and modifying terms and conditions of a subsidized loan based on theclassified set of parameters 12508. The set of entities may includeentities among a set of subsidized loans, a set of parties, a set ofsubsidies, a set of guarantors, a set of subsidizing parties, and a setof collateral 12510. A set of subsidizing parties may include amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity 12512. The set ofsubsidized loans may include a municipal subsidized loan, a governmentsubsidized loan, a student loan, an asset-backed subsidized loan, and acorporate subsidized loan 12514. The loan may be a student loan wherethe condition classifying system classifies at least one of the progressof a student toward a degree, the participation of a student in anon-profit activity, and the participation of the student in a publicinterest activity 12518.

FIG. 126 depicts a system including an asset identification servicecircuit 12612 structured to interpret assets 12624 corresponding to afinancial entity 12622 configured to take custody of the assets (e.g.,identifying assets for which a bank may take custody), where an identitymanagement service circuit 12614 may be structured to authenticateidentifiers 12628 (e.g., including a credential 12630) corresponding toactionable entities 12626 (e.g., an owner, a beneficiary, an agent, atrustee, a custodian, and the like) entitled to take action with respectto the assets. For example, a group of financial entities may havepermissions with respect to actions to be taken with respect to anasset. A blockchain service circuit 12616 may be structured to store aplurality of asset control features 12632 in a blockchain structure12618, where the blockchain structure may include a distributed ledgerconfiguration 12620. For instance, transactional events may be stored ina distributed ledger in the blockchain structure where the financialentity and actionable entities may have distributed access through theblockchain structure to share and distribute the asset events. Afinancial management circuit 12610 may be structured to communicate theinterpreted assets and authenticated identifiers to the blockchainservice circuit for storage in the blockchain structure as asset controlfeatures, wherein the asset control features are recorded in thedistributed ledger configuration as asset events 12634 (e.g., a transferof title, death of an owner, disability of an owner, bankruptcy of anowner, foreclosure, placement of a lien, use of assets as collateral,designation of a beneficiary, undertaking a loan against assets,providing a notice with respect to assets, inspection of assets,assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, a designation of an ownership status, and the like).A data collection circuit 12602 may be structured to monitor theinterpretation of the plurality of assets, authentication of theplurality of identifiers, and the recording of asset events, where thedata collection circuit may be communicatively coupled with an Internetof Things system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem. A smart contract circuit 12604 may be structured to manage thecustody of the assets, where an asset event related to the plurality ofassets may be managed by the smart contract circuit based on terms andconditions 12608 embodied in a smart contract configuration 12606 andbased on data collected by the data collection service circuit. Inembodiments, the asset identification service circuit, identitymanagement service circuit, blockchain service circuit, and thefinancial management circuit may include a corresponding applicationprogramming interface (API) component structured to facilitatecommunication among the circuits of the system, such as where thecorresponding API components of the circuits further include userinterfaces structured to interact with users of the system.

FIG. 127 depicts a method including interpreting assets corresponding toa financial entity configured to take custody of the plurality of assets12702, such as where the interpreting of the assets may includeidentifying the plurality of assets for which a financial entity isresponsible for taking custody. The method may include authenticatingidentifiers (e.g., including a credential) corresponding to actionableentities (e.g., owner, a beneficiary, an agent, a trustee, and acustodian) entitled to take action with respect to the plurality ofassets 12704, such as where authenticating the identifiers includesverifying the identifiers corresponding to actionable entities areentitled to take action with respect to the assets. The method mayinclude storing a plurality of asset control features in a blockchainstructure (e.g., including a distributed ledger configuration) 12708(e.g., the blockchain structure may be provided in conjunction with ablock-chain marketplace, utilize an automated blockchain-basedtransaction application, the blockchain structure may be a distributedblockchain structure across a plurality of asset nodes, and the like).The method may include communicating the interpreted assets andauthenticated identifiers for storage in the blockchain structure asasset control features, where the asset control features may be recordedin the distributed ledger configuration as asset events 12710. Themethod may include monitoring the interpretation of the assets,authentication of the identifiers, and the recording of asset events12712, such as where asset events may include transfer of title, deathof an owner, disability of an owner, bankruptcy of an owner,foreclosure, placement of a lien, use of assets as collateral,designation of a beneficiary, undertaking a loan against assets,providing a notice with respect to assets, inspection of assets,assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, and designation of an ownership status. Inembodiments, monitoring may be executed by an Internet of Things system,a camera system, a networked monitoring system, an internet monitoringsystem, a mobile device system, a wearable device system, a userinterface system, an interactive crowdsourcing system, and the like. Themethod may include managing the custody of the assets, where an assetevent related to the plurality of assets may be based on terms andconditions embodied in a smart contract configuration and based on datacollected by a data collection service circuit 12714. The method mayinclude sharing and distributing the asset events with the plurality ofactionable entities 12718. The method may include storing assettransaction data in the blockchain structure based on interactionsbetween actionable entities 12720. An asset may include a virtual assettag where interpreting the assets comprises identifying the virtualasset tag (e.g., storing of the asset control features may includestoring virtual asset tag data, such as where the virtual asset tag datais location data, tracking data, and the like). For instance, anidentifier corresponding to the financial entity or actionable entitiesmay be stored as virtual asset tag data.

FIG. 128 depicts a system 12800 including a lending agreement storagecircuit 12802 structured to store a lending agreement data 12804including a lending agreement 12814, wherein the lending agreement mayinclude a lending condition data 12816. In embodiments, the lendingcondition data may include a terms and condition data 12818 of the atleast one lending agreement related to a foreclosure condition 12822 onan asset 12820 that provides a collateral condition 12824 related to acollateral asset 12826, such as for securing a repayment obligation12828 of the lending agreement. The system may include a data collectionservices circuit 12806 structured to monitor the lending condition dataand to detect a default condition 12808 based on a change to the lendingcondition data. Further, the data collection services circuit mayinclude an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, and aninteractive crowdsourcing system. The system may include a smartcontract services circuit 12810 structured to, when the defaultcondition is detected by the data collection services circuit, interpretthe default condition 12812 and communicate a default conditionindication 12830, such as to initiate a foreclosure procedure 12832based on the collateral condition. For instance, the foreclosureprocedure may configure and initiate a listing of the collateral asseton a public auction site, configure and deliver a set of transportinstructions for the collateral asset, configure a set of instructionsfor a drone to transport the collateral asset, configure a set ofinstructions for a robotic device to transport the collateral asset,initiate a process for automatically substituting a set of substitutecollateral, initiate a collateral tracking procedure, initiates acollateral valuation process, initiate a message to a borrowerinitiating a negotiation regarding the foreclosure, and the like. Thedefault condition indication may be communicated to a smart lock and asmart container to lock the collateral asset. The negotiation may bemanaged by a robotic process automation system trained on a training setof foreclosure negotiations and may relate to modification of interestrate, payment terms, collateral for the lending agreement, and the like.In embodiments, each of the lending agreement storage circuit, datacollection services circuit, and smart contract services circuit mayfurther include a corresponding application programming interface (API)component structured to facilitate communication among the circuits ofthe system, where the corresponding API components of the circuits mayinclude user interfaces structured to interact with a plurality of usersof the system.

FIG. 129 depicts a method 12900 for facilitating foreclosure oncollateral, the method including storing a lending agreement dataincluding a lending agreement, where the lending agreement may include alending condition data, such as where the lending condition dataincludes a terms and condition data of the lending agreement related toa foreclosure condition on an asset that provides a collateral conditionrelated to a collateral asset for securing a repayment obligation of theat least one lending agreement 12902. The method may include monitoringthe lending condition data and to detecting a default condition based ona change to the lending condition data 12904. The method may includeinterpreting the default condition 12908 and communicating a defaultcondition indication that initiates a foreclosure procedure based on thecollateral condition 12910. For instance, the foreclosure procedure mayconfigure and initiate a listing of the collateral asset on a publicauction site, configure and deliver a set of transport instructions forthe collateral asset, configure a set of instructions for a drone totransport the collateral asset, configure a set of instructions for arobotic device to transport the collateral asset, initiate a process forautomatically substituting a set of substitute collateral, initiate acollateral tracking procedure, initiate a collateral valuation process,initiate a message to a borrower initiating a negotiation regarding theforeclosure, and the like 12914. The default condition indication may becommunicated to a smart lock and a smart container to lock thecollateral asset 12912. The negotiation may be managed by a roboticprocess automation system trained on a training set of foreclosurenegotiations 12918 and may relate to modification of interest rate,payment terms, collateral for the lending agreement, and the like. Inembodiments, communications may be provided by a correspondingapplication programming interface (API) 12920, where the correspondingAPI may include user interfaces structured to interact with a pluralityof users.

Market Orchestration System Platforms

Referring to FIG. 201 , the present disclosure relates to a marketorchestration system platform 20500 that is configured to facilitateelectronic marketplace transactions, referred to herein in thealternative as the “platform,” the “system” or the like, with such termscomprising various alternative embodiments involving various sets ofcomponents, modules, systems, sub-systems, processes, services, methods,and other elements described herein and in the documents incorporatedherein by reference. According to embodiments herein, a marketplace mayrefer to an environment where assets may be listed and traded by buyersand sellers. Assets may refer to commodities, physical assets, digitalassets, services, stocks, bonds, marketplace-traded funds (ETF), mutualfunds, currencies, foreign exchange (FX), artwork and other works ofauthorship, alternative assets, recycled plastics, digital 3D designs,digital gaming assets, virtual goods, real estate, placement rights(such as for advertising), cryptocurrencies, metals and alloys, energyresources, derivatives (such as futures, forwards, options, puts, calls,and swaps), 3D printing capacity, digital twins, storage, intellectualproperty (e.g., trade secrets, patents, trademarks, designs, know how,privacy rights, publicity rights, and others), instruction sets, hybridinstruments, synthetic instruments, tranches of assets (includingsimilar and mixed-asset tranches), streams of value (such as ofinterest), certificates of deposit (CDs), and the like, as well asportions of the above (such as divisible and undivided interests),hybrids of the above, and aggregates of the above (including tranches ofsecurities, mutual funds, index funds, and others).

Referring to FIG. 202 , the market orchestration system platform 20500may include an exchange suite 20204, an intelligent services system20243, a digital twin system 20208, an intelligent agent system 20210,and a quantum computing system 20214.

In embodiments, the platform 20500 includes an API system 20238 thatfacilitates the transfer of data between a set of external systems andthe platform 20500. In some embodiments, the platform 20500 includesmarketplace databases 20216 that store data relating to marketplaces,whereby the marketplace data is used by the exchange suite 20204, theintelligent services system 20243, the digital twin system 20208, theintelligent agent system 20210, and the quantum computing system 20214.

As used herein, quantum computing may refer to the use ofquantum-mechanical phenomena (such as superposition and entanglement) toperform computation. Quantum computers may refer to computers thatperform quantum computations. Quantum computers may be configured tosolve certain computational problems, such as integer factorization(which underlies RSA encryption), with a fraction of the computationalmemory of traditional computers.

In some embodiments, the exchange suite 20204 provides a set of variousmarketplace tools that may be leveraged by marketplace participants(such as traders and brokers). The marketplace tools may include, butare not limited to, a strategies tool 20240, a trading practice tool20233, a news tool 20244, a screener tool 20248, a market monitoringtool 20250, an entity profile tool 20252, an account management tool20254, a charting tool 20258, an order request system 20260, and a smartcontract system 20262. In embodiments, the strategies tool 20240 isconfigured to enable the creation and/or testing of pre-defined tradestrategies. In embodiments, the pre-defined trade strategies may beconfigured for a particular asset type. In embodiments, the tradingpractice tool 20233 allows users to test and simulate strategies usingan account funded with virtual money. In embodiments, the news tool20244 may be configured to stream live media (e.g., CNBC), news feeds,and/or social media feeds (e.g., Twitter). In embodiments, the livemedia, news feed, and/or social media feed content may be related to theone or more asset(s) traded in the marketplace. In embodiments, thestreamed live media content, news feed content, and/or social media feedcontent may be selected by an AI system, such as one that is trainedbased on selections by expert users and/or trained based on outcomes ofusage, such as outcomes indicating successful trading activities andother outcomes noted throughout this disclosure. In embodiments, usersmay define streamed live media content, news feed content, and/or socialmedia feed content to be displayed by the news tool 20244 via agraphical user interface. In embodiments, the screener tool 20248 allowsusers to filter assets by setting criteria via the graphical userinterface. In embodiments, the market monitoring tool 20250 allows usersto view marketplace-related data, graphics, heatmapping, watch lists,and the like. In embodiments, the entity profile tool 20252 allows usersto view profiles of marketplace entities (e.g., company profiles, assetprofiles, broker profiles, trader profiles, and the like) wherein theprofiles contain information related to the respective marketplaceentities. For example, the entity profile tool 20252 may allow a user toview an asset profile for an asset listed in the marketplace. Inembodiments, the account management tool 20254 allows users to managetheir accounts and to view account information (e.g., account balances,history, orders, and positions). In embodiments, the charting tool 20258allows users to build charts related to assets to identify trends. Forexample, the charting tool may allow users to chart price over time foran asset to identify trends in price movement. The quantum computinginterface 20241 enables the interface between the exchange suite 20204and the quantum computing system 20214.

Referring to FIG. 203 , the market orchestration system platform 20500may include a marketplace configuration system 20302. In embodiments,the marketplace configuration system 20302 interfaces with aconfiguration device 20304. The configuration device 20304 may consistof any suitable computing device (or set of devices) that executes aclient application 20312 that connects to the platform 20500 to provideconfiguration parameters 20306. Examples of configuration devices 20304may include, but are not limited to, mobile devices, desktop computers,artificial intelligence-based trading systems, and third-partyapplications that interface to the marketplace API System 20238.

In embodiments, these third-party applications are thin layers that mayconsist of a mash-up of different APIs connecting various back endservices. For example, the third-party applications may interface to themarketplace API and to a weather API, if weather is deemed relevant totrading a particular asset (e.g., in a market for 3D printed snow skis).In embodiments, these mashup environments connect to various systemswithout the different back end systems requiring knowledge of themash-up environments. In some embodiments of the platform 20500,security is centrally managed or outsourced. For example, GoogleAuthentication may be used via OAuth certificates providing for themash-up to connect to multiple systems and not requiring multiplelogins, such that it supports single sign-on.

In embodiments, the market orchestration system platform 20500 may bemulti-threaded and provide for seamless real-time monitoring andexecution of tasks. In some embodiments, the platform 20500 supportshigh-performance device implementation using compiled languages,including, but not limited to, SwiftUI™ and Flutter™.

In embodiments, the market orchestration system platform 20500 may beconfigured to support automated testing. For example, building reliablehandling of failures and errors may prevent an application crashinghalfway through a trade.

In embodiments, internal device storage of the platform 20500 is basedon encrypted data and encrypted use of memory to protect sensitiveinformation, such as personal data, trade secrets and/or sensitivefinancial information, or the like, from discovery and hacking. In someembodiments, the platform 20500 is configured to enable obfuscation oftrading network patterns to prevent third parties from monitoringnetwork traffic to discover major trading events.

In embodiments, the platform 20500 is configured to support differenttypes of traders, including retail traders, institutional traders,individual traders, secondary market traders, brokers, dealers, buyers,sellers, market makers, and others, as well as various other parties andcounterparties to marketplace transactions, such as regulators,procurement officers, tax officials and other government personnel,reporters, analysts, bankers, custodial agents, trustees, proxyholders,service providers, ratings agencies, auditors, assessors, accountants,compliance parties, legal service providers, lenders, and many others.References to “traders” or “users” in examples and embodimentsthroughout this disclosure should be understood to encompass any ofthese, except where context indicates otherwise. The different types oftraders and other parties will likely have different needs aroundperformance specifications for the system, such as relating to latencyof execution, latency of data availability, overall availability,quality-of-service, bandwidth, throughput, failover, failure avoidance,error correction, reconciliation, disaster recovery, and the like of thetrading system, as well as varying needs for handling of automationcapabilities (such as algorithmic execution) and varying needs for tradetypes and handling of asset classes (e.g., enabling exchange and/orarbitration opportunities between different market environments), andthe like.

In embodiments, the platform 20500 is configured to support marketplaceparticipant user devices 20218 in executing a set of atomic transactionsin a sequence. In embodiments, these atomic transactions may requiredependency (such as selling a first asset before buying a second asset).In some embodiments, the atomic transactions may be independent ofsequence (such as selling an asset as fast as possible). In embodiments,orchestration may include generation and/or configuration of policies,rules, business logic, or the like that define sets of allowabletransaction patterns by asset type, trade type, trader type,jurisdiction, or the like. In embodiments, these elements (collectivelyreferred to for simplicity as “policies”) may be embodied in codeelements that are attached to workloads and/or workflows for transactionexecution, such that as transactions types are defined for particularasset classes, trade types, or the like, the policies are embedded into,integrated with, linked to, and/or wrapped around transaction objects,entities, states, and actions, such that each instance of a transactioncarries with it the code necessary to recognize and apply policies,including context-sensitive policies, such as ones that aresystem-dependent, jurisdiction dependent, time-dependent,role-dependent, or the like.

In embodiments, the platform 20500 includes a message response system.The marketplace participant user device 20218 may consistently respondto real-time messages (such as notifications of events relating tomarket positions, such as trades, price changes, asset-class-relatedevents, and many others). The response mechanism within the marketplaceparticipant user device 20218 may be configured to respond to thesemessages with automated trading responses and/or with displayednotifications to the user of the device.

In embodiments, platform 20500 includes, integrates with, and/or linksto an algorithm-based trading system having the ability to create, test,modify, and/or execute a set of automated algorithms. These automatedalgorithms may be controlled and managed by the marketplace participantuser device 20218 and may be adjusted in real-time in response tochanges in events or in response to user controls. In embodiments, thealgorithm-based trading system may be constantly running in an extremelysecure tier of an execution environment 20202 and may be run with orwithout the knowledge of the marketplace. In embodiments, thealgorithm-based trading system may include algorithm control systems. Ifan algorithm is hidden in nature, the algorithm control systems mayutilize obfuscation behaviors to constrain the ability of the executionenvironment 20202 to determine that artificial intelligence engines areundertaking trading activities.

In embodiments, the platform 20500 includes marketplace databases 20216.Marketplace databases 20216 may be ACID-compliant, and this ACIDcompliance may include building the data layer in the ACID-compliantdatabase following ACID-compliant data management practices.ACID-compliant data management practices may include, but are notlimited to, handling of duplication or aggregation of data as a part ofa transaction or with a known latency against real-time, building anormalized data structure where data is not duplicated, rigoroustime-stamping of all data to allow for seamless recovery of past statesof the system, and transaction replication, which allows for real-timereplication of fine grained data.

In embodiments, data may be configured differently for different typesof marketplaces. The database schema abstraction may impact theimplementation details for ACID compliance. For example, highly abstractstorage may lead to a middle tier ACID implementation layer. Inembodiments, the marketplace databases 20216 may include file systems,normalized schemas, denormalized schemas, replicated data, and/or starschemas. In embodiments, the marketplace databases 20216 may enableaudit trails. In embodiments, the marketplace databases 20216 may enableblockchain sequencing for accounting resilience.

In some embodiments, the storage levels for the marketplace databases20216 may include the storage of individual trades and/or the storage ofaggregation of the trading information (current state only). Inembodiments, historical trading information may be stored as thespecific requests to allow for auditing and/or as more processedversions of the trading. For example, if trading is at an extremely highvolume, the system may only be able to hold the current state; however,for audit purposes, a log of all historical requests is stored in alinear sequence, providing the ability to reconstruct a position in themarket.

In embodiments, the marketplace configuration system 20302 provides aninterface (e.g., a graphical user interface (GUI)) by which a user(e.g., a marketplace host) may configure and/or launch a marketplace.While described as a marketplace host, the configuration of themarketplace may be performed by other users, including, but not limitedto, brokers and traders (e.g., buyers and/or sellers). In embodiments,the configuration of the marketplace may be performed automatically, asdescribed in greater detail throughout this disclosure.

Referring to FIG. 204 , a method is provided for launching a newmarketplace according to some embodiments of the present invention. At20401, a marketplace opportunity identification module 20310 identifiesan opportunity to facilitate a new marketplace and/or identifies demandfor a new marketplace. In embodiments, the marketplace opportunityidentification module 20310 interfaces with third party electronictrading platforms (e.g., buying and selling platforms withshopfront-style trading), social networks, news sources, and the likeand applies continuous automated monitoring and/or human-controlledmonitoring of these sources for marketplace opportunities. For example,marketplace opportunity identification module 20310 may automaticallydetect a need for a marketplace for an asset class (e.g., a marketplacefor digital twins) from an online source, such as a discussion board. Inthis example, marketplace opportunity identification module 20310monitors demand and/or other factors indicating potential economicopportunity through the application of models, analytics, or the like,such as linear regression, and/or the application of artificialintelligence systems, such as neural networks or other AI systemsdescribed throughout this disclosure and the documents incorporated byreference herein. Continuing the present example, if the marketplaceopportunity identification module 20310 finds that there is substantialdemand for a marketplace for digital twins (such as a marketplace ofdigital twins of particular items), the marketplace opportunityidentification module 20310 may make a decision to build a newmarketplace to address such demand, enabling traders to buy and sell thedigital twins. In examples, the marketplace opportunity identificationmodule 20310 may make a decision to build a new marketplace forrefurbished exercise equipment upon finding a substantial demand forsuch equipment via monitoring social networks. Continuing the examplefurther, the refurbished exercise equipment may be delivered to thebuyer, or the exercise equipment may be traded without the equipmentbeing delivered to the buyer, thus creating liquidity in the market. Inexamples, marketplace opportunity identification module 20310 mayautomatically detect a need for airplane kit certification services froma trading platform chat discussion. Alternatively, a user (e.g.,marketplace host) may identify a market opportunity and request tolaunch a new marketplace for one or more assets via the graphical userinterface.

At 20402, the marketplace configuration system 20302 receivesmarketplace opportunity data (asset(s), asset type(s), asset data, assetdemand data (demand quantities, demand locations, demand demographics,demand indicators), and the like) from the marketplace opportunityidentification module 20310. In some embodiments, a user may define theassets and/or type(s) of assets that may be listed in the marketplace.In embodiments, the user may select different assets and/or asset typesthat will be supported for the marketplace by the platform 20500 via aGUI presented by the marketplace configuration system 20302. Forexample, the user may select different assets from a menu of assetsand/or select different types of assets from a menu of asset types.

At 20403, the marketplace configuration system 20302 determines,optionally automatically, marketplace configuration parameters 20306based on the received market opportunity data. In embodiments, themarketplace configuration system 20302 optionally leverages machinelearning and/or artificial intelligence to automatically selectmarketplace configuration parameters 20306, such as to optimize themarketplace for efficiency, risk management, profitability, and/or othermeasures. In some embodiments, a user may enter marketplaceconfiguration parameters via the graphical user interface. Themarketplace configuration parameters 20306 may include, but are notlimited to, assets, asset types, description of assets, method forverification of ownership, method for delivery of traded goods,estimated size of marketplace, methods for advertising the marketplace,methods for controlling the marketplace, regulatory constraints, datasources, insider trading detection techniques, liquidity requirements,access requirements (such as whether to implement dealer-to-dealertrading, dealer-to-customer trading, or customer-to-customer trading),anonymity (such as determining whether counterparty identities aredisclosed), continuity of order handling (e.g., continuous or periodicorder handling), interaction (e.g., bilateral or multilateral), pricediscovery, pricing drivers (e.g., order-driven pricing or quote-drivenpricing), price formation (e.g., centralized price formation orfragmented price formation), custodial requirements, types of ordersallowed (such as limit orders, stop orders, market orders, andoff-market orders), supported market types (such as dealer markets,auction markets, absolute auction markets, minimum bid auction markets,reverse auction markets, sealed bid auction markets, Dutch auctionmarkets, multi-step auction markets (e.g., two-step, three-step, n-step,etc.), forward markets, futures markets, secondary markets, derivativesmarkets, contingent markets, markets for aggregates (e.g., mutualfunds), and the like), trading rules (e.g., tick size, trading halts,open/close hours, escrow requirements, liquidity requirements,geographic rules, jurisdictional rules, rules on publicity, insidertrading prohibitions, conflict of interest rules, timing rules (e.g.,involving spot-market trading, futures trading and the like) and manyothers), asset listing requirements (e.g., financial reportingrequirements, auditing requirements, minimum capital requirements),deposit minimums, trading minimums, verification rules, commissionrules, fee rules, marketplace lifetime rules (e.g., short-termmarketplace with timing constraints vs. long-term marketplace), andtransparency (e.g., the amount and extent of information disseminated).In some embodiments, marketplace configuration parameters 20306 mayinclude allowing failed trades with no recourse.

In some embodiments, each type of asset has a predefined set of defaultconfiguration parameters. In some embodiments, the set of configurationparameters for each type of asset may be customized (e.g., by themarketplace host). In these embodiments, a user may define themarketplace configuration parameters that govern the marketplace for atype of asset.

In embodiments, a user, such as a buyer, seller, broker, agent, or thelike, may define marketplace configuration parameters under which theuser is willing to engage in trading activity and the marketplaceopportunity identification module 20310 may use the defined parametersto identify opportunities to establish configurations that willencourage active trading among an aggregate set of parties that shareconfiguration preferences. For example, a buyer may indicate apreference to trade in day-ahead futures of a defined type of token andbe matched with sellers who hold such tokens are similarly interested inday-ahead trading.

At 20404, the marketplace configuration system 20302 makes or enablesone or more decisions related to the setup and nature of the marketplaceto be built. In this step, the marketplace configuration system 20302may evaluate the received marketplace opportunity data and/ormarketplace configuration parameters 20306 and prioritize theimplementation of the marketplace based on a set of desired outcomes(such as overall profitability of the marketplace, efficiency of themarketplace, generation of threshold levels of overall participationand/or participation by parties of desired types, generation ofthreshold levels of trading activity, and the like). Configuration maybe based on a model or plan of marketplace development, such as one thatindicates and manages phases of marketplace development, such as marketinitiation (e.g., involving allocations of tokens, credits, tradingrights, or the like according to desired rules or business logic), earlystage marketplace development (such as involving offering incentives,subsidies, promotions or the like to facilitate development of tradingactivity to threshold levels), healthy marketplace operation (such asadjusting, optionally automatically, parameters of operation of themarketplace (such as smart contract terms, APIs, trading rules, or thelike) upon receipt of indicators that the marketplace has reachedthreshold levels of trading and participation by desired numbers andtypes of counterparties and supporting users), and unhealthy operation(such as where one or more desired characteristics of market operationare outside desired ranges or thresholds, e.g., where trading is toothin, where gaming behavior is evident, where undue market power isevident (e.g., the market is cornered), where front-running is observed,or the like). In embodiments, artificial intelligence systems may betrained to recognize or understand the stage of a marketplace and toautomatically adjust parameters of configurations of the marketplacebased at least in part on the understood stage, including any of theconfiguration or other marketplace parameters noted throughout thisdisclosure. The artificial intelligence system may be trained by deeplearning on outcomes, by use of a training set of data involving expertconfiguration by human operators, by combinations of the above, or byother techniques described herein or in the documents incorporated byreference, or other training techniques known to those of skill in theart.

In embodiments, the marketplace configuration system 20302 evaluates andexperiments with new marketplaces, which may involve setting up testenvironments to determine if the marketplace is technologically oreconomically feasible and/or evaluating the marketplace with a test setof traders, with a test set of trading rules, with a test set of assets,with a test set of initiation parameters (such as incentives orpromotions) or the like. In embodiments, digital twins may be generatedby the digital twin system 20208 to perform simulations so that theviability of the suggested marketplace may be evaluated. Digital twinsmay include twins of goods (physical and digital) and other assets,twins of users, twins of environments and facilities, and other items.For example, a digital twin may track and represent conditions ofphysical items the ownership rights to which are to be traded in amarketplace, reflect impacts of environmental conditions (e.g., weather,climate, or other physical processes, and many others) on items, and thelike, thereby allowing testers to observe impacts of physical changes inthe marketplace (e.g., to test or simulate impacts of depreciation ordegradation). Twins can similarly simulate marketplace activity, such astrading levels and patterns, price changes, and many others. Inembodiments, the marketplace configuration system 20302 may determinethat certain marketplace configuration parameters 20306 are unfavorable,and as a result, the marketplace configuration system 20302 may updatethe configuration parameters 20306 to improve and/or optimize theperformance of the marketplace.

At 20405, the marketplace configuration system 20302 determines datasources to support the marketplace, including optionally configuring oneor more databases. Configuration of a core database architecture may, inembodiments, facilitate various performance capabilities of themarketplace. Database types that may be implemented may includerelational databases, SQL and NoSQL databases, highly real-timedatabases, graph databases, distributed databases, elastic databases,object-oriented databases, and the like, including various combinationsof the above.

At 20406, the marketplace configuration system 20302 determines thearchitecture of the marketplace, which may include determining the toolsand/or libraries used to support the marketplace. Decisions at this stepmay involve careful planning of the algorithms that may be used by themarketplace and around the key requirements for the system. Keyarchitecture considerations may include logging requirements, auditrequirements, acceptable latency, failover requirements, disasterrecovery requirements, acceptable input/output volumes per period oftime, volume of trades, requirements for complex transactions, andresolution of trades that take longer time periods.

At 20407, the marketplace configuration system 20302 determines thedesign of the data within the selected database environment using datamodeling and data flow design tools. The data modeling processes mayleverage data modeling tools and/or intelligent agents 20234 to lay outnew schemas from scratch or to use existing template schemas. Inembodiments, these processes may be fully automated using sophisticatedautomatic schema design tooling. Data modeling tools that may beimplemented include, but are not limited to, ERWIN™, Visio™, andWhereScape RED™. Building on the architecture for the underlyingdatabase schema may be an iterative process involving block 20406 andblock 20407 to determine the overall system architecture.

At 20408, marketplace configuration system 20302 configures amarketplace object 20308 in accordance with the determined architecture,configuration parameters 20306, and the like. The establishment of a newmarketplace in this step may be either an entirely new kind ofmarketplace or an implementation of an existing marketplace withadjusted parameters. The marketplace configuration system 20302 readsthe input parameters and loads them into its system. Key tasks in thisstep may include filling in default values, determining monitoringparameters (to determine when market is operating outside of itsdesigned nature), management of failure and exceptions, and handling ofhacking and security.

At 20409, the marketplace configuration system 20302 connects databasesto the marketplace object 20308. In embodiments, the underlying databasebusiness rules are version-controlled and overlaid withversion-controlled marketplace object 20308 that provides for theexecution of trades. In embodiments, the marketplace object 20308 holdsa set of metadata that defines the overall market operationalparameters, the state held within this object can be held in versionsoftware (such as GIT or a version-controlled database). Thisversion-controlled marketplace object 20308 may be used by the executionenvironment 20202 to operate the marketplace. In embodiments, theunderlying database is designed to hold information regarding assets,transactions, and market positions held by buyers and sellers, as wellas optionally holding various additional data and/or metadata about theabove and other elements relevant to the marketplace, such as externalfactors that may impact buyers, sellers, assets, trading, or the like.In embodiments, connection information may include information aboutmarkets for derivative markets. For example, a marketplace for fooddelivery may include traders in derivative cash-settled marketplaceswhere the traders are betting on the future value of commodities in amonitored hot food delivery marketplace. Once the marketplace object20308 is connected to the underlying database, the logic of theoperating market may be tied directly to the data that is generated,which places a requirement that future releases of the marketplaceobject 20308 need to be able to seamlessly upgrade without breakinghistoric data collection rules. Future upgrades of the marketplaceobject 20308 may include upgrade logic that may include procedures thatupdate the underlying database to make it compliant with therequirements of the future database.

In some embodiments, a user (e.g., a marketplace host) may connect oneor more data sources 20224 to the market orchestration system platform20500. Examples of data sources 20224 that may be connected to theplatform 20500 may include, but are not limited to, the sensor system20274 (e.g., a set of IIoT sensors), news sources 20278, the market data20280 (such as level 1 and level 2 market data), the fundamental data20282, reference data 20284, historical data 20288, third party datasources 20290 that store third party data, edge devices 20292,regulatory data 20294 (e.g., SEC filings), social network data 20298,and message board data 20201. Level 1 market data may refer to thereal-time best bid-offer-volume data for a given asset while level 2market data may refer to the real-time quotes for each market maker(e.g., individual market participant or member firm of a marketplace).Fundamental data may refer to data relating to a marketplace asset'sunderlying value and potential for future growth (e.g., revenue,earnings, and cash flow for a yield-producing asset, appraised orassessed value, or the like). Reference data may refer to marketplaceentity identifiers used to complete and settle financial transactions.The data sources 20224 may include additional or alternative datasources without departing from the scope of the disclosure. Once theuser has defined the configuration of a marketplace, wherein theconfiguration includes the selected asset types and trading rules, theuser may then define the data sources 20224 that are connected to theplatform 20500. In some embodiments, data from one or more of the datasources may be fused and/or analyzed before being fed into the platform20500.

At 20410, the marketplace configuration system 20302 launches themarketplace. In embodiments, the marketplace configuration system 20302may leverage cluster management tools (such as Trinity X™) to change therun-time parameters and operational nature of instances, allowing forthe continuous operation in the face of workload demands. Inembodiments, the marketplace configuration system 20302 may leveragehigh performance computing (HPC) clustering. In embodiments, clustersmay be dynamically changeable based on the requirements of specificmarketplaces or system workloads. In embodiments, the marketplaceconfiguration system 20302 may allow for some marketplaces to be shutdown in response to workloads (including excessive or inadequate demand)or in response to other factors, such as improper trading patterns(e.g., triggering of a market crash or bubble by unconstrainedalgorithmic trading systems), exogenous events (e.g., changes in othermarkets, natural disasters, civil unrest, or the like), etc. In someembodiments, the marketplace configuration system 20302 may allow forservice-level agreements (SLAs) to be changed in response to demand andother factors. In embodiments, the marketplace configuration system20302 may limit users on the system or change entry requirements fortraders in an environment.

In embodiments, the marketplace configuration system 20302 enables auser (e.g., a marketplace host) to define the users that may accessand/or may not access the marketplace. For example, the user may definea blacklist of users that may not access the marketplace and/or define awhitelist of users that may access the marketplace. As examples, awhitelist may include members of a trade organization, a set of membersof an industry consortium, a set of members of a treaty, members of acorporate group, members of a list of permitted parties (e.g., partieson a government contracting schedule or the like), a set of parties toan agreement, or others. In embodiments, the marketplace configurationsystem 20302 enables a marketplace host to invite other users to tradein the marketplace. In embodiments, the platform 20500 may be configuredto enable the creation of trader accounts for buyers and sellers. Inembodiments, the platform 20500 may be configured to automaticallygenerate a trader profile associated with each created account.

In embodiments, the platform 20500 may include serverless environments.In these serverless environments, the application software may rundirectly on “bare metal” computational infrastructure or incomputational systems optimized for execution. The serverlessenvironments may include a set of cloud environments where the cloudprovider is completely responsible for service level, such as latency ofresponse, overall memory availability, backup, disaster recovery, loadbalancing, and the like. In embodiments, the cloud environments mayemploy elastic load balancing, including application load balancing,network load balancing (including path-sensitive or route-sensitive loadbalancing), and the like.

In embodiments, the platform 20500 may allow users to add assets suchthat the assets are listed in the marketplace. In embodiments, theplatform 20500 may allow users to remove assets from the marketplacesuch that the assets are no longer listed in the marketplace. Inembodiments, the platform 20500 may be configured to automaticallygenerate a profile associated with each asset. In embodiments, adding anasset may include digitizing an asset. Digitizing an asset may beperformed by capture in digital media (such as scanning, photography,video, audio recording, or the like), by generation of digital content(such as entering descriptive information into an interface), or thelike. Digitizing may include populating a digital object for the assetthat corresponds to the class of the asset, where the object reflectsparameters and attributes of the asset class and/or a data schema thatis appropriate for the asset and for the marketplace. Attributes mayinclude digital representations of analog data (such as transformed,compressed, or similar data), physical data, logical data, outputs ofnatural language processing, metadata elements, and the like. Digitizingmay include automated extraction, transformation and loading of data,including steps of normalization, deduplication, clustering, scaling,cleansing, filtering, linking (such as linking to one or moreidentities), and the like. Digitizing may be performed by artificialintelligence, such as by robotic process automation, where theartificial intelligence system is trained to digitize an asset accordingto a data schema, object class, or the like based on a training set ofdata wherein one or more experts has digitized assets of the same orsimilar type. In embodiments, adding an asset may include uploadingmetadata related to the asset. In embodiments, adding an asset mayinclude uploading one or more photos, videos, virtual realityexperiences, documentation, digital twins, and the like.

In embodiments, a user may create an order request for an offer to buyor sell one or more assets. In embodiments, a user may select an optionto create a new order request. In some of these embodiments, the usermay be presented a GUI to provide one or more parameter values. Forinstance, the GUI may include fields for the user to identify one ormore assets and define a requested action (e.g., buy or sell), quantityof asset(s), order type (e.g., limit order), price, time-in-force,special instructions, advanced order entry, and the like. Inembodiments, the platform 20500 may be configured to enable thecancellation of orders. In some of these embodiments, the ordercancellation may be triggered upon the detection of an event, such as byone or more monitoring and/or detection systems described herein or inthe documents incorporated herein by reference. Events that result incancellation may include price shifts in the marketplace or anothermarketplace, changes in eligibility or other statuses of a party,changes in state of an asset, changes in regulatory or policy factors,cancellation actions by a party, and others.

In some implementations, the platform 20500 may include an executionengine 20228. In embodiments, the execution engine 20228 may beconfigured to receive an order request from a party to execute atransaction for one or more assets listed in a marketplace. Inembodiments, the execution engine 20228 may be configured to selectivelyexecute a transaction based on the order request. For example, theexecution engine 20228 may receive an order request, which may include,but is not limited to, requested action (e.g., buy or sell), quantity,asset(s) (e.g., stock symbol), order type (e.g., limit order), price,and time-in-force. The execution engine 20228 may, upon determining thatthe requested order is permissible (e.g., the assets are not illegal andthere is no detected fraudulent activity), feed this information into anintelligent matching system 20230 that matches the order to one or moreother orders (e.g., matching a buy order with a corresponding sell orderfor the same asset type where the respective prices are compatible). Inembodiments, the execution engine 20228 may receive matched orders fromintelligent matching system 20230 and execute the matched orders. Inembodiments, the execution engine 20228 may generate a tradeconfirmation and send the trade confirmation to the one or more tradersassociated with an executed transaction.

Smart contracts are executable computer programs that operate uponrelevant inputs from data sources and apply logic that embodies a set ofapplicable contract terms and conditions to produce outputs. Inembodiments, smart contracts may be compiled into a data block in adistributed ledger or other data repository and may be configured to bedeployed on computational infrastructure with appropriate provisioningof computational resources, definition of interfaces (e.g., APIs), andsecurity framework (e.g., setting permissions for identities, roles, andthe like). Once deployed to a distributed ledger or other securecomputational platform, the smart contract may be accessed by dataconnection by various computational systems, such as to accept inputsand to provide outputs. In embodiments, a smart contract is deployed ona ledger that provides cryptographic security, such as involving ablockchain, such that the smart contract may be executed with confidencethat it has not been modified by a malicious actor. While referred to as“smart contracts” because they may represent and implement agreementsbetween various parties, such as regarding the transfer ofcryptocurrency, the purchase and sale of goods, and transactionsinvolving other types of assets, a smart contract does not strictly haveto represent an explicit contractual arrangement; for example, a smartcontract may implement business logic upon inputs to provide outputswithin a workflow or business process.

In embodiments, a smart contract may be written in program code using ascripting language such as JavaScript, Solidity, Python, or otherscripting languages, or an object coding language, such as Java, or amachine coding language such as C or C++. When a smart contract isdeployed, such as into a distributed ledger or other computationalsystems, the program code may be processed into a block by a participantand written to the distributed ledger or other computational systems inthe same manner any other data block is written to the distributedledger or system (for example, in exchange for a fee paid to the nodeparticipant who compiles the contract/program). In embodiments, theprocess of deploying the smart contract may include compiling theprogram code into bytecode, object code, binary code, or some otherexecutable form. When the smart contract is successfully deployed, thesmart contract/data block containing the smart contract may be assignedan address, which may subsequently be used to access the smart contractand execute the functionality provided therein. In embodiments, a smartcontract may include a connection to or provision of an ApplicationProgramming Interface (API), a connection to or provision of anApplication Binary Interface (ABI), which is analogous to an API, orother interfaces (such as a bridge, gateway, connector, portal, or otherdata integration interface), such that the smart contract may interfacewith external software modules and systems. In this way, the smartcontract may interact with various software modules (e.g., a walletapplication and/or other smart contracts), data sources (such as datafeeds, event streams, logs, search engines, and many others), and/or auser of the smart contract. In embodiments, a smart contract may haveAPI, ABI, or other connection interface information associated therewiththat defines a manner by which a user leverages the interface so thatthe user can interact with the various functions of the smart contract.In embodiments, the connection interface information describes thevarious functions and methods provided as part of the smart contract sothat they can be accessed by the user or the user's software.

Once a smart contract has been deployed, the smart contract may then beused by access to the address of the smart contract according to definedpermissions, which may include open access and/or private access. Inembodiments, executing the contract, or a portion of it, does notnecessarily incur fees unless required as part of a step in the contract(such as fees required to update a distributed ledger upon which thecontract is deployed). In embodiments, many different users may utilizethe contract/program simultaneously to govern their own specificagreements or transactions.

In some embodiments, the smart contract may be invoked by conditionallogic (e.g., as defined in the program code of the smart contract, ofanother smart contract, or being executed by a software system). Forexample, a smart contract may be invoked upon the occurrence of externalor internal events. An external event may be an event that occursindependent of the smart contract and the parties associated therewith,while an internal event is an event that occurs with respect to thesmart contract and/or the parties associated therewith. In embodiments,a smart contract includes conditional logic that responds to a set oftriggers and executes a set of steps (e.g., a set of smart contractactions) that are performed by the smart contract in furtherance of thesmart contract. These actions may include recording documentation ofevents, transferring funds or assets, filing documents withgovernmental, regulatory, or corporate entities, initiating a workflow(e.g., maintenance workflows, refund workflows, purchasing workflows,and/or the like), and/or other suitable actions. In embodiments, a smartcontract may be configured to receive data that is indicative of events,for example, via an API, ABI, or other connection interfaces of thesmart contract. In embodiments, the smart contract may include alistening thread that listens for specific types of data. Inembodiments, the smart contract may employ an active thread, such as asearch or query of applicable logs or other data sources, to search forrelevant events or triggers. When the data is received and/or retrieved,the smart contract may process the data and operate on the data usingthe conditional logic defined in the smart contract. For example, inresponse to the conditional logic detecting the occurrence of an eventor other trigger, the smart contract may execute the smart contractaction defined therein. In embodiments, the parties may agree to amanner by which triggers are verified, such as which data sources may beleveraged to verify events.

In embodiments, a smart contract 20232 may refer to software (e.g., aset of computer-executable instructions) executed by one or morecomputing devices that performs one or more predefined actions uponverification of one or more triggering conditions/events, where theactions and triggering conditions/events embody the terms and conditionsof an agreement among counterparties that is reflected in the structureof the smart contract. For example, a smart contract may be configuredto monitor the price of a barrel of oil and to transfer the contractrights to a set quantity of oil from a seller to a buyer when the priceof a barrel of oil falls below a threshold, such that the transfer ofcontract rights from the seller to the buyer is the predefined action ofthe smart contract and the price of a barrel of oil falling below thethreshold is the predefined condition. In embodiments, smart contractsmay be stored on a distributed ledger 20222 (e.g., a blockchain) and maybe executed by the nodes that store the distributed ledger 20222.Additionally or alternatively, the platform 20500 may execute smartcontracts generated by or associated with the platform 20500. In someembodiments, the platform 20500 and/or one or more of ledger nodes thathost the smart contract may provide an execution environment on whichthe smart contract 20232 is executed. In embodiments, the smart contractmay be defined in accordance with one or more computing protocols (e.g.,the Ethereum protocol). In some embodiments, the smart contract 20232may be contained and/or executed in a virtual machine or a container(e.g., a Docker container).

In embodiments, a smart contract may operate on a set of data storageand computational resources, which may be optionally shared with otherservices, components, systems, modules, sub-systems and/or applicationsof the platform 20500, such as where the smart contract system includesor is composed of a set of microservices that are part of a set ofmicroservices in the architecture for the platform 20500. Storage,computation, and workflow execution may be performed, for example, on aset of blockchains, such as on a set of blockchain-based distributedledgers; on a set of application programming interfaces, such as APIsfor input connections to a smart contract and output connections fromthe smart contract to various other systems, services, components,modules, sub-systems, applications, or the like; on a set of dedicatedhardware devices (including hardware wallets, hardware storage devicesof various formats (hard disks, tape, cloud-based hardware, data centerhardware, servers, and many others); in a set of wallets; in a set ofaccounting systems; in a container; in set of virtual machines; embeddedwithin an API to a marketplace; on a public cloud; on a public/privatecloud (such as where elements are subject to varyingpermissions/authorization); on an intelligent switching device (such asan edge computational device or a network device that isprovisioned/assigned to an exchange or marketplace); on and/orintegrated with a physical asset to which the smart contract relates,such as in the premises of the asset in a local area network and/orphysically located on the asset (such as on an asset tag or integratedinto a native storage system of an information technology system of theasset, such as an on-board diagnostic system of a machine or randomaccess memory of a consumer device); integrated into a digital twin ofan asset to which the smart contract relates (such as any of the typesof twin described herein); in a software system (such as an ERP system,a CRM system, an accounting system or the like); embedded in acollaboration system (such as a shared document environment (e.g., aDropbox™, Google™ doc, sheet or slide presentation, or the like));embedded in a communication system storage element (such as avideo-conferencing system storage element); embedded in storage for amarketplace execution engine (such as a payments engine, a fulfillmentengine, or the like); and/or combinations of the foregoing, and variousothers.

In embodiments, a smart contract 20232 may include executable logic,data, and/or information related to facilitating a marketplacetransaction, including one or more triggers and one or more smartcontract actions to be executed in response to indication orverification of one or more of the triggering conditions or events. Inembodiments, the triggers (e.g., triggering events or conditions) maydefine conditions that may be satisfied by performance of activities byone or more parties (such as the sellers, buyers, agents, third parties,etc.) and/or occurrences of events outside the performance of parties(e.g., a value of an asset or set of assets exceeds or falls below athreshold, the occurrence of a natural disaster within a geographicregion, the allowance of a particular intellectual property right by aparticular jurisdiction, the degradation of the condition of an asset,the depreciation of the value of an asset, a regulatory change, or thelike). Examples of the triggering events or conditions include paymentof a defined amount of currency by one party (e.g., the buyer),verification that a party to a marketplace transaction is within adefined geographic area (e.g., a country, city, state, or the like),verification that an asset has been certified by a third-party,verification of an occurrence of a predefined market condition, or thelike. Examples of smart contract actions may include initiating atransfer of an asset from a seller to a buyer, recording a transfer ofownership of an asset from the seller to the buyer on a distributedledger, adjusting one or more terms (e.g., price, interest rates,allocation of responsibility or other suitable terms) in response todetermining that a party to the transaction is located within or outsideof a predefined area, or the like.

In some embodiments, the smart contracts may be generated by expertusers (e.g., smart contract developers) that are associated withcustomers or the platform 20500. Additionally or alternatively, theplatform 20500 may provide a graphical user interface that allows a userto parameterize a smart contract based on a smart contract template. Insome of these embodiments, the platform 20500 may include a set ofpredefined smart contract templates that are used for different types oftransactions and/or different types of assets. Each smart contracttemplate may include predefined code that may include parametrizableinstructions, such that a user may provide one or more values toparametrize the parameterizable instructions. In these embodiments, asmart contract developer may define the smart contract templates,whereby the smart contract templates include parameterizable fields.

Additionally or alternatively, the platform 20500 may provide a roboticprocess automation or other artificial intelligence systems that maygenerate a smart contract and/or a smart contract template, and/or mayparameterize a smart contract that is characterized by a template, basedon a model, a rule set, and/or a training set of data created by one ormore expert users, or combinations thereof. For example, a model may beprovided to an artificial intelligence system for generating a smartcontract that embodies an option transaction, where the artificialintelligence system is trained to generate the smart option contractbased on a training set of data whereby expert users generate optioncontracts for options to purchase an asset class, including trainingdata that indicates selection by the expert users of the duration of theoptions, the pricing of the option itself, and the pricing of the assetupon triggering of the option.

In some embodiments, the smart contract template may be associated witha type of marketplace, such that the template may be used to generatesmart contracts suitable for the types of assets and the types oftransactions that occur within the particular marketplace. In somecases, this may include a smart contract template for each transactiontype for the marketplace, for each asset type and/or for eachcombination of asset and transaction type. For example, a template mayrelate to a smart contract for a purchase and sale contract for definedquantities of a commodity in a commodities exchange, or it may relate toa firm price offer for a defined product, deliverable or service in anoutsourcing marketplace or a reverse auction marketplace. In someembodiments, the set of smart contract templates that may beparameterized for a particular marketplace may be limited by the type ofthe marketplace. For instance, in supporting generation of smartcontracts for trading financial instruments, the set of parameterizablesmart contract templates may be limited to smart contracts that governthe selling, buying, trading, and/or optioning financial instruments.Similarly, in supporting generation of smart contracts governing thetrading of real property rights, the set of parameterizable smartcontracts may be limited to smart contract templates that govern theselling, leasing, buying, trading, or otherwise transacting with respectto real estate. In this example, smart contract templates governing realestate transactions may be parameterized with an address of the realestate, a price associated with the transaction, requirements (e.g.,cash only, proof of financing, citizenship/legal status in thejurisdiction of the real estate, or the like), parties associated withthe transaction (e.g., property owner, seller agent, and/or the like),legal terms and conditions (e.g., liens, encumbrances, rights of way,property boundaries, and the like), or other suitable parameters.

In example implementations of a warranty smart contract configured tomanage a warranty for a product, the warranty smart contract may beconfigured to be invoked in response to the purchase of a product. Inthis example, a customer registering the product on the seller and/orproducer's website, the product (e.g., a smart product) being turned onand connecting to a network, the sale of the product itself (e.g., via amarketplace), and/or other suitable events may trigger the invocation ofthe smart contract. In response, the smart contract may execute at oneor more nodes of the distributed ledger and may listen for or activelyretrieve specific data. For example, if the product is a smart product,the product may report usage data (e.g., such as each time the productis used, each time the product is turned on, and the like), error data(e.g., each time the product encounters an error condition), misuse data(e.g., when an accelerometer or other motion data collected by thedevice indicates the product was misused), or other suitable data. Upondetection of a trigger, the smart contract may automatically calculatean applicable warranty period, such as ninety days from productactivation, thirty days from purchase, or the like. In embodiments, asmart contract may be configured to initiate issuance of a refund orreplacement product in response to determining that the product is in anerror state that cannot be resolved. In examples, the warranty smartcontract may be configured to void the warranty if the smart contractreceives misuse data that indicates that the product is damaged as aresult of misuse of the product.

As described elsewhere herein, smart transactions may include automatedsmart contract negotiation/review, such as for establishing, among otherthings, contract enforceability. Automated smart contractnegotiation/review may include configuring logic and/or artificial-basedintelligence for ensuring that contract terms are at least enforceableand that terms in the contract can be enforced. As a smart contract isbeing constructed/negotiated and/or as part of contract review,computing logic, interfaces (computer-based and real world), andvalidation functions could be instantiated and performed based on termsof the contract, preferences of the participants, agreements of theparticipants, market factors, risk, existing contracts, and the like. Asmart contract could consider events that trigger a condition of acontract and ensure that they can be detected and validated, optionallyon an ongoing basis during the life of the contract. A smart contractgeneration process could consider the conditions on which a contact isbased (e.g., terms) and ensure that they are detectable. A smartcontract could consider contract actions required and ensure/validatethat they can be successfully taken. A smart contract could beconfigured to be aware of the “type” of contract, such as a domain inwhich the contract is operative and adapt itself (e.g., ensuring termsare compliant within a regulated industry). A smart contract couldconsider risk when instantiating/validating interfaces. This may bebeneficial to contract stability and may ensure that a high-riskcontract participant might require more onerous initial (and possiblyongoing) validation (and consequently stricter terms) as compared to alow-risk participant. Further, a smart contract could use risk todetermine/adjust aspects of a contract (or actions based on terms in thecontract), such as frequency of checking an account balance or the like.

In embodiments, a smart contract might be interactive with a negotiatingparticipant. It may present impact scenarios for a proposed contractterm to a participant and offer alternatives, such as suggestingconditional escrow in lieu of direct payments, etc.

A basic smart contract negotiation/review/enforceability example ofensuring enforceability for a royalty term (e.g., pay X to A when Y issold by B) might involve several actions that may be performed in one ormore sequences, such as the following exemplary sequence: (i)identifying a payment account for A into which the royalty is to be paidand ensuring that a deposit into that account can be verified; (ii)verifying an interface to a sales/AR system of that tracks when Y issold; (iii) verifying that a sale of Y by B can be detected; (iv)detecting an interface to a financial account of B that is credited whenY is sold, etc. Ensuring enforcement might include further establishingconditional rights (and the real-world mechanisms) to perform afinancial transaction from the sales account of B to the royalty paymentaccount of A. A smart contract could instead enforce sales proceeds of asale of Y to pass through an interim account where the royalty could bewithdrawn (under proper contract terms) so that the royalty recipient Ais not dependent on the Y seller B to voluntarily make the royaltypayment.

In examples of a smart contract implementation, a smart contract may beconfigured to facilitate distribution of a settlor's estate upon thesettlor's death. Such an estate smart contract may take into accountparticipants of an estate including inheritors of the estate, such asdescendants of the settlor, entities defined in the estate or relatedcontracts (e.g., a settlor's will and the like), administrators of theestate, such as a Trustee, Independent Trustees, personalrepresentatives, and the like. Participants may be individuallyidentified and/or defined, through use of terms such as “descendant.”Estate administrators may be defined individually (e.g., a person and/oran entity such as a law firm and the like). Additionally, estateadministrators may further be defined through estate rules forestablishing and maintaining such administrators.

Relationships for the purpose of administration and/or distribution ofan estate between and among the participants may be called out in or inassociation with an estate smart contract. An example of how an estatesmart contract may be configured to address relationships amongparticipants may include automatic generation, delivery, andverification of attestation agreements for each participant. An estatesmart contract may rely on the terms of an estate that require, forexample, that an Independent Trustee be unaffiliated with otherparticipants and under no obligation of and receive no benefit from theestate to generate an electronic attestation document and workcooperatively with a digital signature system (e.g., such as a system bywhich real estate transactions and other contracts are executed) forverification thereof.

An estate smart contract may be configured to determine asset controlterms by which assets of the estate are to be administered and/ordistributed. The asset control terms of or for an estate smart contractmay cover different phases of an estate (e.g., a first estate phasewhile the settlor is alive, a second estate phase after the settlor'sdeath, a third phase based on an age of an inheritor, and the like) andtherefore may provide for different control of estate assets based onthe current phase of the estate. As an example of estate smart contractasset control, during a lifetime of a settlor, a financial account maybe placed under the settlor's control. Upon verification of thesettlor's death, which may be automated in a range of ways, control ofthe financial account may automatically be changed to the designatedTrustee of the settlor's estate. This may involve verification of thesettlor's death certificate (automated or otherwise) and presentationthereof to an account control designation function of the financialaccount along with the necessary authorization by the Trustee to bedesignated as the owner of the financial account. An estate smartcontract may be configured with functions and/or interfaces throughwhich necessary information, such as electronic delivery of a verifieddeath certificate, and/or legally identifying information for thetrustee may be accessed and used for financial control change purposes.

Assets of the estate that may be administered and/or distributed throughuse of an estate smart contract may include physical assets (e.g.,objects, real estate, and the like), financial assets (e.g., bankaccounts, investment accounts, retirement accounts, individual financialinstruments, cash, and the like), and financial obligations (e.g.,debts, business obligations of the settlor, estate taxes, estateadministrative fees, legal fees, and the like). An estate smart contractmay facilitate distribution of a family heirloom (e.g., an autographedbaseball) to an inheritor (that may be defined in a linked smart willcontract) by automatically notifying the inheritor of the object,processing instructions from the inheritor regarding the disposition ofthe object, and coordinating the inheritor's instructions with aphysical asset disposition service.

An estate smart contract may be configured to be linked with othercontracts (smart or otherwise) that may have dependent terms, such assettlor's will, an inheritor's will, and the like. Operation of anestate smart contract, such as for administration and/or distribution ofassets of an estate upon a settlor's death, may therefore be configuredto automatically identify and enable dependence upon terms of such alinked contract. In examples of smart contract linking for facilitatingdistribution of estate assets a settlor's will, an estate smart contractmay define one or more assets to be placed into the estate upon thesettlor's death. An estate smart contract may facilitate renaming theasset, such as a vacation home, into the name of the estate byproviding, electronically and/or as physical documents, theauthorization needed by a government agency, such as county recordsdepartment to make the change in ownership name. Such a smart contractaction may instead occur based on other terms defined in the estate,such as in response to an estimated value of the vacation home exceedinga resale threshold.

An estate smart contract may be configured to facilitate estateadministration and/or asset distribution based on terms of an estate. Anexemplary term may involve age limits for estate asset distribution,such as a minimum age after which a portion of an estate designated inan estate smart contract for an inheritor may be distributed free oftrust to the inheritor. Upon detection or notification of the inheritorreaching the minimum distribution age (e.g., based on verifying a birthcertificate of the inheritor and setting a date for distribution basedthereon), the smart estate contract may automatically notify an estatetrustee and the inheritor of the assets and may further provide theinheritor access (e.g., email a username and password of a brokerageaccount) to those assets designated for age-based distribution. Anotherexemplary estate term may relate to generations of descendants so that,for example, distribution of estate assets to a descendant of aninheritor may be free of trust. Another exemplary estate term that maybe configured into an estate smart contract may relate to a requirementfor the presence of one or more trustees at one or more phases of theestate. In a basic example of a smart contract facilitating estatedistribution with trustee management, an estate smart contract mayprovide a portal through which a trustee may be designated and/orthrough which a designated trustee may decline designation. Such aportal may be linked to a trustee control facility of the estate smartcontract that may automatically designate an alternate trustee (if analternate trustee has been identified or is identifiable) and/or notifya third party, such as a personal representative of the settlor, adescendant of the settlor, and the like of a need to designate atrustee.

An estate smart contract may be configured with tax optimizing logicthat may, based on value of assets of an estate, reconfigure an estateto gain tax benefits for one or more inheritors, such as by splitting anestate into two or more related estates with suitable taxabledesignations.

In examples of a smart contract implementation, a smart contract may beconfigured to close a contract or a portion thereof. Contract terms mayinclude severability that facilitates closing portions of a contract,such as one or more terms of a contract, without causing an entirecontract to be closed. Contract terms may include conditions under whicha contract may be closed. Closing of contracts, or portions thereof mayinclude one or more parties to the contract exercising a right,optionally a conditional right, to close. Closing contracts, or portionsthereof may automatically include closure, such as when a term of acontract is satisfied (e.g., when a delivery is confirmed, when adeliverable is not made timely, and the like).

Smart contracts may be configured to facilitate closing a contract orportion thereof by evaluating, from time to time, compliance with and/orsatisfaction of terms and conditions of the terms of a contract. A smartcontract that is, for example, executable on one or more processors maybe configured with a contract term evaluation facility, such as a set oflogic executable on the one or more processors that receives as inputsdata representative of conditions of the contract that facilitatedetermining adherence to a contract term, such as a contract term startdate, end date, start condition, end condition, and/or a derived valuebased on measurable elements of the contract (e.g., a minimum level ofinventory at a local distribution depot), and the like. Such a contractterm evaluation facility may be controlled by other terms in a contract,and therefore may process data representative of another contract term,such as a time period over which the inventory level must be replenishedup to the minimum level. In this example, the contract term evaluationfacility may generate a term evaluation result that may impact a stateof the contract from “active” to “pending closing.” The smart contractmay further include contract state processing logic that may, based onconditions activated when a portion of the contract is pending closing,perform actions to configure transactions and the like that canautomatically be executed to close the contract (or the relevant portionthereof) if the conditions required to change the contract state back to“active” are not met, such as if inventory records remain below aminimum value beyond the replenishment period. An example oftransactions that may be loaded for automatic execution by the smartcontract may include transfer of funds from an escrow account to aprivate account of a participant defined in the contract for receivingthe escrow balance upon closing of at least the relevant portion of thecontract. Another example transaction may include issuance of an amendedand restated contract with the closed portion removed. In embodiments,closing a portion of a contract may impact terms in other portions, suchas for commercial contracts, payment schedules. Therefore, a smartcontract may automatically adjust these other terms in the amended andrestated contract.

A smart contract may close an entire contract by taking actions definedin contract closing terms, such as automatically returning a deposit toa buyer, notifying at least the participants (and any other partiesidentified in the closing terms) of the contract closure, renegotiatingterms of the contract, signaling to a request for proposal facility toreactivate requesting proposals (or activating a backup contract with athird party) for work defined in the contract that was not delivered,and the like.

In examples of a smart contract implementation, a smart contract may beconfigured to trigger a remediation event. Parties may enter into anagreement that may be memorialized by a contract, optionally a smartcontract that may define events that contribute to compliance withcontract terms as well as events that may be triggered based on terms inthe agreement, such as contract terms. One such event that may betriggered based on terms in an agreement is a remediation event. Inembodiments, a remediation event may direct mediation actions to makeone of the parties of the agreement whole when another party of theagreement fails to comply with terms of the agreement. In embodiments, aremediation event may cause remediation actions when conditions that areoutside of control of the parties to the agreement occur, such as anatural disaster, pandemic, and the like. A smart contract may beconfigured to understand conditions that may require triggering aremediation event. In embodiments, a smart contract operable on one ormore processors may be configured with machine learning logic that may,over time, identify patterns of one or more parties to the agreementregarding actions/conditions that the smart contract is monitoring toensure compliance. The smart contract may determine that a partyconsistently provides a deliverable identified in the contract at theend of an extended delivery grace period while requiring early payment.In this example, the smart contract may trigger a remediation event thatmay initiate renegotiation of the terms of the agreement. Anotherremediation action that the smart contract may trigger is toreprioritize compliance with terms of the agreement so that lack ofcompliance with other terms that had smaller impact on the agreement maybe increased in importance, such as to be flagged to the affected partyand/or to trigger other actions that would, under nominal contractexecution conditions, not be triggered.

In examples of a smart contract implementation, a smart contract may beconfigured to deliver crypto keys to a digital product (private keyevent). Conducting secure digital transactions over a network mayrequire use of crypto keys, such as public and private crypto keys toensure, among other things, that participants of such a transaction canbe digitally verified. Smart contracts may be utilized to facilitateconducting secure digital transactions over a network by, among otherthings delivering crypto keys. A smart contract may further be utilizedto facilitate use of digital products, such as by delivering crypto keysto the digital product. In a smart contract example, two parties maydesire to enter into an agreement for use of a digital product toconduct transactions on their behalf, such as a digital product thatconducts secure transactions among parties for payments and the like.This agreement may be constructed as a smart contract that may beprovided with public crypto keys for the participants so thattransactions defined by the agreement can be conducted electronically,such as through the use of a Blockchain and the like. Such a smartcontract may be configured with not only the public crypto keys of theparticipants, but other keys that are required to conduct thetransaction, such as crypto keys for digital products (e.g., a mobiletransaction platform) to enable the digital product(s) to play a role inthe execution of the agreement. A transaction conducted under such anagreement may involve the smart contract signaling to the digitalproduct that participant A in the agreement wishes to perform atransaction with participant B of the agreement, such as sending digitalcurrency to participant B. The smart contract may, optionally, validatethe transaction is in compliance with the terms of the agreement (e.g.,ensure that the payment to participant B meets a condition of thecontract) and then forward a public encryption key for participant B(optionally along with transaction instructions) to the digital product.The smart contract may be configured with conditions, terms, and logicthat is processed to ensure that the use of the digital product is alsoin compliance with the agreement (e.g., that the transaction amount doesnot exceed a maximum threshold for use of the digital product and thelike).

In examples of a smart contract implementation, a smart contract may beconfigured to configure and execute auctions. In embodiments, rules ofan auction, such as an established minimum bid, bid increments,financial or other qualifications of bidders, obligations of bidderswhen making a bid for an item, obligations of auction participantsoffering items at the auction, forms of payment, and the like may beconfigured as features of a smart auction contract. Bidders may beparticipants to the auction smart contract with a set of terms of thecontract established and enforced for their participation, such asauction attendance, establishment and use of proxies, and the like. Anauction smart contract may comprise a functional, computer executablecontract that automates establishing and enforcing binding agreementsamong buyers, sellers, an auction service, third-parties, such as itemtransportation and warehousing providers and the like. An auctioncoordination service may configure an auction smart contract withpertinent information that facilitates operation of an auction frominitial auction planning through to delivery of auctioned items, such astime, place, bidding process, requests for items for the auction,allocation and use of auction proceeds, terms for third-parties toparticipate in the auction, and the like. In embodiments, anitem-specific smart contract may be configured for each item for auctionwith its own terms, such as minimum bid, acceptable form of payment, andthe like. Each item-specific smart contract may be linked (e.g.,logically, operationally, and the like) with one or more smart auctioncontracts, such as a master smart auction contract. An example oflogical item-specific smart contract linking with a master smart auctioncontract may include sharing certain information, such as auctionlocation, auction payment processor, item order of auction (e.g., whichitem is auctioned before and which item is auctioned after the item forwhich an item-specific smart auction contract is configured), and thelike. In examples of a smart item-specific auction contract, terms suchas minimum bid amount (bidding does not start until a participant makesan offer of at least the minimum bid amount), payment facilitator(vendor, such as a credit card transaction service, digital currencyservice, and the like), service fee recovery supplemental amount (e.g.,in addition to the bid amount, an auction service fee, logistics vendorfee, charitable donation fee, and the like), distribution of proceedsbased on a fixed amount per item and/or a percentage of auction pricefrom a winning bid (e.g., 2% auction service fee, 5% or $25 logisticsfee whichever is less, 3% charitable donation rider and the like) may beconfigured as logical terms that are enforced by execution of a smartauction contract.

In examples of a smart contract implementation, a smart contract may beconfigured to facilitate distribution of currency tokens and/ortokenized digital knowledge. Allocation and distribution of currencytokens, digital knowledge tokens, digital assets, and the like may bebased on one or more terms of an agreement or a set of agreements thatestablish the who, what, why, and when of digital token distribution. Atypical contract for controlling digital token distribution, such ascurrency tokens, digital knowledge token and the like, may be embodiedas a smart contract configured to operate within the agreement terms.Elementary examples of capabilities of a smart contract configured fordigital token distribution include automating reallocation of digitalassets among participants of an agreement embodied as a digital contractbased on terms of a contact, such as based on financial marketmovements, and the like. Terms of the agreement for conductingreallocation may be further dependent on use of a marketplace, such as adistributed financial transaction platform (e.g., a Blockchain-basedtransaction platform and the like.) A smart contract may be configuredwith interfaces and operational logic that identify participants of theagreement on or in association with the distributed financialtransaction platform and, based on the relevant terms thereof, conductor cause to be conducted secure transactions on the platform for thereallocation. Such a smart contract may be configured with currencydistribution instructions and the like, such as digital asset accountsfor a payor participant of the agreement (e.g., a buyer) and payeeparticipant of the agreement (e.g., seller), terms and timing of suchdistributions, and the like. The smart contract, or portions thereof mayoperate, such as on a processor of one or more servers, IoT devices, andthe like to cause the currency distribution to be effected. As anexample, an IoT enabled digital currency kiosk may be configured with aportion of a smart contract that controls, at least in part, operationof a fleet of IoT enabled kiosks. The kiosk (or, for example, aprocessing portion thereof, such as a set of computing logic of the IoTenabled kiosk) may be defined as a participant in the smart contractthat can be authorized to receive inputs from other participants (e.g.,payors) for conducting transactions of the smart contract. Inputreceived through the kiosk smart contract participant may be shared withother portions of the smart contract, which may optionally be operatedby other network-accessible computing systems, and processed accordingto the currency distribution terms of the underlying agreement embodiedas a smart contract.

In examples of a smart contract implementation, a smart contract may beconfigured to configure and manage the exchange of digital knowledgeacross marketplaces, exchanges, transaction platforms, and the like. Ina value-chain network example, a first marketplace may facilitate rawmaterials transactions. A second marketplace may facilitate finishedgoods materials wholesale transactions. A third marketplace mayfacilitate retail transactions of the finished goods. A smartmarketplace exchange contract may be configured with computer executablefunctionality to process terms of a knowledge-sharing/exchange agreementamong some of these marketplaces. As an example, a smart contract may beconfigured to facilitate exchange of knowledge regarding waste of rawmaterials resulting from production of finished goods made available infinished goods marketplace(s). Such an exemplary smart contract mayfurther be configured to facilitate sharing other production byproductinformation (e.g., carbon emissions and the like) among marketplaces sothat pricing and/or terms of purchase of finished goods may be adaptedbased thereon. A smart contract may be configured to enforce terms ofmaterial transfer from one marketplace (e.g., distribution) to another(e.g., retail), such as proper reuse/recycling of packaging material bythe retail marketplace. This may be enabled by, for example, packaginglocation-tracking devices that provide information to the smart contractto ensure packaging material is routed per the terms of the agreementand failure by a party to adhere to such terms will trigger actions ofthe smart contract, such as retention of a deposit paid for thepackaging, increase in automated invoice settlement, and the like.

In examples of a smart contract implementation, a smart contract may beconfigured to manage Electronic Medical Records (EMR) for variousactions/requirements thereof, such as consents, scope of consents,document access, and the like. Access to and use of electronic medicalrecords may be subject to regulatory requirements that are designed toensure a high degree of privacy, security, and integrity. Access byproviders, insurers, billing departments, patients, and the like mustcomply with a range of authorization that generally stems from patientconsent. A smart contract may be configured to operate as a primarycontrol for electronic medical record access based on, for example,patient consent. EMR access systems, such as electronic record systemsused by emergency room medical staff and the like, may be configuredwith one or more consent portals that direct requests for EMR access toan EMR smart contract where at an access request can be processed toensure that it meets the consent requirements thereof. In embodiments,an EMR smart contract may be configured to detect such access requests(e.g., by a medical imaging system to import a set of medical images(e.g., Mills and the like) to a patient's EMR). Information in orassociated with the request, such as a degree of urgency of the request,a provider making the request, a location of a facility where therecords will be viewed (e.g., a domestic office within a patient's homestate, a location outside of a home state of the patient, a foreignjurisdiction and the like) and others may be input to control functionsof the smart contract that may process the request, determine therequired degree and scope of access consent (e.g., an explicit consentgiven more than the consent validity duration may be deemed an invalidconsent except when a life threatening condition of the patientaccompanies the request), and based thereon authorize access by arequesting EMR access system. The EMR smart contract may provideautomated authorization for access only to records explicitly authorizedin a consent to a medical records access management facility participantof the underlying EMR smart contract. The requested records that complywith the consent may, as a result of the smart contract operation, becaused to be made available to an initiator of the request.

In examples of a smart contract implementation, a smart contract may beconfigured to manage clinical trials. Aspects of clinical trials that asmart contract may be configured to manage may include, withoutlimitation, tracking IRB approvals, patient enrollment and incentivepayments, collaboration of physicians and facilities, pharma-relatedaspects, clinical trial data access, authentication, and the like. Inembodiments, a master smart contract may be configured to actively linkwith other smart contracts that control portions of a clinical trial. Asan example, physician collaboration may be controlled by a smartcontract to which physicians, facilities, and the like may beparticipants. This smart contract may interact with a clinical trialmaster smart contract so that, under the terms of the physiciancollaboration smart contract, the participant physician may becomeparticipants of the master clinical trial smart contract with all of itsterms and conditions taken into consideration. Within this example, aphysician may opt out of participation in the clinical trial smartcontract, but may remain bound by the physician collaboration smartcontract for collaboration that is separate from the clinical trial. Amaster clinical trial smart contract may further link with anintellectual property development engagement smart contract that maycontrol terms under which intellectual property developed for theclinical trial may be owned, controlled, and monetized.

In examples of a smart contract implementation, a smart contract may beconfigured to manage medical grants. Aspects of medical grants that maybe managed by a smart contract include grant funding, grant resources,and grant parties (patients, providers, research institutes, grantproviders, government agencies, grant findings consumers, medical fieldaffiliates, Nobel prize record keeping, and the like). A medical grantmanagement smart contract may facilitate control of government grants,industry funded grants, higher-education funded grants, privately fundedgrants, and the like. A medical grant may be offered with a set of termsand conditions that a grantee must agree to observe for the funding tobe provided. These terms and conditions may include a phased set ofgrant disbursements. A medical grant management smart contract may beconfigured with interfaces through which participants of such anagreement may provide relevant information for compliance with the termsof a grant. As an example, a medical grant term may require thatcandidate participants in a portion of the grant complete aqualification questionnaire. An interface to a medical grant managementsmart contract may be configured to receive each completed questionnaireand/or a summary of completed questionnaires. A grant term compliancefunction of the smart grant may monitor such an interface to receive andprocess (e.g., count/validate/document/serialize) questionnaire-relatedinformation input to the interface. Such a function may operatecooperatively with a funding disbursement function of the smart contractthat may, based on a result of processing the received questionnaireinformation, determine if and how funds that are conditionally based onsatisfaction of a questionnaire term are to be released. Such a fundingdisbursement function of a smart contract may further interact with afunds disbursement auditing function that, based on an auditor'sauthorization (optionally an automated authorization), may cause theconditional funds to be disbursed to a grantee account.

In examples of a smart contract implementation, a smart contract may beconfigured to manage consultants. In embodiments, a consultantmanagement smart contract may facilitate management of consultantadministrative aspects, such as consultant payment arrangements andexecution, consultant conflict of interest vetting, consultant statementof work agreements, and the like. A consultant administrative managementsmart contract could receive information from a statement of workagreement (itself optionally a smart contract) that could be used toestablish a conflict of interest criteria (that may be embodied as afunctional term in a smart contract). Consultants may provide conflictof information vetting information to such a consultant administrativemanagement smart contract (e.g., a list and optional description ofcurrent work assignments, work history, current and prior affiliations,and the like). Optionally, a smart contract could employ public andthird-party information harvesting services, such as general Internetsearches, social-media and business-media information gatheringplatforms, industry information platforms, consultant referrals, similarconsultant information, and the like to gather and optionally vetinformation for at least one of determining potential conflict itemsrequiring further follow-up (e.g., by a human, artificial intelligencesystem and the like) and deciding whether a conflict of interest existsfor a given consultant. A consultant agreement may further define aconflict of interest petition process by which a consultant couldpetition to be exempt from some portion of a potential conflict ofinterest. A corresponding smart contract may automate submission,processing, and authorization (with optional human review) of conflictof interest condition waiver. In examples, a conflict of interest termmay identify employment with or consultation for for-profit forestrycompanies as a conflict. The smart contract may examine governmentforestry programs that use consultants and/or contracting firms anddetermine that the specific consultant is/was named as a consultant toone of these programs. A smart contract may further receive and/orretrieve payment information for a relevant government forestry programand automatically determine if the specific consultant is a payee forservices to the program. Based on a result of this finding, a smartconsultant administrative management contract may reject a petition ofwaiver or may grant a petition of waiver. The smart contract may handlethe waiver petition automatically, and/or with human assistance.

In examples of a smart contract implementation, a smart contract may beconfigured to track publications, such as publications for whichcontract terms are established, such as for publication distribution,selling price, and the like. A publication tracking smart contract maybe configured to track a wide range of publications, such as digitalpublications, newsletters, email campaigns, physical publications,newspapers, newsletters, regulatory publications, updates to terms ofsale/use, and the like. Terms of a publication agreement may includeadvance payments to an author to develop the content of the publication.These terms may include demonstrable milestones, such as a minimumnumber of pages, meetings with editors, and the like within timeframescalled out in the agreement. In embodiments, successful completion ofmilestones may impact other terms of a publication agreement, such asfurther advance payments, distribution channel priority, and the like. Apublication tracking smart contract may be configured with a portal intowhich an author may submit work product that is intended to demonstrateprogress toward one or more milestones. Optionally, a publicationtracking smart contract may include methods and systems that monitordeliverable activity, such as a module executing on or in associationwith an author's writing system (e.g., a personal computer, browser, andthe like) that monitors deliverable impacting activity, such as keyentries, file updates, and/or time spent working on a deliverable (e.g.,a draft manuscript and the like). Deliver-side terms of a smart contractmay include deliverables based on a number and timing of copies of apublication delivered to retail outlets (e.g., newsstands, bookstores,and the like). A smart contract may interface with various publicationproduction, delivery, distribution, end-reader sales systems to captureinformation that may impact a determination of satisfactory progresstoward and/or completion of publication delivery terms of such anagreement. Other forms of publication tracking may include an end userportal of the smart contract through which customer touch point activity(e.g., a customer scanning a QR code on a back cover of a publication)may be channeled so that third-party agreements associated with thepublication may be maintained.

In examples of a smart contract implementation, a smart contract may beconfigured to manage media licensing. A smart media license contract maymanage a range of media license aspects including without limitationcontent licensing, music sampling, talent contracting, royalty trackingand distribution, residual tracking, pay-per-play tracking,pay-as-you-go usage, such as within video games, and the like.Configuring a smart media license contract may include configuring alist of content for which the contract defines licensing terms, such ascontent owner fees, distribution fees, advertiser fees, and the likeinto one or more data structures. The information in such datastructures is accessible by a computing system (e.g., a processor,server and the like) that executes a smart contract algorithm thatapplies logic representing contract terms (e.g., what each advertise hasto pay for ad placement associated with an instance of content) to datarepresentative of content activity, such as delivery and rendering aninstance of the content by a video rendering service on a smart phoneand the like. A smart media licensing contract may, from time to time,capture information from the data structure, to update compliance withcontent licensing terms of the contract. In embodiments, a smart contentlicensing contract, or portion thereof, may be deployed into and/or witha gaming system, game program, or other gaming feature (e.g., virtualreality devices, and the like). A deployed portion of the smart contractmay address pay-as-you-go content usage within the scope of game play bythe user, such as by updating a portion of the data set to reflect usageview-time of content and related features.

In example implementations, a smart contract may be configured to ordersupplies or materials. The supplies or materials may be ordered inresponse to fulfillment of a triggering condition, such as a triggeringcondition related to an amount of supplies or materials stored, needed,requested, contracted for, and the like. The smart contract may beconfigured to order the supplies or materials from a predeterminedsource, such as a particular vendor. The smart contract may beconfigured to have the supplies or materials sent to a predeterminedlocation, such as an address of a customer, of a supplier, and the like.Attributes of the supplies or materials such as source, cost, amount,quality, etc. may be determined and encoded into the smart contract whenthe smart contract is created, the smart contract may be updated toretrieve information regarding attributes of the supplies or materialsafter smart contract creation, and/or the smart contract may includelogic that allows the attributes of the supplies or materials to bedetermined by the smart contract, by the distributed ledger, and/or by arelated system or data source. The supplies or materials may include anysuitable type of supply or materials, such as raw goods, partiallymanufactured goods, manufactured goods, natural resources, computationalresources, energy resources, and/or data management resources.

In example implementations, a smart contract may be configured torelease funds and/or assets to a party. The release of funds and/orassets to a party may be performed in response to fulfillment of atriggering condition, such as delivery of goods and/or services by oneor more parties. The funds and/or assets may be predetermined atcreation of the smart contract and/or may be determined after creationof the smart contract. The funds may include fiat currency, digitalcurrency, or any other suitable type of currency. The assets may includephysical assets such as property, resources, supplies, materials, land,tools, equipment, and/or title to the same. The assets may also oralternatively include digital assets, such as processing power, cloudstorage capability, digital signatures, programs, files, data, and thelike. The smart contract may include logic configured to determinetiming, quantity, quality, source, or any other suitable condition orattribute related to release of the funds and/or assets.

In example implementations, a smart contract may be configured to updatea government/regulatory database. The government or regulatory databasemay be updated in response to fulfillment of a triggering condition,such as fulfillment or lack of fulfillment of one or more regulatoryrequirements by one or more parties. The government or regulatorydatabase may include any suitable database, such as a municipaldatabase, a state database, a federal database, a foreign database, adatabase of a government agency, and the like. The database may beupdated with any suitable data, such as data related to one or moreparties to the smart contract, data related to one or more entries onthe distributed ledger, data related to one or more amounts of currencyand/or pieces of physical or digital property, etc.

In example implementations, a smart contract may be configured to issuea notice of breach to a party. The notice of breach may be issued inresponse to fulfillment of a triggering condition, such as a materialnoncompliance with terms of an agreement by one or more parties to theagreement. The smart contract may be configured to automatically detectbreach by a party, such as by monitoring one or more conditions relatedto breach. An example of a condition related to breach may be nonpaymentby a party by a particular date and/or time. Another example of acondition related to breach may be failure to deliver or adequatelydeliver goods and/or services according to one or more terms of anagreement between parties. The notice of breach may include atransmission to the breaching party, such as by email, facsimile,instant message, text message/SMS, post on a website and/or socialmedia, traditional mail, publication (e.g. in a newspaper), processserver, and/or any other suitable means of issuing a notice of breach.

In example implementations, a smart contract may be configured to changean exchange rate between currencies and/or tokens. The change inexchange rate between currencies and/or tokens may be performed inresponse to fulfillment of a triggering condition, and/or may beperformed at one or more predetermined times, such as according to aschedule. An example of a triggering condition that may trigger changeof an exchange rate by the smart contract may be a value of one or morecurrencies and/or tokens changing, such as the values thereof exceedinga threshold. The currency may be a fiat currency, a digital currency, orany other suitable type of currency. The token may be a digital tokenrepresenting a digital currency, a digital token representing ownershipor rights to one or more digital and/or physical goods, a digital tokenrepresenting a program, a digital token representing information storedon the distributed ledger, or any other suitable type of token.

In example implementations, a smart contract may be configured toincrease or decrease an interest rate. The increase or decrease in aninterest rate may be performed in response to fulfillment of atriggering condition, such as payment of a predetermined amount of debtby a party to an agreement and/or making of a down payment above athreshold by a party to an agreement. The increase or decrease may bemade to an interest rate of any suitable type of loan or securityagreement. The increase or decrease of the interest rate may be made byadjusting one or more interest related to an agreement transacted on thedistributed ledger or an agreement transacted separate from thedistributed ledger, such as by a bank or mortgage company.

In example implementations, a smart contract may be configured toinitiate and/or perform foreclosure on a piece of collateral. Theforeclosure may be initiated and/or performed in response to afulfillment of a triggering condition, such as default by a party to anagreement in collateral is used to secure a loan. The foreclosure may beinitiated and/or performed according to terms encoded into the smartcontract. The foreclosure may be initiated and performed by the smartcontract itself, or may be initiated by transmitting an initiationsignal external to the smart contract, such as to a financialinstitution.

In example implementations, a smart contract may be configured to placea lien on a piece of property involved in an agreement. The lien may beplaced upon creation of the smart contract and/or upon configuring ofthe contract with terms of an agreement made between a plurality ofparties. The lien may be placed in response to fulfillment of atriggering condition, such as use of a piece of digital and/or physicalproperty to secure a loan agreement. The lien and/or conditions relatedto the lien may be stored on the distributed ledger and/or may beencoded into the smart contract. The lien may be placed on a digitalitem that is stored on the distributed ledger. The smart contract mayadditionally include one or more conditions related to release of thelien upon fulfillment thereof.

In example implementations, a smart contract may be configured to recorda change in title. The title may be a title to one or more instances ofdigital property, one or more instances of physical property, one ormore instances of real property, or a combination thereof. The change intitle may be recorded in response to fulfillment of a triggeringcondition, such as transfer of property from one or party to another inan agreement, or such as payment or rendering of services by one partyof an agreement in exchange for property by another party to theagreement. The title may be stored on the distributed ledger. Therecordation of change in title may be performed by transmission of oneor more signals and/or documents to one or more recipients external tothe distributed ledger, such as to a county registrar or to a digitaltitle database.

In example implementations, a smart contract may be configured to make aUCC filing. The UCC filing may be made to any suitable recipient, suchas a government office. The UCC filing may be made in response tofulfillment of one or more triggering conditions, such as acquiring ofan interest in the property of a first party by a second party accordingto an agreement between the first and second parties. The agreement maybe stored in the smart contract. The UCC filing may be made bytransmitting one or more signals and/or documents to a suitablerecipient. The UCC filing may be stored on the distributed ledger.

In example implementations, a smart contract may be configured toextinguish a UCC filing. The UCC filing may be extinguished bytransmitting a signal, digital document, or any other suitable notice ordata to a suitable recipient, such as a government office. The UCC filemay be extinguished in response to fulfillment of one or more triggeringconditions, such as payment of a debt by a first party to a secondparty, the payment of the debt calling for release of an interest of thesecond party in a piece of property owned by the first party accordingto terms of an agreement. The agreement may be encoded in the smartcontract.

In example implementations, a smart contract may be configured toallocate payments among multiple parties. The allocation of payments maybe performed in response to fulfillment of one or more triggeringconditions, such as initiation of an agreement between the partiesamongst whom the payments are to be allocated. Another example of atriggering condition that may cause the smart contract to allocatepayments among multiple parties is delivery of goods and/or services byone or more of the parties to whom payments are to be allocated. Thepayments may be allocated according to terms encoded in the smartcontract, stored on the distributed ledger, and/or external to thedistributed ledger. The payment may be allocated according to paymentterms encoded in the smart contract at creation of the smart contract,upon agreement between the multiple parties, or at any time or uponfulfillment of any suitable condition.

In example implementations, a smart contract may be configured toallocate profits among joining owners. The allocation of profits may beperformed according to a formula, such as terms of an ownership and/orpartnership agreement that may be encoded in and/or imported to thesmart contract. The formula and/or agreement may be stored on thedigital ledger. The formula and/or agreement may be of any suitabletype, such as ownership of a business, ownership of one or moresecurities, co-investment in one or more types of tangible and/orintangible properties and/or securities, and the like.

In example implementations, a smart contract may be configured to make apayment. The payment may be made in response to fulfillment of one ormore triggering conditions, such as according to a payment schedule ofan agreement between parties. The payment may be made by transferringone or more digital currencies and/or balances stored on the digitalledger. Additionally or alternatively, the payment may be made bytransmitting a signal such as a wire transfer signal to a recipientoutside of the distributed ledger network, such as to a bank. Thepayment may be made to one or more parties to an agreement encoded inthe smart contract, and/or may be made to one or more parties for thesale, license, lease, and/or transfer of goods stored on the distributedledger.

In example implementations, a smart contract may be configured to send agift. The gift may be sent in response to fulfillment of one or moretriggering conditions, such as at a certain date or time. The gift maybe sent from one party to another and/or to or from any users of thedistributed ledger, parties to agreements stored thereon, and/or ownersor lessees of digital property and/or currency stored thereon. The giftmay include digital currency and/or property, fiat currency, physicalproperty, title to digital and/or physical property, or any othersuitable gift.

In example implementations, a smart contract may be configured totrigger a gaming event. The gaming event may be triggered in response tofulfillment of a triggering condition, such as achievement of one ormore gaming incentive goals by one or more parties to an agreementencoded in the smart contract. For example, parties may engage in anagreement encoded in the smart contract for the sale of goods, whereinupon selling a set increment of goods a party may receive one or moregame incentives, such as digital game tokens. The gaming event may berelated to any suitable game, such as a gamification of a sale orcontract. The gaming event may include awarding game incentives such asdigital currency to one or more users of the distributed ledger.

In some implementations, smart contracts may be configured, for example,to confirm receipt of a shipment at a delivery location, instantiateanother smart contract or sequence of smart contracts, manage franchiseagreements (such as tracking and applying franchise rules), managegovernment contracts, manage real estate (such as managing mortgages andlending, title, insurance, or the like), manage transportation assets(such as managing title, insurance, emissions, or the like), managefinancial contracts, manage corporate agreements (such as statements ofwork, purchase agreements, employment agreements, mergers, acquisitions,insurance, or the like), track data privacy, manage wills or trusts,perform outcome-dependent transactions, or the like.

In some implementations, smart contracts may be invoked upon theoccurrence by one or more of the following events: social media events,social impact measurements (including number of followers, number ofposts, number of likes, number of views, or the like), weather ordisaster events (including severe weather damage, crop destruction,fires, floods, pandemics, earthquakes, hurricanes, war, or the like),the purchase of a product or service, changes related to collateral(including tokenization of collateral, movement of collateral, damage tocollateral, depreciation of collateral, or the like), covenant events(such as bonds or loans linked to IoT devices), machine-to-machineevents (including digital twins contracting with each other, IoT agentscontracting with each other, machine-to-machine or digital twin paymentnetwork events, or the like), advertising events, marketing events,gaming events, quantum services events, sporting events, gamblingevents, security events, energy markets events, and product releaseevents.

In embodiments, the platform 20500 may present a GUI to a user thatrequests to generate a new smart contract. In embodiments, the platform20500 may provide a set of smart contract templates that the user mayselect based on the type of transaction that the user has requested. Forexample, if the marketplace is configured for buying and/or sellinginterests in real property, the platform 20500 may provide the user withone or more options for generating smart contracts that relate to realestate transactions. The user may be given a set of questions that, whenanswered, result in the platform 20500 selecting the smart contracttemplate that is optimized for the user's intentions (e.g., alending-based smart contract template, a smart contract templategoverning the sale of an interest in a real estate property, a commoditytrading smart contract template that governs a forward contract, or thelike). Alternatively, the user may be provided with a menu of availablesmart contract templates, and the user may select one of the smartcontract templates from the menu.

Upon determining a smart contract template, the platform 20500 mayprovide an interface (e.g., a GUI) that allows a user to set theparameter values corresponding to the determined smart contracttemplate. For example, in a smart contract governing a futures contractwith respect to a commodity, the user may set the type of commodity, anumber of units (e.g., barrels of oil, bushels of wheat, ounces of gold,or the like), a contract price to be paid for the commodity, theexecution date of the futures contract, the contract price, and othersuitable parameter values. In examples, in a smart contract governingthe sale of a physical asset, the seller may set a first price if abuyer is located within the United States and a second price if thebuyer is located outside the United States. In this example, the usersets parameter values that are used to parameterize triggers, namely ageographical restriction. In this example, the platform 20500 maygenerate a smart contract that has location-sensitive pricing. Thus,when a buyer seeks to accept the terms of the smart contract, the smartcontract may verify a location of the potential buyer and may configurethe terms of the contract (e.g., the price and/or other suitable terms,such as logistics information, location-specific tax information, or thelike) based on the location of the potential buyer. It is noted that inother examples, users may parameterize smart contracts with parametervalues corresponding to triggering actions, such as initiating acertification process associated with the transaction, initiating areporting process associated with the transaction, configuring logisticsinformation associated with the transaction, reconfiguring of terms(e.g., premium rates, interest rates, contract price, delivery date,payment due date, and/or the like). It is appreciated that depending onthe type of smart contract, the types of data that may be used toparameterize a smart contract will differ. As with the other embodimentsthe involve parameterization, parameterization may be undertaken byrobotic process automation or other artificial intelligence systems,such as trained on a training set involving parameterization by a set ofexperts.

In some implementations, a smart contract may include one or more eventlisteners. In embodiments, an event listener may be a listening threadthat monitors one or more data sources to determine when a certain eventoccurs, such as whether a triggering condition is met. In embodiments,an event listener may subscribe to a data feed, query an API, receivenotifications, query a database or other data source, passively receivedata from a set of Internet of Things (IoT) devices (consumer IoTdevices and/or industrial IoT devices and/or sensors), or otherwisereceive/retrieve data from a data source to obtain a specific type ortypes of data. For example, a smart contract governing an insurancepolicy that covers an industrial facility may include an event listenerthat queries a municipality database, such as via an API, to verify thatthe owner of the industrial facility has paid its taxes and to identifythe presence of changes in title, liens, or encumbrances on theproperty. Continuing the example, a smart contract governing theinsurance contract may include an event listener that connects to anindustrial internet of things (IIoT) sensor system (or “sensor system”)of the industrial facility to receive one or more sensor streams. Forexample, the smart contract may be parameterized with a set of IPaddresses and authentication credentials to access a sensor system(e.g., via a set of edge devices of the sensor system) to access a setof data streams from the sensor system. In some embodiments, an edgedevice of the sensor system may include an intelligence system thatfilters the stream (such as to deliver information relevant to the smartcontract parameters while omitting unnecessary information) and/orperforms one or more analytic operations on the sensor data collectedfrom a set of one or more sensors (such as to calculate a metric that isused as a parameter of the smart contract) and may communicate one ormore data streams based on the filtering and analytics to the systemhosting the smart contract. The smart contract event listener may listento such streams to verify one or more triggering conditions. In thisway, the smart contract may ingest sensor data and determine whether oneor more triggers have occurred. In response to determining that adefined set of triggers have occurred, the smart contract may executeone or more smart contract actions. For example, in the context of aninsurance contract, the detection of a warning condition by the smartcontract that is derived from sensor data received from a sensor systemassociated with an industrial facility may result in an action thatadjustments a premium rate of the insured. In this way, the smartcontracts may be configured to receive IoT data (e.g., IoT-collectedsensor data, IoT-collected health data, IoT-collected location data,and/or the like) to verify one or more triggers and, in response, toinitiate one or more smart contract actions. It is appreciated thatsmart contract event listeners according to other example embodiments ofthe disclosure may listen for data obtained from additional oralternative data sources.

In embodiments, the platform 20500 can support smart contracts of anumber of different types for a number of different types ofmarketplaces. As used herein, references to “supporting smart contracts”may refer to the platform 20500 generating and deploying a smartcontract on behalf of a user and/or facilitating the generation of smartcontracts by users of the platform 20500 in a decentralized manner(e.g., generated from a user device that writes the smart contract to adistributed ledger), as well as generating and deploying a smartcontract automatically, such as by an artificial intelligence systemand/or set of services (e.g., involving robotic process automation)within the platform 20500 or linked to the platform 20500, such as viaone or more interfaces, such as application programming interfaces.Examples of types of marketplaces/transactions that may be supported bythe platform 20500 may include, but are not limited to, asset-basedtransactions, insurance transactions, supply-chain transactions,commodity/stock-based transactions, cryptocurrency transactions,intellectual property transactions, and/or any other types oftransactions described herein or in the documents incorporated byreference herein, and may include the core transactions thatcharacterize marketplaces (e.g., purchase and sale of bonds in anequities market), as well as other transactions, such asmicrotransactions and exchanges that are involved in workflows orprocesses (e.g., a transfer of value in exchange for priority within aprioritization system, providing value to induce behavior, such asviewing an advertisement, and many others), and many others.

In some embodiments, the platform 20500 may support smart contracts thatgovern transactions involving assets. In these embodiments, a smartcontract may include information defining the asset (e.g., an assetidentifier, a serial number, a name, a make/model, or the like) orassets that are subject to the transaction, the price of the asset, thenumber of units, or the like. Furthermore, as the smart contract may begenerated by a buyer, a broker, a market maker, a seller, or the like,the smart contract may or may not define the parties to the transaction,or the types of parties that are permitted to transact (e.g., limitingto licensed broker/dealers for transactions in regulated securitieswhere required). For instance, a buyer wishing to purchase a vehicle maygenerate a new smart contract via the platform 20500 that offers a priceto purchase a particular vehicle (e.g., make, model, and year) with oneor more additional requirements (e.g., <50,000 miles, single owner,under warranty, pickup location/area, and/or the like). In this example,there is no seller identified in the smart contract, but the buyer maybe identified in the smart contract. In response, the platform 20500 maygenerate a smart contract that includes the triggering conditions forcompleting the sale of the vehicle and a smart contract action thatinitiates the transfer of title from the seller to the buyer. This mayinclude an event listener or other smart contract element that requiresthe buyer to prove that he or she has the cash and/or adequate financingto purchase the vehicle and an event listener or other smart contractelement that requires a seller to prove that they have title to avehicle meeting the buyer's requirements. In embodiments, the platform20500 may be configured to use automation systems, such as artificialintelligence, such as one or more classification systems that istrained, such as using a model and/or a training set of human-labeleddata, to discriminate between valid and invalid inputs that are offeredto satisfy applicable triggers in the smart contract. For example, suchsystems may be trained to process account data to determine adequacy ofadequate financial strength of the buyer and to process title records(e.g., title certificates) to determine the adequacy of the seller'sclaim to title. Such artificial intelligence systems used forclassification may include a recurrent neural network (including a gatedrecurrent neural network), a convolutional neural network, a combinationof a recurrent neural network and a convolutional neural network, orother types of neural network or combination or hybrid of types ofneural network described herein or in the documents incorporated byreference herein. In this example, the prospective buyer may upload adocument that proves that he or she has secured financing to cover thedefined price and the seller can upload a copy of the title of thevehicle as well as a certified statement declaring that the otherrequirements are met. Alternatively, if the vehicle is a connectedvehicle, the seller may provide access to the vehicle data, whereby thewarranty status and the mileage of the vehicle may be confirmed.

In embodiments, the platform 20500 may support smart contracts thatgovern insurance policies. In embodiments, an insurance policy smartcontract may be generated in response to a party seeking to insure anasset, a property, a business, a person, or the like. Insurance policiesmay take any form of insurance, such as health insurance, lifeinsurance, homeowner's insurance, disaster insurance (e.g., fire, flood,hurricane, pandemic, or the like), property insurance, auto insurance,third party liability insurance, business interruption insurance,disability insurance, or the like. In a specific example, a party mayagree to a set of terms provided by an insurance provider. In thisexample, the insurance provider may agree to reduce the premium rates aslong as the insured agrees to provide one or more requested data types.In some embodiments, the requested data types may be one or more datastreams from a set of IoT devices associated with the insured. Forinstance, in insuring an industrial facility, the smart contractgoverning the insurance policy may be configured to receive a datastream of sensor data from an IIoT sensor system distributed within theindustrial facility. Either the smart contract, the platform 20500, athird-party service, or an edge device of the sensor system may receivethe raw sensor data from the IIoT sensor system and may determinewhether the sensor data indicates a deteriorating condition of thefacility or a piece of industrial equipment within the facility. In theexample, the smart contract may reconfigure the terms of the insurancepolicy to a provide for a higher premium and/or deductible until thedeteriorating condition is resolved (as indicated by the sensor data).In examples, a smart contract governing a health insurance policy may beconfigured to receive health-related data from a wearable device of theinsured individual. In this example, the smart contract may beconfigured to lower the premium rate if the health-related dataindicates that the user is taking actions to improve his or her health.For instance, if the health-related data includes a number of dailysteps and the number of daily steps over a period of time (e.g., sixmonths) indicates that the user is taking at least 10,000 steps a day,the smart contract may reduce the premium of the individual by an agreedupon amount (e.g., 100 dollars a month). Conversely, if the smartcontract receives negative health-related data (e.g., high bloodpressure, low blood oxygen, less than 1000 steps a day over a six monthperiod, or the like) or if the user does not provide the health-relateddata (e.g., does not grant access to the wearable device), the smartcontract may increase the premium to an agreed upon amount.

In some embodiments, the platform 20500 is configured to bind parties tosmart contracts via a digital twin, such as where the digital twinoffers interfaces that are integrated with and/or linked to the platform20500, that are shared with the platform 20500, and/or the like. Adigital twin platform may be integrated with or into the platform 20500and/or linked to it, such that the digital twin platform and theplatform 20500 share data sources, resources, services, interfaces andthe like, including data sources that are accessed to determine triggersfor the smart contract and thereby facilitating triggering of actions inthe digital twin (and in turn various services, systems, processes andthe like that may be controlled by or from the digital twin) in responseto actions determined by the smart contract. For example, an ownershiptransfer of an asset may be affected by a smart contract andautomatically reflected in a digital twin that represents the asset,such as by a change in data, metadata, or the like in the data schemathat is used to generate the digital twin. In embodiments, the platform20500 may be configured to serve transaction offers to users in adigital twin (e.g., an “in-twin” marketplace) via an API. In response toa user agreeing to an offered transaction, the user may be committed toa smart contract. In some embodiments, the user may be required toprovide additional information and/or access to certain types of datapursuant to the smart contract.

In embodiments, the platform 20500 may support smart contracts that aredeployed in connection with forward contracts that are traded via assettrading marketplaces (e.g., commodity trading marketplace, stock tradingmarketplace, or the like). In embodiments, a trading marketplace mayrefer to a marketplace that is created to facilitate the brokering offorward contracts. In embodiments, a user may create a smart contractgoverning a forward contract. In embodiments, a user may select anoption to create a new smart contract governing a forward contract. Insome of these embodiments, the user may be presented a GUI to provideone or more parameter values. For instance, the GUI may include fieldsfor the user to identify an asset (e.g., a stock or commodity), the longparty/buyer, the short party/seller, a contract settlement date, and/ora price (e.g., price per unit or a total price). In this example, theuser setting the forward contract may be the short party (e.g., buyer),the long party (e.g., seller), or a third party (e.g., a broker). In thecase that either the short party or long party is to be determined, thefield may be left unparameterized and may be parameterized when the tobe determined party commits to the forward contract. Upon receiving theparameterization values, the platform 20500 may deploy the smartcontract (e.g., to a distributed ledger and/or platform 20500 mayexecute the smart contract). Furthermore, to the extent that one or moreparties remain unresolved, platform 20500 may publish the offer of thefuture contract with a defined price via a corresponding marketplace(e.g., a forward contract marketplace, a commodity marketplace, or anequities marketplace). In embodiments, the platform 20500 may generateand deploy a smart contract in connection with a forward contractautomatically, such as by an artificial intelligence system and/or setof services (e.g., involving robotic process automation) within theplatform 20500 or linked to the platform 20500, such as via one or moreinterfaces, such as application programming interfaces.

In some embodiments, the platform 20500 may create and host forwardmarketplaces. In embodiments, a forward marketplace may refer to anelectronic marketplace that provides a medium for counterparties tonegotiate and engage in forward contracts. A forward contract may referto a customized contract between two parties to buy/sell a negotiatedquantity of an asset at a negotiated price on a negotiated date.Examples of assets that may be sold using forward contracts includeagricultural commodities (e.g., wheat, corn, oranges, cotton, and/or thelike), natural resources (e.g., natural gas, oil, gold, silver,platinum, or the like), financial instruments (e.g., stocks, bonds,currencies, or the like), non-traditional assets and/or other suitablecommodities (e.g., fuel, electricity, energy, computational resources(e.g., quantum computational resources), cryptocurrencies, definedincome streams, data streams (such as sensor data, network data and thelike), knowledge structures, and the like. In some embodiments, a futurecontract may require additional terms, such as a delivery locationand/or storage location for the assets (if physical assets to bedelivered/stored), warranties and/or guarantees (e.g., warranties thatthe assets will meet certain requirements), or the like. In embodiments,the forward marketplace may provide an interface where parties maynegotiate the terms of a forward contract. For example, a firstparty/user may create an initial offer that includes a set of terms(e.g., asset, quantity, contract expiration date, price, and any othernegotiable terms). In response, the forward marketplace may present theoffer to the counterparty, which may accept the offer, reject the offer,and/or submit a counteroffer (e.g., by changing one or more terms). Theparties may iterate via the forward marketplace in this manner until anoffer or counteroffer is accepted or the deal is rejected. In responseto the parties agreeing to a forward contract (e.g., one party acceptsthe other party's bid), the platform 20500 may generate a forwardcontract based on the negotiated terms. In some embodiments, a forwardcontract may be formed between parties using the forward marketplace viaa bidding process. In these embodiments, a party may generate an offerto buy/sell a set quantity of an asset at a set price on a set date. Forexample, a seller may offer to sell 10000 bushels of wheat at fivedollars a bushel on Nov. 5, 2020. The forward marketplace may publishthe offer, such that potential counterparties may view the offer. It isappreciated that the forward market may provide additional informationin connection with the offer, such as a rating of the party thatgenerated the offer. If a potential counterparty accepts the offer, theplatform 20500 may generate a forward contract between the parties. In avariation of the bidding process, a listing party may define a specificquantity of a specific asset to be completed on a proposed date, andcounterparties may provide bids that indicate a price of the contract.For example, a buyer may offer to buy 10000 bushels of wheat on Nov. 5,2020. In response, potential sellers may offer different prices for therequested asset. Continuing this example, a first seller may offer to aprice of four dollars a bushel and a second seller may offer fivedollars a bushel. The listing party may then accept one of the bids(e.g., the buyer may accept the four dollar a bushel price). In responseto an offer being accepted, the platform 20500 may generate a futurecontract based on the negotiated terms. In embodiments, the platform20500 may create and host forward marketplaces automatically, such as byan artificial intelligence system and/or set of services (e.g.,involving robotic process automation) within the platform 20500 orlinked to the platform 20500, such as via one or more interfaces, suchas application programming interfaces.

In embodiments, a forward market orchestration system platform 20500 isconfigured to generate smart contracts governing forward contracts inresponse to a completed negotiation process via a forward marketplace.As discussed, a listing party may publish an offer, engage in a seriesof offers and counteroffers, and/or request offers for a forwardcontract relating to an asset during the negotiating process. Once anoffer has been accepted by both parties (or three or more, depending onthe parameters of the contract), the platform 20500 may generate a smartcontract and may parameterize the smart contract based on the parametersdefined in the accepted offer (e.g., the party that made the bid, theparty that accepted bid, the assets at issue, the quantity of assets,the contract price, the contract settlement date, and any other suitableparameters). In some embodiments, the platform 20500 may deploy thesmart contract once generated (e.g., to a distributed ledger and/orinternally). In embodiments, the smart contract may be configured withan event listener that listens for events associated with the forwardcontract and triggers actions based thereon. For example, an eventlistener may be configured to listen for a date, and when the datereaches the contract settlement date, the smart contract may initiatethe transfer of funds from the buyer to the seller and/or the transferof the assets to the buyer from the seller. Additionally oralternatively, an event listener may be configured to listen for apayment from the buyer to the seller and/or delivery of the assets fromthe seller to the buyer. If either of the conditions is not met, such aswithin a time period parameter defined within the smart contract, thesmart contract may be configured to initiate a process that handlesdefault scenarios (e.g., automatically transferring funds from thedefaulting party to the counterparty from an account of the defaultingparty).

It is appreciated that trading marketplaces may support smart contractsthat govern other financial trading instruments, such as options, swaps(e.g., credit/default swaps, in-kind exchanges, and the like), futures,derivatives, and the like without departing from the scope of thedisclosure. In embodiments, in generating smart contracts that governoptions, a smart contract may be configured to listen for events relatedto the option. For example, if an option is triggered when the price ofa particular commodity, financial instrument, or index reaches atriggering value, the option may be automatically executed. Similarly,the option may automatically vest (i.e., become exercisable) upon atrigger condition, which may include any of the triggers noted herein.In the example of a smart contract listening for a price trigger, anevent listener of a smart contract that governs an option may beconfigured to receive data from a commodity or stock marketplace and tocompare the current price of the commodity to the triggering value, suchthat when the current price reaches the triggering value, the smartcontract may execute one or more actions that exercise the option inaccordance with the agreed upon terms of the option contract.

In embodiments, the market orchestration system platform 20500 maycluster a set of smart contracts by attribute similarity. Inembodiments, smart contract attributes may include smart contract types(e.g., smart legal contracts, decentralized autonomous organization(DAO) smart contracts, application logic contracts (ALCs), ancillarysmart contracts and many others), programming language type (Solidity,Rust, JavaScript, Vyper, Yul, and many others), transaction types (e.g.,payment of funds upon certain triggering events, imposing financialpenalties if certain objective conditions are not satisfied, and manyothers), smart contract function types, parties or party types subjectto the smart contracts, number of parties subject to the smartcontracts, relevant chain(s) related to the smart contracts, assets orasset types subject to the smart contracts, asset volumes covered by thesmart contracts, total monetary value of the smart contracts, pricingrelated to the smart contracts, events related to the smart contracts,conditions related to the smart contracts, smart contract timingattributes, smart contract statuses (e.g., pending, executed, abandoned,and the like), smart contract terms, likelihood of execution,transaction fees, off-chain resources (i.e., information or parametersfrom resources that are not on the blockchain), oracles, and manyothers.

In embodiments, the market orchestration system platform 20500 mayleverage the artificial intelligence system to cluster a set of smartcontracts by attribute similarity. In embodiments, an artificialintelligent services system 20243 receives an intelligence request andany required data to process the request from the market orchestrationsystem platform 20500. In response to the request and the specific data,one or more implicated artificial intelligence system perform theintelligence task and output a clustering of the set of smart contractsby attribute similarity.

For example, the intelligent services 8800 may receive data from varioussources described throughout this document and the documentsincorporated by reference herein and may generate a set of featurevectors based on the received data. The intelligent services system20243 may input the set of feature vectors into a machine-learned model(e.g., using a combination of simulation data and real-world data) tocluster a set of smart contracts by attribute similarity by a set ofhuman experts and/or by the other systems or models. Data sources andfeature vectors used for smart contract clustering may includemarketplace smart contract data, asset data, user data, transactiondata, as well as external data sources (such as publicly available smartcontract data) and many others. Such artificial intelligence systemsused for clustering, in the present example and other examples describedherein, may include a recurrent neural network (including a gatedrecurrent neural network), a convolutional neural network, a combinationof recurrent neural network and a convolutional neural network, or othertypes of neural network or combination or hybrid types of neural networkdescribed herein or in the documents incorporated by reference herein.

In embodiments, machine learning and/or artificial intelligence modelsmay be trained using existing public facing smart contracts to determineclustering and high-level meta tags.

In embodiments, outliers within a cluster of smart contracts may behighlighted or otherwise presented to a user. For example, buy-side bondpurchase smart contracts may be assigned to a cluster to reveal priceoutliers.

In embodiments, the market orchestration system platform 20500 mayleverage the intelligent services system 20243 (such as artificialintelligence system, RPA module and/or NLP module 8824) to automaticallyconvert discussions into a smart contract. In embodiments, discussionsmay include email discussions, instant messaging and/or chatdiscussions, text messaging discussions, video conferencing discussions,phone call discussions, and many others. For example, an agreementcaptured in a video conference to keep the video conference discussionconfidential may be captured and applied to the video conferencediscussion as a wrapper.

In embodiments, the market orchestration system platform 20500 mayprovide visual representations of relevant terms and/or conditions froma set of smart contracts and/or proposed smart contracts. Such visualrepresentations may be presented to the user via a set of digital twins,the user interface of an application, a wearable, an augmented realityheadset, a virtual reality headset, and many others.

For example, a digital twin accessed by a trader (e.g., a trader digitaltwin, a digital twin of the trader's account, a digital twin of amarketplace, a digital twin of a set of smart contracts, or the like)may present visual representations of the relevant terms and/orconditions of a set of smart contracts related to buy-side assetpurchases, such as smart contract duration, decision points, pricing,current position exposure, risk, liquidity, and the like. Inembodiments, the digital twin may also present recommendations, such asfor risk mitigation (e.g., hedging or insurance), termination,amendment, expansion, or the like. In embodiments, such recommendationsmay be represented visually via the digital twin.

In embodiments, the market orchestration system platform 20500 mayexecute simulations relating one or more terms and/or conditions from aset of smart contracts or proposed smart contracts and present suchsimulations and/or the results of such simulations to the user via auser interface.

In some embodiments, the market orchestration system platform 20500 mayleverage the intelligent services system 20243 to provide data storiesabout relevant terms and/or conditions from a set of smart contractsand/or proposed smart contracts. Continuing the example, a machinelearning and/or artificial intelligence system may generate anaudiovisual data story based on the set of smart contracts and/orproposed smart contracts and output the generated audiovisual data storyto the market orchestration system platform 20500. In embodiments, themarket orchestration system platform 20500 may be configured to enablepresentation of the data story to a user via a user interface, such asthe user interface of a digital twin or the user interface of a digitalwallet. In embodiments, the audiovisual data story may include theresults of various simulations related to the set of smart contractsand/or proposed smart contracts.

In embodiments, the intelligent services system 20243 performs machinelearning, artificial intelligence, intelligent order matching,counterparty discovery, counterparty intelligence, analytics tasks,and/or any other suitable tasks on behalf of the platform 20500. Inembodiments, the intelligent services system 20243 includes a machinelearning system that trains machine learned models that are used by thevarious systems of the platform 20500 to perform intelligence tasks,including robotic process automation, predictions and forecasts,classifications (including behavioral classifications, typedetermination and others), process control, monitoring of conditions,translation (such as language translation), natural language processing,prescriptive analytics, and the like. In embodiments, the platform 20500includes an artificial intelligence system that performs various AItasks, such as automated decision making, robotic process automation,and the like. In embodiments, the platform 20500 includes an analyticssystem that performs different analytics across marketplace data toidentify insights related to the states of a marketplace, marketplaceassets, traders, and the like. For example, in embodiments, theanalytics system may analyze the performance data, condition data,sensor data, or the like with respect to a physical asset to determinewhether the asset is in excellent condition, satisfactory condition, orin poor condition. In embodiments, the analytics system may perform theanalytics in real-time as data is ingested from the various data sourcesto update one or more states of a marketplace asset. In embodiments, theintelligent services system 20243 includes a robotic process automationsystem that learns behaviors of respective users and automates one ormore tasks on behalf of the users based on the learned behaviors. Insome of these embodiments, the robotic process automation system mayconfigure intelligent agents 20234 on behalf of a marketplace host,trader, broker, or the like. The robotic process automation system mayconfigure machine-learned models and/or AI logic that operate togenerate outputs, such as ones that govern actions or provide inputs toother systems, given a set of stimuli. In embodiments, the roboticprocess automation system receives training data sets of interactions byexperts and configures the machine-learned models and/or AI logic basedon the training data sets. In embodiments, the intelligent servicessystem 20243 includes a natural language processing system that receivestext/speech and determines a context of the text and/or generates textin response to a request to generate text. The intelligent services arediscussed in greater detail throughout the disclosure and the documentsincorporated herein by reference.

In embodiments, the intelligent services system 20243 performs machinelearning, artificial intelligence, and analytics tasks on behalf of theplatform 20500. In embodiments, the intelligent services system 20243includes a machine learning system that trains machine learned modelsthat are used by the various systems of the platform 20500 to performsome intelligence tasks, including robotic process automation,predictions, classifications, natural language processing, and the like.In embodiments, the platform 20500 includes an artificial intelligencesystem that performs various AI tasks, such as automated decisionmaking, robotic process automation, and the like. In embodiments, theplatform 20500 includes an analytics system that performs differentanalytics across data sources, such as enterprise data, to identifyinsights to various states of a marketplace. For example, inembodiments, the analytics system may analyze the financial data of anasset to determine whether the asset is financially stable, in acritical condition, or a desirable condition. In embodiments, theanalytics system may perform the analytics in real-time as data isingested from the various data sources to update one or more states of amarket orchestration digital twin. In embodiments, the intelligentservices system 20243 includes a robotic process automation system thatlearns behaviors of respective users and automates one or more tasks onbehalf of the users based on the learned behaviors. In some of theseembodiments, the robotic process automation system may configure expertagents on behalf of a marketplace and/or marketplace entities, such asusers, a set of hosts, service providers, infrastructure providers,information technology providers, information providers, and others. Therobotic process automation system may configure machine-learned modelsand/or AI logic that operate to generate outputs, such as ones thatgovern actions or provide inputs to other systems, given a set ofstimuli. In embodiments, the robotic process automation system receivestraining data sets of interactions by experts and configures themachine-learned models and/or AI logic based on the training data sets.In embodiments, the intelligent services system 20243 includes a naturallanguage processing system that receives text/speech and determines acontext of the text and/or generates text in response to a request togenerate text. The intelligent services are discussed in greater detailthroughout the disclosure.

In some implementations, the intelligent services system 20243 performsmachine learning and artificial intelligence related tasks on behalf ofthe market orchestration system platform 20500. In embodiments, theintelligent services system 20243 may train any suitable type of model,including but not limited to various types of neural networks,regression models, random forests, decision trees, Hidden Markov models,Bayesian models, and the like, including any of the expert and/orartificial intelligence examples described herein and, in the documents,incorporated by reference. In embodiments, the intelligent servicessystem 20243 trains machine learned models using the output ofsimulations executed by the digital twin simulation system 20804 (FIG.208 ) or other simulation system included in, integrated with, or linkedto the platform 20500. In some of these embodiments, the outcomes of thesimulations may be used to supplement training data collected fromreal-world environments and/or processes. In embodiments, theintelligent services system 20243 leverages machine learned models tomake predictions, identifications, classifications, and recommendations;automate processes, perform marketplace configuration and control,and/or provide decision support relating to the marketplace and/orprocesses represented by respective digital twins.

For example, a set of machine-learned models may be used to predict theprice of an asset at some future point in time. In embodiments, a “set”of machine-learned models may include a set with one member. Inembodiments, a “set” of machine-learned models may include a set withmultiple members. In embodiments, a “set” of machine-learned models mayinclude hybrids of different types of models (e.g., hybrids of RNN andCNN). In this example, the intelligent services system 20243 may receiveasset data, historical pricing data, discussion board data, and newsdata and may generate a set of feature vectors based on the receiveddata. The intelligent services system 20243 may input the feature vectorinto the set of machine-learned models trained specifically for theasset (e.g., using a combination of simulation data and real-world data)to predict the price of the asset at a future point in time. Inembodiments, the feature vector may include a set of predictions, suchas ones made by human experts, by other systems, and/or by other models.Such artificial intelligence systems used for prediction (in thisexample and other examples described throughout this disclosure) mayinclude a recurrent neural network (including a gated recurrent neuralnetwork), a convolutional neural network, a combination of a recurrentneural network and a convolutional neural network, or other types ofneural network or combination or hybrid of types of neural networkdescribed herein or in the documents incorporated by reference herein.

In examples, a set of machine-learned models may be used to predict theprobability of order execution for an order. In this example, theintelligent services system 20243 may receive order data, historicalorder data, and location data for the marketplace participant userdevice 20218 and may generate a set of feature vectors based on thereceived data. The intelligent services system 20243 may input thefeature vectors into machine-learned models trained (e.g., using acombination of simulation data and real-world data) to predict theprobability of order execution for an order, such as based on a trainingdata set of outcomes. In embodiments, the system 20243 may include aninput set of training data representing predictions or the probabilityof order execution by a set of human experts and/or by other systems ormodels.

In examples, a set of machine-learned models may be used to predict theprofitability of a marketplace. In this example, the intelligentservices system 20243 may receive marketplace configuration parameterdata (e.g., asset type(s), fees, anonymity settings, and the like) andmay generate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to predict the profitability of a marketplace,such as based on a training data set of outcomes. In embodiments, theintelligent services system 20243 may include an input set of trainingdata representing predictions related to marketplace profitability by aset of human experts and/or by other systems or models.

In yet other examples, a set of machine-learned models may be used topredict the execution speed for a marketplace at a given point in time.In this example, the intelligent services system 20243 may receivemarketplace configuration parameter data and marketplace operationaldata and may generate feature vectors based on the received data. Inembodiments, feature vectors may include other data, such as datacharacterizing information technology elements upon which executionspeed may depend, including network path information (e.g., the type offixed and/or wireless network, what networking protocols are used, thedistance of physical layer paths, and the like); computational resourceinformation (such as types and processing capabilities of servers andother data center resources, including, as applicable, availability ofmulti-core and/or multi-threaded processing, quantum computation and/orquantum algorithm execution, and the like, as well as edge computationalcapabilities that are available on premises involved in marketplaceexecution, in data centers that support cloud computing for marketplaceexecution and in local and telecommunications networks that supportmarketplace execution); data storage and retrieval information (such asinput/output performance specifications for databases and other storageresources, caching performance capabilities, data location information(e.g., geo-location and federation of data resources), query performanceinformation, and the like), and many others. The intelligent servicessystem 20243 may input the feature vectors into machine-learned modelstrained (e.g., using a combination of simulation data and real-worlddata) to predict the execution speed for a marketplace at a given pointin time from the point of view of a system that is at a given location(e.g., a geo-location, a network address, or the like). Prediction ofexecution speed may involve testing and simulation, such as usingsimulation methods and systems described herein, as well as in thedocuments incorporated by reference herein. This may include, in onenon-limiting example, testing the latency, bandwidth, upload speed,download speed, round-trip speed, ping, or other network performancecharacteristics, such as by, optionally automatically, sending testsignals that provide an indication of current network speed, executionspeed, or the like.

In examples, a set of machine-learned models may be used to detectillicit and/or illegal items and/or services listed in a marketplace. Inthis example, the intelligent services system 20243 may receive assetlisting data and may generate feature vectors based on the receiveddata. The intelligent services system 20243 may input the featurevectors into machine-learned models trained (e.g., using a combinationof simulation data and real-world data) to detect illicit and/or illegalitems and/or services listed in the marketplace. In embodiments,detection of illicit and/or illegal items may involve a set of distinctmodels that are respectively trained based on training data sets and/orfeature vector inputs that are specific to jurisdictional factors,including laws or regulations (e.g., training with awareness oflegality), cultural factors (e.g., where whether the item is consideredillicit varies based on cultural norms), religious factors (e.g.,training the model with awareness of proscribed items), and the like.For example, a model may be trained to detect whether an item is kosher,whether it satisfies other cultural and/or religious requirements, orthe like. In embodiments, training may include providing, such asthrough human experts, information about alternative terminology, or thelike, that sellers or other users may employ to offer illegal or illicititems, such as code words, euphemisms, or the like. In embodiments, amodel may be trained to provide a word cloud or cluster of words orother features, such as to facilitate recognition of illegal or illicititems and/or recognition of words, images, or other elements used tocharacterize them. As one non-limiting example, a self-organizing map(SOM) may be employed to generate a mapping of entities, such as mappingentities, classes, objects, workflows, or the like to jurisdictions, totopics, to each other, or the like.

In yet other examples, a set of machine-learned models may be used todetect trading patterns of a trader in a marketplace. In this example,the intelligent services system 20243 may receive trader data and orderdata and may generate feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to detect trading patterns for a particulartrader in the marketplace. In embodiments, trading patterns may belinked to strategies, such that the model may be used to determine a setof governing strategies, heuristics, models, rules, or other governingprinciples (collectively referred to for convenience as “strategies”) ofa trader or other counterparty to a transaction. Thus, a machine-learnedmodel may take various feature vectors related to marketplace activitiesand output a determination of a strategy of a party, such as a user or acounterparty. Such a determination may facilitate identification andoptionally automated recommendation to a user of resources, such as dataresources, models, predictions, and the like, that are consistent withand/or that support or enable the defined strategy. In otherembodiments, such a determination may facilitate identification andoptionally automated recommendations to a counterparty user, such as toassist the counterparty in identifying complementary strategies (e.g.,where two parties are seeking opposite sides of the same type of trade)and/or competitive strategies (e.g., where the strategy of acounterparty makes the counterparty vulnerable to trading strategies).Models may be trained to recognize various strategies, such as arbitragestrategies (e.g., where a counterparty's strategy is likely to over- orunder-value an asset class in a certain set of situations), squeezestrategies (such as a short squeeze where a counterparty has taken alarge “short” position anticipating that an asset is overvalued, where ahigher volume of orders that increase prices force the counterparty toabandon the short position due to growing risk), market corneringstrategies, and the like. Feature vectors that may be used to trainmachine-learning models to identify trading patterns and strategies mayinclude trade sizes, sequences (e.g., combinations of buy and sellorders in given sequences), position sizes (including short and longpositions of assets, options, futures, derivatives and the like),trading volume metrics, relative sizes of positions (e.g., share oftotal market positions), market metrics (e.g., overall P/E ratios),external data (e.g., relating to general economic conditions, weather,geopolitical factors, and the like), and many others. In embodiments,automated, machine-learned strategy recognition enables furtherautomation (including by robotic process automation, such as trained onstrategic decisions of human experts) of marketplace strategy, includingautomated recommendation of trades and automated recommendation ofcomplementary and competitive strategies. This may be referred to as acounterparty strategy engine, such term encompassing variouscapabilities by which the platform 20500 may employ machine learningand/or other intelligence capabilities to facilitate complementaryand/or competitive trading strategies based on understanding thepatterns and strategies of counterparties. Trading strategies that maybe generated, detected, managed, or countered using artificialintelligence, such as machine-learned models described herein, mayinclude a wide variety of strategies, including, without limitation: (a)buy and hold, or “fundamental” strategies (where input data sources andresulting feature vectors may be sought that relate to long-termfundamental performance, such as data sources relating to trends inasset class values, asset-related income streams (e.g., rents,royalties, interest rates, and the like), pricing and related metrics(such as P/E ratios), cost accounting information, tax information,exchange rate information, macroeconomic information (such as inflationinformation, unemployment information, gross domestic productinformation), and the like); (b) long/short equity strategies, such asones that tranche securities into long and short buckets based oncalculated alpha factors, with long positions taken on relativelyfavorable alpha assets or asset classes and short positions taken onrelatively unfavorable alpha assets or asset classes (where input datasources and feature vectors include many of the same factors used forbuy and hold strategies, with a particular interest in indicators ofrelative performance among securities or other assets, such as relativeP/E ratios, relative historical asset class performance, or the like);(c) asset allocation strategies where parties allocate positions inportfolios among asset classes based on relative risk/return ratios thatare suitable for a party; (d) intertemporal portfolio choice strategiesinvolving bet (e.g. trade) sizing that is configured according to adefined proportion of wealth, such as using the Kelly criterion (wherethe bet size is calculated by maximizing the expected value of thelogarithm of wealth), such as where data sources and feature vectors mayinvolve information indicating the trades made by an identified partyand information indicating the parties total wealth or capitalization;(e) pairs trading strategies where similar stocks are paired and a shortposition is taken on the top (potentially overpriced) asset and a longposition is taken on the bottom (potentially underpriced) asset (whichmay optionally involve pairing similar stocks and using a linearcombination (or other combination) of their price to generate astationary time-series, computing a set of scores, such as z-scores, forthe stationary signal and trading on the spread assuming reversion tothe mean) where input data sources and feature vectors may includetrading data that indicates trades of similar size and timing in pairsof similar assets; (f) swing trading strategies seeking to takeadvantage of volatility, where input data sources and feature vectorsmay relate to pricing information and patterns, as well as factors thatmay influence volatility (such as geopolitical data, social data,macroeconomic data, and the like); (g) scalping strategies (such asmaking very large numbers of trades during a trading session in order toseek to aggregate small profits from each trade based on a spreadbetween the bid and the ask price for an asset) where input data sourcesand feature vectors may relate to trade volumes and sizes and the sizeof the average bid/ask spread, as well as many of the other sources andfeatures noted above; (h) day trading strategies involving buying andselling within the same trading session, thereby closing out positionsduring periods when the market is not operating, which may involve datasources and feature vectors that indicate complementary pairs (e.g., abuy and a sell) of trades of the same quantity of the same asset duringa trading session, among others; (i) news-based or information-basedtrading strategies involving rapidly anticipating the impact of newsevents or other emerging information on asset prices, which may involvedata sources and feature vectors that help predict or anticipate news(e.g., using predictive models), that help identify relevant events inreal-time (such as Internet of Things sources, crowdsources, social datasites, websites, news feeds, and many others) and data sources thatindicate historical trends, such as reactions to similar news or eventsin past trading involving the same or similar asset classes; (j) markettiming strategies, including signal-based trading strategies, momentumtrading strategies, and the like, involving timing the purchase or saleof an asset class based on market signals, which may use a wide varietyof sources used by signals providers to produce aggregate forecasts ofmarket signals as well as other information that indicates patterns ofreaction of markets to new information and events; (k) social tradingstrategies, such as involving trading based on behavioral models ofcounterparty trading, which may involve data sources and feature vectorsthat reflect trading behavior, such as trading volume data, pricepattern data, and the like, in response to market conditions, events andstimuli, including many of the data sources and feature vectorsmentioned herein; (1) front-running strategies (involving detectingindicators of trading intent by a counterparty and rapidly executing atrade before the intent is realized in the form of an actual trade bythe counterparty); (m) chart-based or pattern-based strategies, wheretrading is based on analysis of charts, such as trends in pricing oftrades over a session or series of sessions, such as ones that seek toanticipate future movements in prices based on patterns of past movement(e.g., anticipating an upward surge after a “cup with a handle” pricepattern), which are typically based on some underlying behavioral modelof aggregate trading behavior of a set of traders in a marketplace andwhich may use data source or feature vectors that indicate patterns ofmarket behavior and market outcomes, such as trend charts and othervisual information on patterns; (n) genetic programming, deep learning,or other computer science-based strategies (such as involvingintroducing a degree of random or non-random variation into a tradingalgorithm or model, such as a variation of data source, feature vector,feedback source, weighting, type of neural network, or the like used inan artificial intelligence system, variation of trading patterns (size,timing, price, volume), variation in asset class, variation of strategy,or the like), such as where data sources of feature vectors may beidentified that relate to patterns, trends or changes in any of theabove within marketplace trading data; (o) automated or algorithmictrading strategies (which may be used to implement any of the foregoingand other strategies), where marketplace trading data sources may beused to identify trading patterns that indicate very rapid execution orother patterns that are markers of algorithmic execution; (p) varioushybrids and combinations of the foregoing; and various other tradingstrategies used in any of a wide range of asset classes and marketplacesdescribed herein. Such artificial intelligence systems used fordetection or identification (in this example and other examplesdescribed throughout this disclosure) may include a recurrent neuralnetwork (including a gated recurrent neural network), a convolutionalneural network, a combination of a recurrent neural network and aconvolutional neural network, or other types of neural network orcombination or hybrid of types of neural network described herein or inthe documents incorporated by reference herein.

In yet other examples, a set of machine-learned models may be used todetect an opportunity for a new marketplace. For example, theintelligent services system 20243 may receive data from various sourcesdescribed throughout this document and the documents incorporated byreference herein and may generate a set of feature vectors based on thereceived data. The intelligent services system 20243 may input the setof feature vectors into machine-learned models trained (e.g., using acombination of simulation data and real-world data) to detect anopportunity for a new marketplace. Data sources used to produce the setof feature vectors may include, for example, discussion boards (such asinvolving chats, comment threads or the like about deals, trades, assettypes, streams of value, or the like that may be organized into amarketplace), social media sites (such as involving posts or threadsinvolving assets that can be traded, deals, or the like), websites (suchas announcing products, services, offerings, events, or the like), andothers. As one non-limiting example, content from a set of websites andsocial media sites involving events (such as ones hosted by eventorganizers, event participants, fans, followers, and others) may be fedto a machine-learned model that may be trained to operate on the featurevectors, such as using a neural network (such as an RNN, CNN, SOM orhybrid, among many other options), to output a candidate set of eventsthat may be suitable candidates for a contingent forward market forrights to the event. The model may be trained, for example, to identifyevents that are likely to be very popular (such as involving populartalent, popular teams, or the like) and to identify cases in which someaspect of the event remains contingent, such as timing, location, actualparticipants, and the like, meaning that a contingency can be set forrights (e.g., attendance rights, accommodation rights, transportationrights, and many others) in the forward market. Output from the modelcan thus be used as a candidate set for the contingent forward marketoperator. In examples, product websites content may be fed to the model,which may be trained to identify new product or service offeringsrelevant to a particular cohort of buyers, which may be automaticallygrouped by the model (or another model) into a cohort-targetedmarketplace of similar buyers.

In examples, a set of machine-learned models may be used to identifyoptimal trading opportunities. For example, the intelligent servicessystem 20243 may receive data from various sources described throughoutthis document and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the set of feature vectorsinto machine-learned models trained (e.g., using a combination ofsimulation data and real-world data) to identify optimal tradingopportunities, such as based on a training data set of outcomes. Inembodiments, the intelligent services system 20243 may include an inputset of training data representing identifications related to optimalmarketplace trading opportunities by a set of human experts and/or byother systems or models. Data sources that may be used to producefeature vectors may include, for example, time of day, location ofprice, moving averages, performance of correlated assets, performance ofindexes, discussion boards (such as involving chats, comment threads orthe like about deals, trades, trends, or the like), websites (such asannouncing products, services, offerings, events, or the like), and manyothers.

In examples, a set of machine-learned models may be used to detectfraudulent asset listings. For example, the intelligent services system20243 may receive data from various sources described throughout thisdocument and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to detect fraudulent asset listings, such asbased on a training data set of outcomes. In embodiments, theintelligent services system 20243 may include an input set of trainingdata representing detection related to fraudulent listings by a set ofhuman experts and/or by other systems or models. In embodiments,training may include providing information related to identical and/orsimilar asset listings that may have been fraudulently duplicated. Inembodiments, a model may be trained to provide a word cloud or clusterof words or other features, such as to facilitate recognition offraudulent listings and/or recognition of words, images, or otherelements used to characterize them. Data sources that may be used toproduce feature vectors may include, for example, websites (such aswebsites listing assets, products, services, offerings, or the like),pricing data (such as unusually low pricing), asset description data(such as overly generic asset descriptions or illiterate assetdescriptions), and many others.

In examples, a set of machine-learned models may be used to detectmarket behavior for an asset. For example, the intelligent servicessystem 20243 may receive data from various sources described throughoutthis document and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vector intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to detect market behavior around a particularasset, such as based on a training data set of outcomes. In embodiments,the intelligent services system 20243 may include an input set oftraining data representing detection related to market behavior by a setof human experts and/or by other systems or models. Data sources thatmay be used to produce feature vectors may include trade sizes,sequences (e.g., combinations of buy and sell orders in givensequences), position sizes (including short and long positions ofassets, options, futures, derivatives, and the like), trading volumemetrics, relative sizes of positions (e.g., share of total marketpositions), market metrics (e.g., overall P/E ratios), and many others.

In examples, machine-learned models may be used to identify trendingassets. For example, the intelligent services system 20243 may receivedata from various sources described throughout this document and thedocuments incorporated by reference herein and may generate a set offeature vectors based on the received data. The intelligent servicessystem 20243 may input the feature vectors into machine-learned modelstrained (e.g., using a combination of simulation data and real-worlddata) to identify trending assets, such as based on a training data setof outcomes. In embodiments, the intelligent services system 20243 mayinclude an input set of training data representing detection related totrending assets by a set of human experts and/or by other systems ormodels. Data sources used to produce the set of feature vectors mayinclude, for example, discussion boards (such as involving chats,comment threads involving assets, or the like), social media sites (suchas involving posts or threads involving assets or the like), websites(such as news involving assets or the like), and others. In examples, aset of machine-learned models may be used to determine market sentimentfor a particular asset. For example, the intelligent services system20243 may receive data from various sources described throughout thisdocument and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vector intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to determine market sentiment for the asset,such as based on a training data set of outcomes. In embodiments, theintelligent services system 20243 may include an input set of trainingdata representing determinations related to market sentiment by a set ofhuman experts and/or by other systems or models. Data sources used toproduce the set of feature vectors may include, for example, discussionboards (such as involving chats, comment threads involving assets or thelike), social media sites (such as involving posts or threads involvingassets or the like), external (such as news involving assets or thelike), trading data, volume data, upside/downside volume ratio data,trader data (scanning many markets to find parties who have ever tradedin the asset class or similar asset classes), data indicating focus(e.g., websites of capital allocators indicating areas of focus), dataindicating strategies (indicators of the general strategy of the trader,such as “buy and hold,” “arbitrage,” “day trading”, and many others,which can be used to recruit parties who favor the behavior of the assetclass (e.g., high within-session volatility for day traders versuslong-term fundamental value aggregation (e.g., growing income streams)for buy-and-hold)), trading news data, survey data, open interest data(e.g., total number of futures contracts or options that are held bytraders), put-call ratio data, volatility index (VIX) data, commitmentof traders (COT) data, high-low index data, crowdsourced data,short-term trading index (TRIN) data, advance/decline ratio data, NYSEhigh/low ratio data, NYSE bullish percent index data, data collected byIoT systems (e.g., smart home IoT devices, workplace IoT devices, andthe like) to monitor a set of entities in a set of environments, andmany others.

Such artificial intelligence systems used for decision-making or otherdeterminations (in this example and other examples described throughoutthis disclosure) may include a recurrent neural network (including agated recurrent neural network), a convolutional neural network, acombination of a recurrent neural network and a convolutional neuralnetwork, or other types of neural network or combination or hybrid oftypes of neural network described herein or in the documentsincorporated by reference herein.

In examples, a set of machine-learned models may be used to identifycounterparties with complementary trading positions and/or strategies.For example, the intelligent services system 20243 may receive data fromvarious sources described throughout this document and the documentsincorporated by reference herein and may generate a set of featurevectors based on the received data. The intelligent services system20243 may input the feature vectors into machine-learned models trained(e.g., using a combination of simulation data and real-world data) toidentify counterparties with complementary trading positions and/orstrategies, such as based on a training data set of outcomes. Inembodiments, the intelligent services system 20243 may include an inputset of training data representing identifications related tocounterparties with complementary trading positions and/or strategies bya set of human experts and/or by other systems or models. Inembodiments, automated, machine-learned counterparty recognition enablesfurther automation (including by robotic process automation, such astrained on strategic decisions of human experts) of marketplacestrategy. The counterparty strategy engine may employ machine learningand/or other intelligence capabilities to facilitate counterpartydiscovery based on understanding the patterns and strategies ofcounterparties. Data sources used to produce the set of feature vectorsmay include, for example, trading data (scanning many markets to findparties who have ever traded in the asset class or similar assetclasses), data indicating strategies (indicators of the general strategyof the trader, such as “buy and hold,” “arbitrage,” “day trading”),trader profile data, and many others.

In examples, a set of machine-learned models may be used to detectpotential marketplace participants for marketplace recruitment purposes.For example, the intelligent services system 20243 may receive data fromvarious sources described throughout this document and the documentsincorporated by reference herein and may generate a set of featurevectors based on the received data. The intelligent services system20243 may input the feature vectors into machine-learned models trained(e.g., using a combination of simulation data and real-world data) todetect potential marketplace participants, such as based on a trainingdata set of outcomes. In embodiments, the intelligent services system20243 may include an input set of training data representing detectionrelated to potential marketplace participants by a set of human expertsand/or by other systems or models. Data sources used to produce the setof feature vectors may include, for example, trading data (scanning manymarkets to find parties who have ever traded in the asset class orsimilar asset classes), data indicating focus (e.g., websites of capitalallocators indicating areas of focus), data indicating strategies(indicators of the general strategy of the trader, such as “buy andhold,” “arbitrage,” “day trading”, and many others, which can be used torecruit parties who favor the behavior of the asset class (e.g., highwithin-session volatility for day traders versus long-term fundamentalvalue aggregation (e.g., growing income streams) for buy-and-hold)), andmany others.

In examples, a set of machine-learned models may be used to providedecision support related to configuration of a marketplace. Inembodiments, the decision support may be provided by a marketplaceconfiguration decision support platform. For example, the intelligentservices system 20243 may receive data from various sources describedthroughout this document and the documents incorporated by referenceherein and may generate a set of feature vectors based on the receiveddata. The intelligent services system 20243 may input the featurevectors into machine-learned models trained (e.g., using a combinationof simulation data and real-world data) to provide decision supportrelated to configuration of a marketplace, such as based on a trainingdata set of outcomes. In embodiments, the intelligent services system20243 may include an input set of training data representing decisionsupport related to marketplace configuration by a set of human expertsand/or by other systems or models. In some embodiments, the decisionsupport may relate to guidance on marketplace anonymity settings, feesettings, transaction type (e.g., buy-sell, auction, reverse auction, orthe like), listing requirements, supported trading types, and the like.In the present example, the intelligent services system 20243, which mayreceive asset data (optionally including asset demand data, supply data,cost data, volatility data, pricing pattern data, trade size data, tradevolume data, geographic trading data, trading party profile data,previous close data, open data, low data, high data, price data, changedata, change percent data, 52 week low data, 52 week high data, sharesoutstanding data, market capitalization data, price-to-earnings (P/E)data, beta data, asset/instrument type data, industry data, employeedata, last trade time data, asset location data, asset condition data,asset performance data, asset dimension data, asset brand data, assetmaterial data, and/or many other types of asset-related data),marketplace profitability data, laws or regulations (e.g., training withawareness of legality), and many others.

In some examples, the marketplace configuration-related decision supportmay relate to guidance on marketplace anonymity settings, fee settings,supported transaction types (e.g., buy-sell, auction, reverse auction,or the like), asset listing requirements, whether futures tradingmechanisms are enabled, whether price arbitrage mechanisms are enabled,whether derivatives trading mechanisms are enabled, supported tradingtypes, selection of data storage and use policies, and many othersdescribed throughout this document and documents incorporated byreference herein.

In examples, a set of machine-learned models may be used to providedecision support related to the pricing of one or more assets. Forexample, the intelligent services system 20243 may receive data fromvarious sources described throughout this document and the documentsincorporated by reference herein and may generate a set of featurevectors based on the received data. The intelligent services system20243 may input the feature vectors into machine-learned models trained(e.g., using a combination of simulation data and real-world data) toprovide decision support related to the pricing of one or more assets,such as based on a training data set of outcomes. In embodiments, theintelligent services system 20243 may include an input set of trainingdata representing decision support related to asset pricing by a set ofhuman experts and/or by other systems or models. Data sources used toproduce the set of feature vectors, may include, but are not limited to,asset data (optionally including asset demand data, supply data, costdata, volatility data, pricing pattern data, trade size data, tradevolume data, geographic trading data, trading party profile data, and/ormany other types of asset-related data), discussion boards (such asinvolving chats, comment threads involving assets or the like), socialmedia sites (such as involving posts or threads involving assets or thelike), external (such as news involving assets or the like), and others.

In examples, a set of machine-learned models may be used to providedecision support related to order request parameters (e.g., pricing,quantity, action type, and the like). For example, the intelligentservices system 20243 may receive data from various sources describedthroughout this document and the documents incorporated by referenceherein and may generate a set of feature vectors based on the receiveddata. The intelligent services system 20243 may input the featurevectors into machine-learned models trained (e.g., using a combinationof simulation data and real-world data) to provide decision supportrelated to order request parameters, such as based on a training dataset of outcomes. In embodiments, the system 20243 may include an inputset of training data representing decision support related to orderrequest parameters by a set of human experts and/or by other systems ormodels. Data sources used to produce the set of feature vectors, mayinclude, but are not limited to, pricing data, profitability data,operational data, product or service performance data, liability data,party performance data, market data (e.g., price trends, volatility, andothers), and many others.

In examples, a set of machine-learned models may be used to providedecision support related to cancelling orders. For example, theintelligent services system 20243 may receive data from various sourcesdescribed throughout this document and the documents incorporated byreference herein and may generate a set of feature vectors based on thereceived data. The intelligent services system 20243 may input thefeature vectors into machine-learned models trained (e.g., using acombination of simulation data and real-world data) to provide decisionsupport related to order cancellation, such as based on a training dataset of outcomes. In embodiments, the intelligent services system 20243may include an input set of training data representing decision supportrelated to cancelling orders by a set of human experts and/or by othersystems or models. Data sources used to produce the set of featurevectors, may include, but are not limited to, asset data (optionallyincluding asset demand data, supply data, cost data, volatility data,pricing pattern data, trade size data, trade volume data, geographictrading data, trading party profile data, and/or many other types ofasset-related data), external data (such as news involving assets or thelike), and others.

In examples, a set of machine-learned models may be used to providedecision support related to setting smart contract parameters (e.g.,pricing, quantity, delivery, and the like). Taking the example further,for a smart contract related to replacement part for a machine, theintelligent services system 20243 may receive historical and currentdata from or about the machine and/or a facility in which it is locatedand may generate a set of feature vectors based on the received data.The intelligent services system 20243 may input the set of featurevectors into a machine-learned model trained (e.g., using a combinationof simulation data and real-world data) to provide decision supportrelated to setting smart contract parameters. In embodiments, a model orset of models may be trained by an expert in the replacement parts andservice marketplace to configure appropriate price, service, anddelivery terms and conditions for replacement of the part for themachine, based on the historical and current data. Smart contractconfiguration may involve sets of feature vectors using or derived fromhistorical contract performance data, including pricing data,profitability data, operational data, product or service performancedata, liability data, data indicative of failure rates (e.g., productfaults, service failures, delivery failures, and many others), partyperformance data, market data (e.g., price trends, volatility, andothers), and many others.

In yet other examples, a set of machine-learned models may be used todetermine regulatory compliance of a marketplace, a trader, a broker, atrade, an asset listing, a holder of inside information, or the like.For example, the intelligent services system 20243 may receive data fromvarious sources described throughout this document and the documentsincorporated by reference herein and may generate a set of featurevectors based on the received data. The intelligent services system20243 may input the feature vectors into machine-learned models trained(e.g., using a combination of simulation data and real-world data) todetermine regulatory compliance. As one non-limiting example, regulatorycompliance may include compliance with regulations that prohibit holdersof inside information from signaling the market in advance of tradingactivities to their benefit. In embodiments, relating to such anexample, a machine-learned model may parse large bodies of material,such as press releases, podcasts, interviews, and the like, such as tofind instances of signaling. In embodiments, automated identification ofsimilar content, and respective timing, among public, or semi-publiccommunications, trading activities, and content of securities filingsmay be performed to identify suspicious sequences, such as where a tradewas followed by a public statement that impacted the value of the trade.In examples, the machine-learned model may parse trades, and tradetiming, along with evidence of party relatedness (e.g., social mediaconnections) to find indications of insider trading where an insideparty has tipped an outside party, such as a family member, a businessassociate, a colleague, or the like. Embodiments extend to policycompliance, such as for marketplaces where insider trading is notprohibited but would be frowned upon if discovered to be done, such aswhere parties are rated based on the extent of their inside tradingactivity.

In yet other examples, a set of machine-learned models may be used togenerate a trading strategy. For example, the intelligent servicessystem 20243 may receive data from various sources described throughoutthis document and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to generate a trading strategy. Generation ofa trading strategy may be trained on outcomes, including by use ofvarious metrics that indicate trading strategy performance, optionallyincluding risk-adjusted strategy performance, such as Sharpe ratios,multiples on invested capital, investment rates of return (IRRs),cost-adjusted return metrics, benchmark comparisons (e.g., benchmarkingagainst an index fund), and many others.

Trading strategies that may be generated, detected, managed, orcountered using artificial intelligence, such as machine-learned modelsdescribed herein, may include a wide variety of strategies, including,without limitation: (a) buy and hold, or “fundamental” strategies (whereinput data sources and resulting feature vectors may be sought thatrelate to long-term fundamental performance, such as data sourcesrelating to trends in asset class values, asset-related income streams(e.g., rents, royalties, interest rates, and the like), pricing andrelated metrics (such as P/E ratios), cost accounting information, taxinformation, exchange rate information, macroeconomic information (suchas inflation information, unemployment information, gross domesticproduct information), and the like); (b) long/short equity strategies,such as ones that tranche securities into long and short buckets basedon calculated alpha factors, with long positions taken on relativelyfavorable alpha assets or asset classes and short positions taken onrelatively unfavorable alpha assets or asset classes (where input datasources and feature vectors include many of the same factors used forbuy and hold strategies, with a particular interest in indicators ofrelative performance among securities or other assets, such as relativeP/E ratios, relative historical asset class performance, or the like);(c) asset allocation strategies where parties allocate positions inportfolios among asset classes based on relative risk/return ratios thatare suitable for a party; (d) intertemporal portfolio choice strategiesinvolving bet (e.g. trade) sizing that is configured according to adefined proportion of wealth, such as using the Kelly criterion (wherethe bet size is calculated by maximizing the expected value of thelogarithm of wealth), such as where data sources and feature vectors mayinvolve information indicating the trades made by an identified partyand information indicating the parties total wealth or capitalization;(e) In pairs trading strategies where similar stocks are paired and ashort position is taken on the top (potentially overpriced) asset and along position is taken on the bottom (potentially underpriced) asset(which may optionally involve pairing similar stocks and using a linearcombination (or other combination) of their price to generate astationary time-series, computing a set of scores, such as z-scores, forthe stationary signal and trading on the spread assuming reversion tothe mean) where input data sources and feature vectors may includetrading data that indicates trades of similar size and timing in pairsof similar assets; (f) swing trading strategies seeking to takeadvantage of volatility, where input data sources and feature vectorsmay relate to pricing information and patterns, as well as factors thatmay influence volatility (such as geopolitical data, social data,macroeconomic data, and the like); (g) scalping strategies (such asmaking very large numbers of trades during a trading session in order toseek to aggregate small profits from each trade based on a spreadbetween the bid and the ask price for an asset) where input data sourcesand feature vectors may relate to trade volumes and sizes and the sizeof the average bid/ask spread, as well as many of the other sources andfeatures noted above; (h) day trading strategies involving buying andselling within the same trading session, thereby closing out positionsduring periods when the market is not operating, which may involve datasources and feature vectors that indicate complementary pairs (e.g., abuy and a sell) of trades of the same quantity of the same asset duringa trading session, among others; (i) news-based or information-basedtrading strategies involving rapidly anticipating the impact of newsevents or other emerging information on asset prices, which may involvedata sources and feature vectors that help predict or anticipate news(e.g., using predictive models), that help identify relevant events inreal-time (such as Internet of Things sources, crowdsources, social datasites, websites, news feeds and many others) and data sources thatindicate historical trends, such as reactions to similar news or eventsin past trading involving the same or similar asset classes; (j) markettiming strategies, including signal-based trading strategies, momentumtrading strategies and the like, involving timing the purchase or saleof an asset class based on market signals, which may use a wide varietyof sources used by signals providers to produce aggregate forecasts ofmarket signals, as well as other information that indicates patterns ofreaction of markets to new information and events; (k) social tradingstrategies, such as involving trading based on behavioral models ofcounterparty trading, which may involve data sources and feature vectorsthat reflect trading behavior, such as trading volume data, pricepattern data, and the like, in response to market conditions, events andstimuli, including many of the data sources and feature vectorsmentioned herein; (1) front-running strategies (involving detectingindicators of trading intent by a counterparty and rapidly executing atrade before the intent is realized in the form of an actual trade bythe counterparty); (m) chart-based or pattern-based strategies, wheretrading is based on analysis of charts, such as trends in pricing oftrades over a session or series of sessions, such as ones that seek toanticipate future movements in prices based on patterns of past movement(e.g., anticipating an upward surge after a “cup with a handle” pricepattern), which are typically based on some underlying behavioral modelof aggregate trading behavior of a set of traders in a marketplace andwhich may use data source or feature vectors that indicate patterns ofmarket behavior and market outcomes, such as trend charts and othervisual information on patterns; (n) genetic programming, deep learning,or other computer science-based strategies (such as involvingintroducing a degree of random or non-random variation into a tradingalgorithm or model, such as a variation of data source, feature vector,feedback source, weighting, type of neural network, or the like used inan artificial intelligence system, variation of trading patterns (size,timing, price, volume), variation in asset class, variation of strategy,or the like), such as where data sources of feature vectors may beidentified that relate to patterns, trends or changes in any of theabove within marketplace trading data; (o) automated or algorithmictrading strategies (which may be used to implement any of the foregoingand other strategies), where marketplace trading data sources may beused to identify trading patterns that indicate very rapid execution orother patterns that are markers of algorithmic execution; (p) varioushybrids and combinations of the foregoing; and various other tradingstrategies used in any of a wide range of asset classes and marketplacesdescribed herein.

In yet other examples, a set of machine-learned models may be used todetect a trading strategy, such as of a set of counterparties. Theintelligent services system 20243 may receive data from various sourcesand may generate a set of feature vectors based on the received data.The intelligent services system 20243 may input the set of featurevectors into a machine-learned model trained to detect the tradingstrategy and to generate an output that classifies the trading strategy.In embodiments, the model may be trained on a training data set whereinexpert traders classify trading strategies based on the data sourcesand/or upon outcomes (such as outcomes of counterstrategies that wereselected based on the classifications and/or upon outcomes where one ormore parties validated the accuracy of the classification). Inembodiments, the model may be generated by deep learning. Inembodiments, the model may be supervised, unsupervised, orsemi-supervised. In embodiments, the model may use a recurrent neuralnetwork, optionally a gated recurrent neural network that providesimproved performance as a result of placing diminishing weight on agingdata and that mitigates compounding error problems. In embodiments, themodel may employ a convolutional neural network (alone or in combinationwith another type of neural network, such as a recurrent neuralnetwork), such as where images of trading patterns (e.g., pricepatterns, volatility patterns, volume patterns and the like) areprovided as input data sources to the model. Once a trading strategy isclassified, a further machine-learned model as previously described maygenerate an appropriate trading strategy that is a suitablecounterstrategy to the detected strategy.

In yet other examples, a set of machine-learned models may be used toselect a trading strategy from a set of trading strategies, includingany of the strategies described herein and/or in the documentsincorporated herein by reference. For example, the intelligent servicessystem 20243 may receive data from various sources described throughoutthis document and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to select a trading strategy from a set oftrading strategies, such as based on a training data set of outcomes. Inembodiments, the intelligent services system 20243 may include an inputset of training data representing decision support related to selectingtrading strategies by a set of human experts and/or by other systems ormodels. Selection of a trading strategy may be trained on outcomes,including by use of various metrics that indicate trading strategyperformance, optionally including risk-adjusted strategy performance,such as Sharpe ratios, multiples on invested capital, investment ratesof return (IRRs), cost-adjusted return metrics, benchmark comparisons(e.g., benchmarking against an index fund), and many others. Datasources and feature vectors used for management may include marketplacedata of the many types described herein as well as external data sourcesthat may assist with prediction of trading behavior and marketplacepatterns.

In yet other examples, a set of machine-learned models may be used tomanage a trading strategy, including any of the strategies describedherein and/or in the documents incorporated herein by reference. Forexample, the intelligent services system 20243 may receive data fromvarious sources described throughout this document and the documentsincorporated by reference herein and may generate a set of featurevectors based on the received data. The intelligent services system20243 may input the set of feature vectors into a machine-learned modelthat is trained (e.g., using a combination of simulation data andreal-world data) to manage the trading strategy. The model performingmanagement of the trading strategy may be trained based on a trainingset of management decisions by a set of experts that manage thestrategies, and in embodiments, may employ robotic process automation bytraining on a set of inputs by experts in a management interface thatmanages the trading strategy. The model may be a deep learning model, asupervised model, an unsupervised model and/or a semi-supervised modeland may employ any of the artificial intelligence techniques and systemsdescribed herein and/or in the documents incorporated by reference. Inembodiments, the management model is trained on outcomes/feedback, suchas one or more performance metrics described herein, such as a Sharperatio or other metric of model performance. Data sources and featurevectors used for management may include marketplace data of the manytypes described herein as well as external data sources that may assistwith prediction of trading behavior and marketplace patterns. A strategymanagement model may be configured to implement a set of rules orpolicies, such as ones that require halting of trading in extremecircumstances, ones that shift to alternate strategies based oncontextual or market conditions, or the like. For example, a set ofrules may provide for a primary strategy and a set of contingentstrategies that are triggered upon determination of a set of triggers,whereby the management model automatically shifts to the contingentstrategy upon detection of the trigger. For example, a buy and holdstrategy may be configured to shift to an active trading (e.g., selling,or shorting) strategy upon detection that the aggregate price/earningsratio of a marketplace exceeds a defined value (such as suggesting thatthe entire asset class is overvalued). As another example, a day tradingstrategy may be configured to shift automatically to a long/shortstrategy if in-session price volatility is detected to be below adefined threshold metric, implying that day trading is unlikely to beprofitable on a cost- and risk-adjusted basis due to trading costs. Inembodiments, a set of trading strategies may be structured in ahierarchy, a flow diagram, a graph (optionally a directed acyclicgraph), or the like, which may be configured in a data schema(optionally stored in a graph database or similar data resource) thatcan be parsed by the machine-learned strategy management model todetermine a sequence of trading strategies in response to determinationof triggers. In embodiments, the data schema capturing the set oftrading strategies may include triggers (states, conditions, thresholds,and the like) for triggering shifts in trading strategy, as well as therules, configuration parameters, and other data and metadata needed toconfigure the model for management of each strategy or set or set ofstrategies.

In examples, a set of machine-learned models may be used to categorizeor classify traders. For example, the intelligent services system 20243may receive data from various sources described throughout this documentand the documents incorporated by reference herein and may generate aset of feature vectors based on the received data. The intelligentservices system 20243 may input the set of feature vectors into amachine-learned model trained (e.g., using a combination of simulationdata and real-world data) to categorize traders, such as based on atraining data set of outcomes. In embodiments, the intelligent servicessystem 20243 may include an input set of training data representingcategorizations or classifications of traders by a set of human expertsand/or by other systems or models. Data sources and feature vectors usedfor categorization or classification of traders may include marketplacedata of the many types described herein, trader profile data, as well asexternal data sources (such as from social media content originatingfrom or relating to traders or discussion board content originating fromor relating to traders) that may assist with classification orcategorization of traders. Such artificial intelligence systems used forclassification, in the present example and other examples describedherein, may include a recurrent neural network (including a gatedrecurrent neural network), a convolutional neural network, a combinationof a recurrent neural network and a convolutional neural network, orother types of neural network or combination or hybrid of types ofneural network described herein or in the documents incorporated byreference herein.

In yet other examples, a set of machine-learned models may be used toclassify or categorize assets. For example, the intelligent servicessystem 20243 may receive data from various sources described throughoutthis document and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to categorize assets, such as based on atraining data set of outcomes. In embodiments, the intelligent servicessystem 20243 may include an input set of training data representingcategorizations or classifications of assets by a set of human expertsand/or by other systems or models. Data sources and feature vectors usedfor categorization or classification of assets may include fromhistorical asset performance data, including pricing data, profitabilitydata, operational data, product or service performance data, liabilitydata, data indicative of failure rates (e.g., product faults, servicefailures, delivery failures, and many others), party performance data,market data (e.g., price trends, volatility, and others), asset data(including asset descriptions, asset profiles, content associated withassets (including images, video, and audio)), as well as external datasources (such as from websites related to assets) that may assist withclassification or categorization of assets, and many others.

In examples, a set of machine-learned models may be used toautomatically configure a marketplace. For example, the intelligentservices system 20243 may receive data from various sources describedthroughout this document and the documents incorporated by referenceherein and may generate a set of feature vectors based on the receiveddata. The intelligent services system 20243 may input the featurevectors into machine-learned models trained (e.g., using a combinationof simulation data and real-world data) to automatically configure amarketplace, such as based on a training data set of outcomes. Inembodiments, the intelligent services system 20243 may include an inputset of training data representing marketplace configurations by a set ofhuman experts and/or by other systems or models. Data sources andfeature vectors used for configuration of marketplaces may include assetdata (optionally including asset demand data, supply data, cost data,volatility data, pricing pattern data, trade size data, trade volumedata, geographic trading data, trading party profile data, and/or manyother types of asset-related data), marketplace profitability data,marketplace efficiency data, latency data, as well as external datasources (such as from laws or regulations) that may assist withmarketplace configuration. Such artificial intelligence systems used forconfiguration, in the present example and other examples describedherein, may include a recurrent neural network (including a gatedrecurrent neural network), a convolutional neural network, a combinationof a recurrent neural network and a convolutional neural network, orother types of neural network or combination or hybrid of types ofneural network described herein or in the documents incorporated byreference herein.

In examples, a set of machine-learned models may be used to optimizemarketplace efficiency. For example, the intelligent services system20243 may receive data from various sources described throughout thisdocument and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to optimize the efficiency of a marketplace,such as based on a training data set of outcomes. In embodiments, theintelligent services system 20243 may include an input set of trainingdata representing marketplace efficiency optimization by a set of humanexperts and/or by other systems or models. Data sources and featurevectors used for optimization of marketplace efficiency may includemarketplace data of the many types described herein (optionallyincluding transaction matching speed data) that may assist withmarketplace efficiency optimization. Such artificial intelligencesystems used for optimization, in the present example and other examplesdescribed herein, may include a recurrent neural network (including agated recurrent neural network), a convolutional neural network, acombination of a recurrent neural network and a convolutional neuralnetwork, or other types of neural network or combination or hybrid oftypes of neural network described herein or in the documentsincorporated by reference herein.

In examples, a set of machine-learned models may be used to optimizemarketplace profitability. For example, the intelligent services system20243 may receive data from various sources described throughout thisdocument and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to optimize the profitability of themarketplace, such as based on a training data set of outcomes. Inembodiments, the intelligent services system 20243 may include an inputset of training data representing marketplace profitability optimizationby a set of human experts and/or by other systems or models. Datasources and feature vectors used for optimization of marketplaceprofitability may include marketplace data of the many types describedherein (optionally including trading data, commission data, or feesdata) that may assist with marketplace profitability optimization.

In yet other examples, a set of machine-learned models may be used toautomate trading activities. For example, the intelligent servicessystem 20243 may receive data from various sources described throughoutthis document and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to automate trading activities, such as basedon a training data set of outcomes. In embodiments, the intelligentservices system 20243 may include an input set of training datarepresenting trading activities by a set of human experts and/or byother systems or models. Data sources used to produce the set of featurevectors, may include, but are not limited to, asset data (optionallyincluding asset demand data, supply data, cost data, volatility data,pricing pattern data, trade size data, trade volume data, geographictrading data, trading party profile data, and/or many other types ofasset-related data), discussion boards (such as involving chats, commentthreads involving assets, or the like), social media sites (such asinvolving posts or threads involving assets or the like), external (suchas news involving assets or the like), and others. Such artificialintelligence systems used for automation, in the present example andother examples described herein, may include a recurrent neural network(including a gated recurrent neural network), a convolutional neuralnetwork, a combination of a recurrent neural network and a convolutionalneural network, or other types of neural network or combination orhybrid of types of neural network described herein or in the documentsincorporated by reference herein.

In examples, a set of machine-learned models may be used to determine acounterparty's willingness to trade. For example, the intelligentservices system 20243 may receive data from various sources describedthroughout this document and the documents incorporated by referenceherein and may generate a set of feature vectors based on the receiveddata. The intelligent services system 20243 may input the featurevectors into machine-learned models trained (e.g., using a combinationof simulation data and real-world data) to determine a counterparty'swillingness to enter a trade, such as based on a training data set ofoutcomes. In embodiments, the intelligent services system 20243 mayinclude an input set of training data representing willingness to tradeby a set of human experts and/or by other systems or models. Datasources and feature vectors used for determining a counterparty'swillingness to trade may include trader profile data, historical tradingdata for the trader, external data (such as social media data ordiscussion board data relating to the trader/counterparty), ormarketplace data of the many types described herein that may assist withdetermining a counterparty's willingness to enter a trade.

In examples, a set of machine-learned models may be used to generate afairness score for a trade. For example, the intelligent services system20243 may receive data from various sources described throughout thisdocument and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to generate a fairness score for a trade, suchas based on a training data set of outcomes. In embodiments, theintelligent services system 20243 may include an input set of trainingdata representing fairness scores by a set of human experts and/or byother systems or models. Data sources and feature vectors used ingenerating a fairness score may include trader data, marketplaceparticipant device location data, latency data, historical trading data,or marketplace data of the many types described herein that may assistwith generating a fairness score. Such artificial intelligence systemsused for generation (such as the generation of scores or generation ofcontent), in the present example and other examples described herein,may include a recurrent neural network (including a gated recurrentneural network), a convolutional neural network, a combination of arecurrent neural network and a convolutional neural network, or othertypes of neural network or combination or hybrid of types of neuralnetwork described herein or in the documents incorporated by referenceherein.

In examples, a set of machine-learned models may be used to calculatethe financial advantage that a trader would have experienced had he orshe been trading with less latency. For example, the intelligentservices system 20243 may receive data from various sources describedthroughout this document and the documents incorporated by referenceherein and may generate a set of feature vectors based on the receiveddata. The intelligent services system 20243 may input the featurevectors into machine-learned models trained (e.g., using a combinationof simulation data and real-world data) to calculate the financialadvantage that a trader would have experienced had he or she beenexperiencing less latency, such as based on a training data set ofoutcomes. In embodiments, the intelligent services system 20243 mayinclude an input set of training data representing financial advantagecalculations by a set of human experts and/or by other systems ormodels. Data sources and feature vectors used for calculating afinancial advantage may include trader data, marketplace participantdevice location data, latency data, or marketplace data of the manytypes described herein that may assist with calculating a financialadvantage that a trader would have experienced had he or she beenexperiencing less latency. Such artificial intelligence systems used forcalculation, in the present example and other examples described herein,may include a recurrent neural network (including a gated recurrentneural network), a convolutional neural network, a combination of arecurrent neural network and a convolutional neural network, or othertypes of neural network or combination or hybrid of types of neuralnetwork described herein or in the documents incorporated by referenceherein.

In examples, a set of machine-learned models may be used to determinethe risk tolerance of a trader. For example, the intelligent servicessystem 20243 may receive data from various sources described throughoutthis document and the documents incorporated by reference herein and maygenerate a set of feature vectors based on the received data. Theintelligent services system 20243 may input the feature vectors intomachine-learned models trained (e.g., using a combination of simulationdata and real-world data) to determine the risk tolerance of a trader.In embodiments, the intelligent services system 20243 may include aninput set of training data representing trader risk tolerance by a setof human experts and/or by other systems or models. Data sources andfeature vectors used for determining the risk tolerance of a trader mayinclude trader profile data, historical trading data for the trader,external (including social media content related to the trader), ormarketplace data of the many types described herein that may assist withdetermining the risk tolerance of a trader.

The foregoing examples are non-limiting examples and the intelligentservices system 20243 may be used for any other suitableAI/machine-learning related tasks that are performed with respect toindustrial facilities.

In embodiments, the platform 20500 includes an intelligent matchingsystem 20230 for performing AI-driven order matching. In embodiments,intelligent matching system 20230 may leverage order matchingalgorithms. In embodiments, order matching algorithms may include, butare not limited to, allocation, FIFO, FIFO with LMM, FIFO with top orderand LMM, pro-rata, configurable, threshold pro-rata, and thresholdpro-rata with LMM algorithms.

In embodiments, the platform 20500 includes a fairness engine 20268 thatmonitors the execution engine 20228 and calculates the fairness of atransaction. In embodiments, the fairness calculation may be used toadjust the operation of the intelligent matching system 20230 such thatthe intelligent matching system 20230 optimizes the fairness of futuretrades.

In embodiments, the fairness calculation may be used to generateindividual fairness scores for traders. In some embodiments, thefairness score may be an accumulated score that has value and can betraded as a part of the overall system. For example, a marketparticipant (e.g., a trader) may have a negative trading fairness scoreand may trade to increase his or her trading fairness score. Inembodiments, the fairness score may be based on the time of placement(rather than the time of receipt) of a bid or ask. In embodiments, thefairness engine 20268 may be configured to calculate the advantage (suchas in dollars or other measures) that the trader would have experiencedhad he or she been trading with less latency. In some embodiments, theLocal Market Maker (LMM) trades may decrease the quality of the trade(e.g., by increasing the price). In embodiments, the fairness may bebased on or include a measure of the value of the disadvantage to thetrader, wherein the value accumulates over time and the intelligentmatching system 20230 works to reduce the value of the fairnessdisadvantage with future advantageous trades.

In embodiments, a fairness engine may include an execution timingfairness engine that may determine or receive a set of measures oflatency for a set of users, such as traders, and may automaticallyorchestrate a set of configuration parameters or other features thatmitigate unfairness that may be caused by disparate latency (such asunfairness resulting from front-running, rapid execution of trades basedon emerging information, and the like). In embodiments, latency mayresult from a number of factors, including processing performance ofedge computational network devices, the network path through which a setof data packets travels, the network protocol used to transport datapackets, the type and physical characteristics (e.g., length of fiberoptic wire) of physical layer used to transport data packets, the numberof coupling nodes present in the data path, the performance of databasesand other data repositories (e.g., input/output performance), and thecomputational performance of systems used to execute algorithms thatdetermine actions, such as trades. In embodiments, latency may bedetermined by testing network return times, such as by determining theping, the upload speed, the download speed, or the like, such as usingpublicly available systems for testing those parameters. In otherembodiments, latency may be determined by testing responsiveness ofsystems to a set of stimuli, such as by observing the response of asystem (such as an algorithmic trading system) to a set of stimuli,e.g., observing how quickly the system executes a trade in response toan event, such as a bid/ask event, a news event, or the like. Inembodiments, the fairness engine may detect the use of a quantumcomputation system, a quantum algorithm, or the like and may adjustexecution to account for the advantages of quantum computation orquantum algorithmic execution. This may include providing a set ofstimuli that is capable of solution only by quantum computational oralgorithmic techniques and detecting responses from identified tradingsystems. In embodiments, detection may include detection of tradingbehavior that includes evidence of utilization of entangled states, suchas involving simultaneously executed trades across differentmarketplaces that may be governed by the platform 20500.

In embodiments, configuration or orchestration may include any set oftechniques that are designed to mitigate advantages in latency thatresult from any of the foregoing causes of latency. For example, inembodiments, configuration or orchestration may include grouping tradersinto cohorts that experience similar latency, such that trades areexecuted only among members of a cohort. Configuration or orchestrationmay include automatically imposing a delay on low-latency instructions,such as to cause such instructions to be executed with average latency,with a minimum threshold latency, or the like. Configuration ororchestration may include deploying computational or network resourcesthat improve latency for high-latency users, such as by using networkcoding technologies (e.g., random linear network coding, polar coding,and the like), path-based routing technologies, caching technologies,load balancing technologies, and the like to diminish latency, such asto an average level of latency, a minimum threshold level of latency, orthe like. Configuration or orchestration may include applying incentivesand/or penalties, such as by imposing additional trading costs onlow-latency traders and/or rewards or incentives for high-latencytraders. Incentives or penalties may, in embodiments, be accumulated infiat currency, in a cryptocurrency, and/or in a token, such as amarketplace-specific token, such as where tokens accumulated as a resultof a fairness disadvantage may be traded for value in the marketplace.In embodiments, configuration parameters may be based on an averagelatency across a cohort of users and may apply to diminish or eliminatedisadvantages to a subset of users that experience latency within adefined difference from the average, such as not more than 25% morelatency, not more than 50% more latency, not more than 75% more latency,not more than double latency, not more than triple latency, or the like.Setting a limit on the applicability of the fairness engine may avoid ormitigate users intentionally using low-performance systems to acquireadvantages in the system. Configuration or orchestration may includesetting parameters to eliminate fairness disadvantages entirely, or itmay include setting parameters to mitigate fairness disadvantages to anextent, while still allowing faster systems to experience someadvantage.

In embodiments, an execution fairness timing engine may be employed inother areas, such as to ensure fair execution among players in onlinegames or other environments where users compete to undertake actions andoutcomes depend on the relative timing of the actions.

In embodiments, the platform 20500 may include a loyalty system 20270for monitoring the data generator in the execution engine 20228 todetermine when non-cancelled orders are placed. In embodiments, theloyalty system 20270 allows volume amounts for trading to grow as aparty's presence in the market increases. In embodiments, the loyaltysystem 20270 allows points to be accumulated as trades are made. Inthese embodiments, the point accumulation may be made at a delay (suchas a one-minute delay, a five-minute delay, a ten-minute delay, aone-hour delay, or the like), such as to allow for efficient applicationconstruction. In embodiments, the overall value of the trades made arecaptured and points may be calculated based on metrics such as totalvolume, total value to the market, and the like. The matching algorithmsof the intelligent matching system 20230 may be adjusted to provide morefavorable outcomes based on the loyalty level of the trader. Inaddition, new traders to a marketplace may request higher loyalty statusbased on moving their existing business to the new marketplace. Pointsmay be embodied in tokens, such as cryptographically secure tokens,which, in embodiments, may be tradeable for value in the market, or thelike.

In embodiments, the platform 20500 may include a latency factor module20239 for calculating a user's latency. This latency factor may bereceived by the intelligent matching system 20230 to provide a morebalanced trading position for more remote traders.

In embodiments, the intelligent matching system 20230 may enable usersto place trades using secret algorithms. In embodiments, a trader mayprovide a time window for the trade and an associated secret algorithmvia a GUI provided by the platform 20500. For example, a user may inputa second peak price or price over 48.100. Continuing the example, theintelligent matching system 20230 would only publish the first price48.100 and then the bid or ask will adjust the price based on theassociated secret algorithm. As this algorithm is executing on thetrading system, the geographic advantage is removed. As the behavior ismulti-faceted, other traders cannot tell what they expect the bid or askto do in response to market trades.

In some embodiments, the intelligent matching system 20230 is configuredto support quantum order matching. Quantum order matching may allowusers, such as counterparties, to coordinate activities, such assimultaneous buy or sell activity that happens in geographicallydistributed markets. This allows for parties to have identical (butunknown to each other) positions that can then be used as part of asophisticated mechanism to manage the risk of a portfolio. Inembodiments, the quantum computing system 20214 has entangled stateswith other quantum computing systems. These entangled states may beresolved to a known state at a predetermined point in time, which may beused to determine the outcome of a trading action. These positions arethen considered to be coordinated remotely. This allows simultaneouslylarger positions to be moved (e.g., by bidding, asking, buying, selling,or the like) in multiple locations without the individual markets beingforewarned of the overall change in position.

In some embodiments, the platform 20500 includes a deterministic stateexecution machine. A sequence of selling and buying may be built arounddeterministic state execution machine. This deterministic stateexecution machine may provide the same output given an identical input.The deterministic process allows for parallel market execution and tradedetermination processes (including cancellations) to provide for linearscaling of intelligent matching. In embodiments, the intelligentmatching system 20230 reads order data (such as from an order book) inan organized and deterministic way. By following a deterministic processusing systems like finite state machines, simple allocation engines, orother non-random processes, the execution process can always be run inparallel, allowing for multiple matching engines to exist and handleredundancy and parallelism.

In embodiments, the platform 20500 includes a state machine. Inembodiments, the state of a marketplace is held in this state machine,which may be subject to deterministic and nondeterministic stateprocesses. This allows for the management of a complex number of factorsin trade execution (such as loyalty and random outcomes). The overallstate transition process is logged to allow for audit of the process sothat regulators can always determine why an outcome happened.

In embodiments, the platform 20500 includes a cancel order engine forreceiving and processing cancelled orders. In embodiments, cancelledorders may be processed by the execution engine 20228. In embodiments,the platform 20500 includes a passive matching engine. In embodiments,the passive matching engine may be configured to identify messagessubmitted by the marketplace participant user devices 20218 and runidentical state machine and identical code of the primary engine asbackup to the intelligent matching system 20230. In embodiments, machinelearning and/or AI algorithms may be leveraged to generate a decision ofwhich matching engine to use. In embodiments, the platform includes anorder book, which may refer to the list of orders that a marketplaceuses to record offers to buy and sell assets.

In embodiments, the quantum computing system 20214 may support manydifferent quantum models, including, but not limited to, the quantumcircuit model, quantum Turing machine, adiabatic quantum computer,one-way quantum computer, quantum annealing, and various quantumcellular automata. Under the quantum circuit model, quantum circuits maybe based on the quantum bit, or “qubit”, which is somewhat analogous tothe bit in classical computation. Qubits may be in a 1 or 0 quantumstate, or they may be in a superposition of the 1 and 0 states. However,when qubits have measured the result of a measurement, qubits willalways be in either a 1 or 0 quantum state. The probabilities related tothese two outcomes depend on the quantum state that the qubits were inimmediately before the measurement. Computation is performed bymanipulating qubits with quantum logic gates, which are somewhatanalogous to classical logic gates.

In embodiments, the quantum computing system 20214 may be physicallyimplemented using an analog approach or a digital approach. Analogapproaches may include, but are not limited to, quantum simulation,quantum annealing, and adiabatic quantum computation. In embodiments,digital quantum computers use quantum logic gates for computation. Bothanalog and digital approaches may use quantum bits or qubits.

A market orchestration process executed by the platform 20500 may be aprocess whereby an asset (such as a product, service, or the like) isintroduced into a tradable form. In traditional market embodiments,assets may refer to bonds, stocks, cash, and the like, including any ofthe wide variety described herein and/or in the documents incorporatedherein by reference. In non-traditional market embodiments, assets mayrefer to 3D printed products, 3D printing instructions and otherinstruction sets, resources (such as energy, computation, storage, orthe like), attention or other user behavior, services (such as computerprogramming services, microservices, process automation services,artificial intelligence services, and many others), and the like,including the many other examples described herein and/or in thedocuments incorporated herein by reference. In embodiments, the marketorchestration processes using quantum optimization via the quantumcomputing system 20214 may apply equally to traditional andnon-traditional asset marketplaces. Furthermore, embodiments may combinenon-traditional assets and traditional assets in order to extendtraditional markets into non-traditional and hybrid market modules.

In embodiments, the quantum computing system 20214 includes a quantumannealing module 20203 wherein the quantum annealing module may beconfigured to find the global minimum or maximum of a given objectivefunction over a given set of candidate solutions (e.g., candidatestates) using quantum fluctuations. As used herein, quantum annealingmay refer to a meta-procedure for finding a procedure that identifies anabsolute minimum or maximum, such as a size, length, cost, time,distance, or other measures, from within a possibly very large, butfinite, set of possible solutions using quantum fluctuation-basedcomputation instead of classical computation. The quantum annealingmodule 20203 may be leveraged for problems where the search space isdiscrete (e.g., combinatorial optimization problems) with many localminima, such as finding the ground state of a spin glass or thetraveling salesman problem.

In embodiments, the quantum annealing module 20203 starts from aquantum-mechanical superposition of all possible states (candidatestates) with equal weights. The quantum annealing module 20203 may thenevolve, such as following the time-dependent Schrödinger equation, anatural quantum-mechanical evolution of systems (e.g., physical systems,logical systems, or the like). In embodiments, the amplitudes of allcandidate states change, realizing quantum parallelism according to thetime-dependent strength of the transverse field, which causes quantumtunneling between states. If the rate of change of the transverse fieldis slow enough, the quantum annealing module 20203 may stay close to theground state of the instantaneous Hamiltonian. If the rate of change ofthe transverse field is accelerated, the quantum annealing module 20203may leave the ground state temporarily but produce a higher likelihoodof concluding in the ground state of the final problem energy state orHamiltonian.

In some implementations, the quantum computing system 20214 includes atrapped ion quantum computer module 20205, which may be a quantumcomputer that applies trapped ions to solve complex problems. Trappedion quantum computer module 20205 may have low quantum decoherence andmay be able to construct large solution states. Ions, or charged atomicparticles, may be confined, and suspended in free space usingelectromagnetic fields. Qubits are stored in stable electronic states ofeach ion, and quantum information may be transferred through thecollective quantized motion of the ions in a shared trap (interactingthrough the Coulomb force). Lasers may be applied to induce couplingbetween the qubit states (for single-qubit operations) or couplingbetween the internal qubit states and the external motional states (forentanglement between qubits).

In embodiments, the quantum computing system 20214 may includearbitrarily large numbers of qubits and may transport ions to spatiallydistinct locations in an array of ion traps, building large, entangledstates via photonically connected networks of remotely entangled ionchains.

In embodiments, a traditional computer, including a processor, memory,and a graphical user interface (GUI), may be used for designing,compiling, and providing output from the execution, and the quantumcomputing system 20214 may be used for executing the machine languageinstructions. In embodiments, the quantum computing system 20214 may besimulated by a computer program executed by the traditional computer. Insuch embodiments, a superposition of states of the quantum computingsystem 20214 can be prepared based on input from the initial conditions.Since the initialization operation available in a quantum computer canonly initialize a qubit to either the |0> or |1> state, initializationto a superposition of states is physically unrealistic. For simulationpurposes, however, it may be useful to bypass the initialization processand initialize the quantum computing system 20214 directly.

According to embodiments herein, the quantum computing system 20214 mayperform quantum trading orchestration, which may be configured tooptimize difficult-to-correlate, related cross-chain and cross-channelinteractions that, when added together through the use of quantumcomputing, make up an individualized marketplace experience.

In embodiments, the quantum computing system 20214 may include quantuminput filters 20209. In embodiments, quantum input filters 20209 may beconfigured to select whether to run a model on the quantum computingsystem 20214 or to run the model on a classic computing system. In someembodiments, quantum input filters 20209 may filter data for latermodeling on a classic computer. Typically, cross-market platforminteractions are interactions across multiple market platforms. In adynamic market orchestration trading atmosphere, service providers andmarket platforms must be able to engage with agents on all levels, fromvarying delivery devices to varying platforms, both traditional andinnovative. Engagement may need to be delivered in real-time, withgenuine transparency and individualized responses. In embodiments, thequantum computing system 20214 may become an integral part of thisinteraction and allow for the service providers to connect with marketplatforms in an optimized and efficient way. In embodiments, the quantumcomputing system 20214 may provide input to traditional computeplatforms while filtering out unnecessary information from flowing intodistributed platform systems. In some embodiments, the platform 20500may trust through filtered specified experiences for intelligent agents.

Quantum computers are commercially available, but they remain expensiveand limited in capacity, and quantum algorithms are available only for asubset of the host of problems to which they may be applied.Accordingly, the advantages of use of quantum computation within theplatform 20500 (the benefits relative to the costs) are likely to beepisodic. In embodiments, a platform for marketplace orchestrationsystem platform 20500 or other platforms may include model or system forautomatically determining, based on a set of inputs, whether to deployquantum computational or quantum algorithmic resources to a marketplaceactivity (such as trade configuration), whether to deploy traditionalcomputational resources and algorithms, or whether to apply a hybrid orcombination of them. In embodiments, inputs to a model or automationsystem may include trading patterns, energy cost information, capitalcosts for computational resources, development costs (such as foralgorithms), operational costs (including labor and other costs),performance information on available resources (quantum andtraditional), market price and volume information, market volatility,and any of the many other data sets that may be used to simulate (suchas using any of a wide variety of simulation techniques described hereinand/or in the documents incorporated herein by reference) and/or predictthe difference in outcome between a quantum-optimized result and anon-quantum-optimized result from a trading strategy. A machine learnedmodel may be trained, such as by deep learning on outcomes or by a dataset from human expert decisions, to determine what set of resources todeploy given the input data for a given marketplace. The model mayitself be deployed on quantum computational resources and/or may usequantum algorithms, such as quantum annealing, to determine whether,where, and when to use quantum systems, conventional systems, and/orhybrids or combinations.

In embodiments, the quantum computing system 20214 includes quantumoutput filters 20211. In embodiments, quantum output filters 20211 maybe configured to select a solution from solutions of multiple neuralnetworks. For example, multiple neural networks may be configured togenerate solutions to a specific problem (such as the optimal tradingstrategy within a marketplace and/or across a set of marketplaces, givena set of input data), and the quantum output filter 20211 may select thebest solution from the set of solutions.

In some embodiments, the quantum computing system 20214 connects anddirects a neural network development or selection process. In thisembodiment, the quantum computing system 20214 may directly program theweights of a neural network such that the neural network gives thedesired outputs. This quantum-programmed neural network may then operatewithout the oversight of the quantum computing system 20214, but willstill be operating within the expected parameters of the desiredcomputational engine.

In embodiments, the quantum computing system 20214 includes a quantumdatabase engine 20213. In embodiments, quantum database engine 20213 mayassist with the recognition of individuals and identities across marketplatforms by establishing a single identity that is valid acrossinteractions and touchpoints. Aligning to a trader's transaction path,this “stitching” together of cross-device and market platform entitiesmay facilitate building a strong underlying dataset. The quantumdatabase engine 20213 may be configured to perform optimization of datamatching and intelligent traditional compute optimization to matchindividual data elements between roles. Matching may be used toestablish an identity of a counterparty, such as by matching patterns oftrading, such as based on various data inputs, including trade types,timing, geolocation, and the like, among many others.

A quantum rules-based predictive transaction path selection may be basedon having granular, agent-level interaction and behavioral data. Thatknowledge, which may come from internal systems as well as third-partydata sources, may be made actionable through automated triggers set upto respond to specific buyer actions. These individual triggers andlevels may be monitored and optimized through the application of quantumoptimization engines that can oversee the entire process.

The quantum computing system 20214 may include, but is not limited to,analog quantum computers, digital computers, and/or error-correctedquantum computers. Analog quantum computers may directly manipulate theinteractions between qubits without breaking these actions intoprimitive gate operations. In embodiments, quantum computers that mayrun analog machines include, but are not limited to, quantum annealers,adiabatic quantum computers, and direct quantum simulators. The digitalcomputers may operate by carrying out an algorithm of interest usingprimitive gate operations on physical qubits. Error-corrected quantumcomputers may refer to a version of gate-based quantum computers mademore robust through the deployment of quantum error correction (QEC),which enables noisy physical qubits to emulate stable logical qubits sothat the computer behaves reliably for any computation. Further, quantuminformation products may include, but are not limited to, computingpower, quantum predictions, and quantum inventions.

In embodiments, the platform 20500 facilitates one or more intelligentagents 20234 to perform research on electronic marketplace assets, shopand/or scan in different markets, compare marketplaces and assets,discuss assets and market benefits, engage proactively in thefacilitation of markets, ask questions, read reviews, and weave througha variety of mediums and paths before initiating facilitation of amarketplace. In some embodiments, the intelligent agents 20234 may beautomated systems that are engaged in the process of building anelectronic marketplace. In embodiments, intelligent agents 20234 may beconfigured to identify marketplaces that may benefit from mergingbecause of similar assets, similar configuration parameters 20306,similar rules, similar traders, and the like. In embodiments,intelligent agents 20234 may be configured to merge the identifiedmarketplaces. In some embodiments, intelligent agents 20234 may beconfigured to identify marketplaces that may benefit from splitting intomultiple marketplaces. In some embodiments, intelligent agents 20234 maybe configured to split the identified marketplace(s). In embodiments,the quantum computing system 20214 may be configured to allow selecteddata streams to come together and produce optimized directions to theautomated marketplace process.

In some embodiments, the quantum computing system 20214 is configured asan engine that may be used to optimize traditional computers, minimizethe cost of trade in the marketplace, identify and set up systems to acton arbitrage opportunities, and/or combine data from multiple sourcesinto a decision-making process.

The data gathered in the process of the market orchestration may involvereal-time capture and management of interaction data by a wide range oftracking capabilities, both directly associated with transactions andindirectly related to transactions. In embodiments, the quantumcomputing system 20214 may be configured to accept cookies, emailaddresses, and other contact data, social media feeds, news feeds, eventand transaction log data (including transaction events, network events,computational events, and many others), event streams, results of webcrawling, distributed ledger information (including blockchain updatesand state information), results from distributed or federated queries ofdata sources, streams of data from chat rooms and discussion forums, andmany others.

In embodiments, the quantum computing system 20214 includes a quantumregister 20215 having a plurality of qubits. Further, the quantumcomputing system 20214 may include a quantum control system 20219 forimplementing the fundamental operations on each of the qubits in thequantum register and a control processor for coordinating the operationsrequired.

In embodiments, the quantum computing system 20214 is configured tooptimize a marketplace and/or pricing of assets in a marketplace. In anaspect, the quantum computing system 20214 is configured to solve verylargely arbitrage-related optimization problems across marketplaces. Forexample, the quantum computing system 20214 may solve the ideal assetpricing across marketplaces. In embodiments, the quantum computingsystem 20214 may utilize quantum annealing to provide optimized assetpricing. In embodiments, the quantum computing system 20214 may useq-bit based computational methods to optimize asset pricing. In someembodiments, the quantum computing system 20214 is configured to solvearbitrage-related optimization problems across marketplaces.

In embodiments, the quantum computing system 20214 and/or artificialintelligence system of the platform 20500 may be used to determine arate of exchange between, among, or across a set of marketplaces,including ones that trade in different fiat currencies,cryptocurrencies, tokens, in-kind value (e.g., exchanges of services),or other units of exchange, such as by simulating a set of tradingactivities involving the set of marketplaces. For example, an exchangerate may be determined between a renewable energy credit marketplace anda cryptocurrency marketplace (e.g., for Bitcoin™), between a pollutioncredit marketplace and an advertising marketplace, between a stockmarket and a bond market, between different fiat currencies, betweenvarious fiat and cryptocurrencies, between an advertising marketplaceand a loyalty marketplace, and the like, optionally including any pairor other combination of any of the types of marketplace described hereinand/or in the documents incorporated by reference herein. Determining anoptimal exchange range may allow a market orchestrator to adjust anexchange rate to make it closer to optimal and/or it may be used toidentify arbitrage opportunities and/or currency trading opportunitiesthat arise from sub-optimal exchange rates being offered in themarketplace(s).

In embodiments, the quantum computing system 20214 is configured tooptimize a portfolio. A quantum-enhanced portfolio optimization mayinclude building a portfolio of assets to yield the maximum possiblereturn while minimizing the amount of risk or maintaining a risktolerance. Quantum enhancement may provide more precise methods ofoptimizations where the risk/reward balance is calculated inside thequantum computing system 20214. In embodiments, the quantum computingsystem 20214 invests in a wide variety of asset types and classes toprovide the appropriate level of diversification of the portfolio. Inembodiments, quantum enhancement is undertaken with awareness ofvolatility, and in particular volatility that may emerge from chaoticbehavior of relevant marketplace entities (such as where behavior of theentities is highly sensitive to initial conditions), such thatoptimization is applied (optionally automatically) to situations wherean optimal solution is less likely to devolve rapidly to a sub-optimalbehavior as a results of chaotic behavior. For example, quantumenhancement may be more effective to optimize strategies involving verylarge numbers of interactions of entities that change relatively slowly,rather than interactions among very rapidly changing entities, whereslight errors in measurement of initial conditions may rapidlypropagate. In embodiments, models of trading strategies, arbitragestrategies, exchange rate optimization, and many others may includeerror estimation factors based on an understanding of sensitivity toinitial conditions, chaotic/fractal behavior of entities, and the like,which may include an error detection and sensitivity estimation engineconfigured to estimate and/or simulate the sensitivity of a quantumoptimized model or other model described herein to potential errors instate information or other information used to populate the model.

In embodiments, the use of the quantum computing system 20214 todetermine asset classes for investment is a risk-mitigation strategy.Asset classes may include types of securities, debt and equities, andthe like (as with other examples throughout this disclosure, exceptwhere context indicates otherwise, mentions of asset classes throughoutthis disclosure may refer to any of the types described herein and/or inthe documents incorporated by reference herein), and each asset classmay have quite different return and risk characteristics. Inembodiments, vastly different types of asset classes may be combinedtogether to provide an efficient portfolio. By way of example, a quantumoptimization may include a mixture of commodities, equities,cryptocurrencies, bonds, and other assets, such as including variouspairs and combinations that are mutually countercyclical in nature inorder to mitigate overall risk.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 may spread its investment across asset classes,including a mixture of traditional assets and non-traditional assets. Inembodiments, traditional assets may include, but are not limited to,bonds, income-generating bonds, stocks, commodities, contracts, cash,and cash equivalents, and cybercurrency. In embodiments, non-traditionalassets may include, but are not limited to, three-dimensional printedproduct markets, private company funding facilities, trade services, andprogramming services.

In some embodiments, the quantum computing system 20214 or other systemsof the platform 20500 may be configured to provide a marketplace thattrades on the bond and commodities markets and exposes the buyers andsellers to a higher-level security and that has a risk profile similarto a mutual fund.

In some embodiments, the quantum computing system 20214 or other systemsof the platform 20500 may be configured to predict volatility in assetsand/or markets, enabling a lower risk profile for investment strategies.In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 may be applied to manage defined risk factors,providing markets where buyers and sellers are operating at higher orlower levels of risk.

In some embodiments of the present invention, the quantum computingsystem 20214 or other systems of the platform 20500 may assign anoptimization weight for each asset class (and all assets within eachclass) traded in a marketplace. In embodiments, the weight may bedefined as the percentage of the portfolio that concentrates within anyparticular class. For example, the quantum computing system 20214 orother systems of the platform 20500 may apply a 10% weight to stocks anda 20% weight to bonds. This weighting results in bonds being twice asimportant as stocks in the portfolio. In examples, the quantum computingsystem 20214 or other systems of the platform 20500 may assignsub-weights to slow-growth stocks and fast-growth stocks at 20% and 10%,respectively. The implementation of the quantum computing system 20214or other systems of the platform 20500 with associated classicalcomputing systems may enable the continuing maintenance of these assetweights. In embodiments, the quantum computing system 20214 or othersystems of the platform 20500 may be configured to adjust the weights toproduce the desired risk profile for the overall portfolio.

According to embodiments herein, the user may assign asset weights basedupon his/her risk and return tolerance. If the user hopes to minimizethe risk, he or she would assign greater weight to low-risk, low-growthassets. In the example above, the quantum computing system 20214 orother systems of the platform 20500 has performed the similar procedureby assigning twice as much weight to safe investments as profitableones.

In some implementations, the quantum computing system 20214 or othersystems of the platform 20500 may perform a plurality of specificassessments, such as determining the investment goals of a trader, therisk tolerance of the trader, and the like. Upon performing the specificassessments, the quantum computing system 20214 or other systems of theplatform 20500 may assign weights to different asset classes to maintainthe balance between the risk and return preferences. The quantumcomputing system 20214 or other systems of the platform 20500 may seekan efficient frontier, which may refer to a maximum amount an investmentcan earn given its established risk level.

For example, if the quantum computing system 20214 or other systems ofthe platform 20500 determines that a 20% risk of loss is the trader'srisk tolerance, the quantum computing system 20214 or other systems ofthe platform 20500 will build a portfolio that can make the most moneypossible without exceeding that risk threshold. Continuing the example,the quantum computing system 20214 or other systems of the platform20500 may select the following assets for its portfolio based on eachone's promised returns: Bond ABC (risk 10%), Stock XYZ (risk 50%), andStock TUV (risk 30%).

In embodiments, the quantum computing system 20214 includes a quantumcomputation module 20221 to calculate the weights. Typically, in anon-optimized portfolio, the users might place too much money in BondABC, thus reducing the possible returns, or over-invest in Stock XYZ,which would create too much risk. So, the quantum computation module20221 calculates exactly how much of each stock is required for theuser.

While a traditional computation module cannot solve the equations belowdue to high number of permutations of options, the quantum computingsystem 20214 may make an investment decision based on meeting desiredend use parameters.

Weight(ABC)+Weight(XYZ)+Weight(TUV)=1  (Equation 1)

0.1*Weight(ABC)+0.5*Weight(XYZ)+0.3*Weight(TUV)=0.2  (Equation 2)

In some embodiments, the quantum computing system 20214 or other systemsof the platform 20500 is configured to select transactions to build adesired position in a particular market. The quantum computing system20214 or other systems of the platform 20500 may build a desiredposition in the market by evaluating possible transactions and factoringconsequences of each transaction.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 utilize a pyramiding method of increasing margin byusing unrealized returns from successful trades. The pyramiding methodmay refer to only adding to positions that are turning a profit andshowing signals of continued strength. These signals could be continuedas asset prices reach to new highs or the asset prices retreat toprevious lows. The pyramiding method takes advantage of trends, addingto the user's position size with each wave of that trend. Further, thequantum-enabled pyramiding method is also beneficial in that risk (interms of maximum loss) does not have to increase by adding to aprofitable existing position. Original and previous additions will allshow profit before a new addition is made, which means that anypotential losses on newer positions are offset by earlier entries.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 are configured to generate a ranked list of assets.In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 may utilize a factor investing approach that involvestargeting quantifiable factors that can explain differences in assetreturns. In a long-only portfolio, a systematic factor investingstrategy will overweight assets that rank highly on a certain factor andunderweight assets that rank poorly on that factor. The factors explainexactly why the portfolio is positioned the way it is and what thedrivers of return are every time.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 may utilize a value investing approach for pickingstocks that appear to be trading for less than their intrinsic or bookvalue. In value investing, equity valuations may be quantified by theratio of a fundamental anchor—like book value, earnings, or cashflows—over price. In embodiments, the quantum computing system 20214 orother systems of the platform 20500 may be configured to perform avaluation of an asset or a set of assets.

In some embodiments, the quantum computing system 20214 or other systemsof the platform 20500 utilize a momentum investing approach for buyingassets that have had high returns over the past three to twelve monthsand selling those that have had poor returns over the same period. Inthe momentum investing approach, the assets that have recentlyoutperformed will tend to do better than assets that have recentlyunderperformed. In some embodiments, momentum is calculated over thelast 12-months price return of an asset.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 are configured to generate a ranked list ofcompanies. In some embodiments, in the building of trading strategies,the given ranking is based on confidence intervals of the performance ofa set of related or comparable assets and/or companies.

In embodiments, the platform 20500 or other systems of the platform20500 may apply rankings of a series of companies to enable deepercomparative basis than a simple selection of the top or bottom assets.In an example, the platform 20500 may be tasked with investing in tenassets based on the company ranking. Continuing the example, supposeasset X is among these ten assets and is predicted to have a rankingbetween second and sixth. In embodiments, the quantum computing system20214 or other systems of the platform 20500 leverage a ranking model tofind the optimal ranking. In some embodiments, the ranking model usesthe portfolio weights to maximize an objective function, even for theworst realization of the ranking within the uncertainty set.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 are configured to generate a ranked list of potentialtransactions. In embodiments, the quantum computing system 20214 orother systems of the platform 20500 apply ranked lists of potentialtransactions to rebalance a portfolio. The quantum computing system20214 or other systems of the platform 20500 may be configured toundertake the rebalancing with the goal of minimizing the explicit (e.g.commission) and implicit (e.g. bid/ask spread and impact) costsassociated with trading.

In some embodiments, trading costs and constraints may be explicitlyconsidered within portfolio construction. For example, a portfoliooptimization that seeks to maximize exposure to some alpha source mayincorporate explicit measures of transaction costs or constrain thenumber of trades that are allowed to occur at any given rebalance.

In some embodiments, the portfolio construction and trade optimizationoccur in a two-step process. For example, a portfolio optimization maytake place that creates the “ideal” portfolio, ignoring consideration oftrading constraints and costs. The quantum computing system 20214 orother systems of the platform 20500 may then undertake tradeoptimization as a second step, seeking to identify the trades that wouldmove the current portfolio “as close as possible” to the targetportfolio while minimizing costs or respecting trade constraints.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 are configured to optimize counterparty matching. Inembodiments, the quantum-based matching is based at least in part oncomplementary trading strategies of the counterparties. In someembodiments, a transaction may include many counterparties. Eachmarketplace of funds, goods, and services to complete a transaction maybe considered as a series of counterparties. For example, if a buyerpurchases a retail product online to be shipped to their home, the buyerand retailer are counterparties, as are the buyer and the deliveryservice. In embodiments, the management of counterparties in complexmulti-step processes can be optimized to enable the efficient transferof funds between parties. In market dealings with a counterparty, thereis an innate risk that one of the parties or entities involved will notfulfill their obligations. This is especially true for over-the-counter(OTC) transactions. Examples of OTC transaction risks include, but arenot limited to, a vendor not providing a good or service after a paymentis processed, that a buyer will not pay an obligation if the goods areprovided first, and that one party will back out of the deal before thetransaction is executed but after an initial agreement is reached. Inembodiments, the quantum computing system 20214 or other systems of theplatform 20500 are configured to identify areas of counterparty risk,and these areas of identified risk are then managed as part of theoverall quantum market orchestration.

Counterparties on a trade can be classified in several ways and mayprovide insights into how the market is likely to act based onpresence/orders/transactions and other similar-style traders. Inembodiments, examples of the counterparties include, but are not limitedto, retailers, market makers, liquidity traders, technical traders,momentum traders, and arbitragers.

Retailers may refer to ordinary individual investors or othernon-professional traders. They may be trading through an online brokerlike E-Trade or a voice broker. The quantum computing system 20214 orother systems of the platform 20500 may provide for automated traderswho act as counterparties in transactions.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 provide for automated market makers (AMMs) that areparticipants to provide liquidity to the market. In embodiments, theAMMs may have substantial market clout and will often be a substantialportion of the visible bids and offers displayed in the order books.Profits may be made by AMA/Is by providing liquidity and collectingElectronic Communication Network (ECN) rebates, as well as moving themarket for capital gains when circumstances dictate a profit may becapturable.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 include automated liquidity trading modules, whichmay refer to non-market makers who generally have very low fees andcapture daily profits by adding liquidity and capturing the ElectronicCommunication Network (ECN) credits. As with AMMs, automated liquiditytrading modules may also make capital gains by being filled on the bid(offer) and then posting orders on the offer (bid) at the inside priceor outside the current market price.

In some implementations, market orchestration system platform 20500includes quantum-enabled technical trader intelligent agents. Inembodiments, the quantum-enabled technical trader intelligent agents areconfigured to trade based on chart levels, whether from marketindicators, support, resistance, trendlines, or chart patterns.Quantum-enabled technical trader intelligent agents may be configured towatch marketplace charts for certain conditions to arise before steppinginto a position.

In embodiments, market orchestration system platform 20500 includesquantum-enabled momentum trader intelligent agents. In embodiments, thequantum-enabled momentum trader intelligent agents may be of differenttypes. Some quantum-enabled momentum trader intelligent agents may beconfigured to stay with a momentum stock for multiple days (even thoughthey only trade it intraday) while others will search for “stocks on themove,” continuously attempting to capture quick sharp movements instocks during news events, volume, or price spikes. Thesequantum-enabled momentum trader intelligent agents may be configured toexit when the movement is showing signs of slowing.

In embodiments, the market orchestration system platform 20500 includesquantum-enabled arbitrager intelligent agents. In some embodiments, thequantum-enabled arbitrager intelligent agents are configured to usemultiple assets, markets, and statistical tools to exploitinefficiencies in the market or across markets.

In embodiments, the quantum computing system 20214 or other systems ofthe platform 20500 is configured to optimize order matching. In someembodiments, quantum computing system 20214 includes a quantum ordermatching system. In embodiments, quantum order matching system is anelectronic system that matches buy and sell orders for a marketplaceusing quantum order matching algorithms. The quantum order matchingsystem executes orders from participants in the marketplace.

In embodiments, orders are entered by traders and executed by a centralsystem that belongs to the marketplace. The quantum order matchingalgorithm that is used to match orders may vary from system to systemand may use rules around best execution. Further, the quantum ordermatching system and order request system 20260 may be a part of a largerelectronic trading system, which may include a settlement system 20241and a central securities depository that is accessed by electronictrading platforms.

The quantum order matching algorithms may determine the efficiency andthe robustness of the quantum order matching system 20231. Inembodiments, marketplaces may be configured to support continuoustrading where orders are matched immediately and/or auction tradingwhere matching is done at fixed intervals. In some embodiments, thequantum order matching system 20231 functions in an auction state at themarket open when a number of orders have built up.

In some implementations, the quantum computing system 20214 or othersystems of the market orchestration system platform 20500 are configuredto perform opportunity discovery through the process of mining anddiscovery. Mining and discovery may involve traditional data miningusing predictive models and/or text-based data mining modules. Inembodiments, traditional data mining using the quantum computing system20214 or other systems of the market orchestration system platform 20500is applied to find opportunities for optimization of portfolios throughtrading activities.

Text mining may refer to the data analysis of natural language works(articles, books, etc.) using text as a form of data. It is often joinedwith data mining, the numeric analysis of data works (e.g., filings andreports), and referred to as “text and data mining” or, simply, “TDM.”

In some embodiments, TDM may be accomplished by applying quantumcomputational or Quantum TDM engines (QTDM) 20223 that allow computersto read and digest digital deep insights in the information far moreefficiently than a human being. QTDM engines 20223 may be configured tobreak down digital information into raw data and text, analyze it, anddetermine new connections. For example, QTDM engines 20223 may determinethat subtle shifts in weather patterns relate to a downturn in the priceof wheat. In embodiments, the quantum computing system 20214 thenapplies QTDM engines 20223 to mine news feeds, social media feeds,and/or discussion boards to predict movements of markets for everythingfrom government bonds to commodities.

In embodiments, the quantum computing system 20214 or other systems ofthe market orchestration system platform 20500 are configured toautomatically discover smart contract configuration opportunities.Automated discovery of smart contract configuration opportunities may bebased on published APIs to marketplaces and machine learning (e.g., byrobotic process automation (RPA) of stakeholder, asset, and transactiontypes.

In embodiments, the quantum computing system 20214 includes a quantumtrading engine 20225. In embodiments, smart contracts are provided bythe quantum trading engine 20225 and are executed by a computer networkthat uses consensus protocols to agree upon the sequence of actionsresulting from the smart contract's code. The result is a method bywhich parties can agree upon terms and trust that they will be executedautomatically, with reduced risk of error or manipulation.

In embodiments, quantum-established or other blockchain-based smartcontracts applications may include, but are not limited to, validatingloan eligibility and executing transfer pricing agreements betweensubsidiaries. In embodiments, quantum-established or otherblockchain-enabled smart contracts enable frequent transactionsoccurring among a network of parties, and manual or duplicative tasksare performed by counterparties for each transaction. Thequantum-established or other blockchain acts as a shared database toprovide a secure, single source of truth, and smart contracts automateapprovals, calculations, and other transacting activities that are proneto lag and error.

Smart contracts may use software code to automate tasks, and in someembodiments, this software code may include quantum code that enablesextremely sophisticated optimized results.

In embodiments, the quantum computing system 20214 or other system ofthe market orchestration system platform 20500 includes aquantum-enabled or other prospect targeting module that is configured toperform prospect targeting. In embodiments, the prospect targetingmodule identifies various strategies to find prospects appropriate tothe market participant needs. In embodiments, the prospect targetingmodule participates in online communities, enabling the identificationof new prospects through monitoring of sites such as Twitter™, LinkedIn™Reddit™, and the like.

In some embodiments, the prospect targeting module generates localhashtags and applies these hashtags to find prospects associated withthe specific topics of interest.

In embodiments, the prospect targeting module sponsors community events(such as digital events or physical events). In some embodiments, theprospect targeting module identifies specific online advertisements.These specific advertisements may include factors such as geographicspecificity, age range, job title, essential keywords, and the nature ofsocial engagement.

In embodiments, the quantum computing system 20214 or other system ofthe market orchestration system platform 20500 includes aquantum-enabled or other valuation module configured to performvaluation tasks. Valuation may be applied when trying to determine thefair value of an asset or security, which is determined by what a buyeris willing to pay a seller, assuming both parties enter the transactionwillingly. When an asset trades in a marketplace, buyers, and sellersdetermine the market value of the asset. The concept of intrinsic value,however, refers to the perceived value of an asset based on futureearnings or some other attribute unrelated to the market price of theasset. In embodiments, the valuation module is used to determine theintrinsic value of an asset. This intrinsic value may be an indicator ofthe over- or under-valuation of an asset.

In embodiments, the valuation module may leverage absolute valuationmodels and relative valuation models. In embodiments, the quantumabsolute valuation models may attempt to find the intrinsic or “true”value of an investment based only on fundamentals. Fundamentals mayrefer to dividends, cash flow, growth rates, and the like. Absolutevaluation models that may include dividend discount models, discountedcash flow models, residual income models, and asset-based models, andthe like. In embodiments, the relative valuation models operate bycomparing the asset in question to other similar assets. These methodsmay involve quantum or other calculations to determine relativeperformance based on quantitative input data, such as price to earningsratio and growth numbers to compare the asset to other assets of similartypes.

In embodiments, the quantum computing system 20214 or other systems ofthe market orchestration system platform 20500 may include aquantum-enabled or other risk identification module that is configuredto perform risk identification and/or mitigation. The steps that may betaken by the risk identification module may include, but are not limitedto, risk identification, impact assessment, and strategies development.In some embodiments, the risk identification module determines a risktype from a set of risk types. In embodiments, risks may include, butare not limited to, preventable, strategic, and external risks.Preventable risks may refer to risks that come from within and that canusually be managed on a rule-based level, such as employing operationalprocedures monitoring and employee and manager guidance and instruction.Strategy risks may refer to those risks that are taken on voluntarily toachieve greater rewards. External risks may refer to those risks thatoriginate outside and are not in the businesses' control (such asnatural disasters). External risks are not preventable or desirable. Inembodiments, the risk identification module can determine cost for anycategory of risk. The risk identification module may perform acalculation of current and potential impact on an overall risk profile.

In embodiments, the step of risk identification module may determine theprobability and significance of certain events. Furthermore, the riskidentification module may be configured to anticipate events.

In embodiments, the quantum computing system 20214 or other systems ofthe market orchestration system platform 20500 may be configured forsampling from risk-neutral probability measures for asset pricing. Inembodiments, a binomial pricing formula may be interpreted as adiscounted expected value. In risk-neutral pricing, the option value ata given node is a discounted expected payoff to the option calculatedusing risk-neutral probabilities and the discounting is done using therisk-free interest rate. The price of the option may be calculated byworking backward from the end of the binomial tree to the front. Inembodiments, the derived risk-neutral probabilities are calculated byquantum computing system 20214 or other systems of the marketorchestration system platform 20500, providing more precision in theoverall asset price calculation.

In embodiments, the quantum computing system 20214 or other systems ofthe market orchestration system platform 20500 are configured foroptimizing asset allocation. The quantum computing system 20214 or othersystems of the market orchestration system platform 20500 may beconfigured to optimize the type and nature of the investment based onthe requirements of the investor. For example, an investor requirementmay be saving for a new car in the next year. In the present example,the investor might invest her car savings fund in a very conservativemix of cash, certificates of deposit (CDs), and short-term bonds. In adifferent example, an investor requirement may be placing holdings inmuch longer-term positions or tax optimized investments if the investoris saving for retirement that may be decades away.

Asset-allocation mutual funds, also known as life-cycle, or target-datefunds, may provide investors with portfolio structures that address aninvestor's age, risk tolerance, and investment objectives with anappropriate apportionment of asset classes, which may be achievedthrough the application of quantum calculations, by artificialintelligence systems, or by other systems of the market orchestrationsystem platform 20500.

In some embodiments, the quantum computing system 20214 or other systemsof the market orchestration system platform 20500 may be configured forhash collision for proof of work in cryptocurrency mining. The value ofBitcoin comes from the difficulty of finding SHA-256 reversals orsimilar calculations, which gives it “proof of work”. Currently, it isbelieved that there is no efficient classical algorithm which can invertSHA-256. Hence, the only way is a brute force search, which classicallymeans trying different inputs until a satisfactory solution is found.The quantum computing system 20214 may be configured to solve reversalof SHA-256, thus breaking the “proof of work” requirement oncryptocurrency mining.

In embodiments, the quantum computing system 20214 is configured forquantum-driven Monte Carlo for derivative pricing. The Quantum MonteCarlo (QMC) valuation relies on risk-neutral valuation. In the QMCvaluation, the price of the option is its discounted expected value. Inembodiments, the QMC valuation technique includes creating a quantumstate combining all price paths for the underlying via simulation,resolving the QMC to calculate the optimum associated exercisevalue/path and discounting the payoffs to today.

In embodiments, the quantum computing system 20214 is configured forquantum-driven Monte Carlo for credit valuation adjustment. Counterpartycredit risk (CCR) may refer to the risk that a party to a derivativecontract may default before the expiration of the contract and fail tomake the required contractual payments. The Quantum Monte Carlocounterparty credit risk (CCR) estimation framework estimators may bedeveloped based on quantum applications of such as quantumimplementation of mean square error (MSE) reduction techniques

In embodiments, the quantum computing system 20214 is configured forimaginary-time propagation for multi-asset Black Scholes equation. TheBlack-Scholes equation may be interpreted from quantum mechanics as theimaginary time Schrödinger equation of a free particle. When deviationsof the quantum state of equilibrium are considered, related to marketimperfection, such as cost, data challenge, short-term volatility,discontinuities, or serial correlations, the classical non-arbitrageassumption of the Black-Scholes model is violated, implying anon-risk-free portfolio. An arbitrage environment is a necessarycondition to embedding the Black-Scholes option pricing model in a moregeneral quantum physics setting.

In some embodiments, the quantum computing system 20214 or other systemsof the platform 20500 are configured for accelerated sampling fromstochastic processes for risk analysis. In embodiments,quantum-simulated accelerated testing is initialized to hold acceleratedlife tests with constant-stress loadings, including accelerateddegradation tests and time-varying stress loadings. Thisquantum-accelerated testing results access product reliability andassists with the design of warranty policy.

In embodiments, the quantum computing system 20214 or other systems ofthe market orchestration system platform 20500 are configured for graphclustering analysis for anomaly and fraud detection. In embodiments, thequantum computing system 20214 or other systems of the marketorchestration system platform 20500 are configured for identifying afraudulent account application. In embodiments, identifying a fraudulentaccount application may include receiving a new account applicationcomprising a plurality of identity-related fields and linking theidentity-related fields associated with the new account application withidentity-related fields associated with a plurality of historicalaccount applications. In embodiments, this quantum-enabled frauddetection determines the likelihood that the new account application isfraudulent.

In some embodiments, the quantum computing system 20214 includes aquantum prediction module, which is enabled to accurately predict futuremarket trends. In addition, the quantum prediction module may beconfigured to generate forecast financial time series, especially forthe Foreign Marketplace (FX). Furthermore, the quantum prediction modulemay construct classical prediction engines to further predict the markettrends, reducing the need for ongoing quantum calculation costs, whichcan be substantial compared to traditional computers.

In embodiments, the quantum principal component analysis (QPCA)algorithm may process input vector data if the covariance matrix of thedata is efficiently obtainable as a density matrix, under specificassumptions about the vectors given in the quantum mechanical form. Itmay be assumed that the user has quantum access to the training vectordata in a quantum memory. Further, it may be assumed that each trainingvector is stored in the quantum memory in terms of its difference fromthe class means. These QPCA can then be applied to provide for dimensionreduction using the calculational benefits of a quantum method.

In embodiments, the quantum computing system 20214 or other systems ofthe market orchestration system platform 20500 are configured for graphclustering analysis for certified randomness for proof-of-stakeblockchains. Quantum cryptographic schemes may make use of quantummechanics in their designs, which enables such schemes to rely onpresumably unbreakable laws of physics for their security. The quantumcryptography schemes may be information-theoretically secure such thattheir security is not based on any non-fundamental assumptions. In thedesign of blockchain systems, information-theoretic security is notproven. Rather, classical blockchain technology typically relies onsecurity arguments that make assumptions about the limitations ofattackers' resources. In embodiments, blockchain and distributed ledgertechnologies have applications in market orchestration includingcryptocurrencies, insurance, and securities issuance, trading, andselling. Quantum cryptographic schemes may enable nontraditional marketsincluding, but not limited to, the music industry, decentralized IoT,anti-counterfeit solutions, internet applications, and decentralizedstorage.

In embodiments, the quantum computing system 20214 or other systems ofthe market orchestration system platform 20500 are configured fordetecting adversarial systems, such as adversarial neural networks,including adversarial convolutional neural networks. For example, thequantum computing system 20214 or other systems of the marketorchestration system platform 20500 may be configured to detect faketrading patterns.

In embodiments, the market orchestration system platform 20500 isconfigured to generate “market orchestration digital twins.” The termdigital twin may refer to a digital representation of a thing or set ofthings. A market orchestration digital twin may refer to any digitaltwin related to a market (including digital twins of marketplaces,assets, workflows, traders, marketplace hosts, brokers, serviceproviders, agents, and the like). Like other systems, services,applications, and components described herein, market orchestrationdigital twins may be used for a wide range of applications, includingparticipant-facing applications (including various types of usersdescribed herein) and applications that are for use by or for a host ofa marketplace. These may optionally include research and developmentapplications (including design of new features, components, andapplications for the marketplace or its participants, such as thosedescribed throughout this disclosure), analytic applications (such asfor providing insight relevant to trading activities, marketplaceoperations, and many other topics), simulations, AI-based monitoring,forecasting/prediction applications, and automation applications(including supervised, semi-supervised and fully autonomousapplications, such as involving robotic process automation), among manyothers. Market orchestration digital twins may include asset digitaltwins, company digital twins, marketplace digital twins, trader (e.g.,buyer and seller) digital twins, marketplace host digital twins, brokerdigital twins, intelligent agent digital twins, transaction workflowdigital twins, marketplace workflow/process digital twins, environmentdigital twins and/or the like, which are discussed in greater detailthroughout the disclosure.

In embodiments, digital twins may be visual digital twins and/ordata-based digital twins. A visual digital twin may refer to digitaltwin that is capable of being depicted in a display such as atraditional 2D display, a 3D display, an augmented reality display, or avirtual-reality display. A data-based digital twin may refer to a datastructure that contains a set of parameters that are parametrized torepresent a state of the thing or group of things. As used herein, theterm “depict” may refer to the visual display of a thing and/or adigital representation of the thing in a data structure (e.g., in adata-based digital twin). In embodiments, visual digital twins may alsobe data-based digital twins, and vice versa.

In some embodiments, a digital twin may be updated with real-time data,such that the digital twin reflects the state of the thing or set ofthings in real-time. For example, an asset digital twin of a home listedin a marketplace for real-estate may depict the physical structure ofthe home (e.g., walls, floors, ceilings, rooms, and the like), as wellas objects appearing in the environment (e.g., appliances, fixtures, andthe like). Furthermore, depending on the manner in which this digitaltwin is configured, the digital twin of the home may include things suchas piping, electrical wire, foundation, and the like. In someimplementations, the digital twin of the home may be updated with datareceived from sensors and devices (e.g., smart home sensors, othersensors deployed in or around the home, appliances or devices within thehome, wearable devices worn by residents of the home, and/or othersuitable data sources). In scenarios where the digital twin is of aprocess or workflow, the digital twin may depict the process orworkflow, such as in a graph, flow diagram, Gantt chart, sequence list,or other representation, which may include embodiments involvingdirected and/or acyclic flows and/or ones including cyclical flows, suchas involving loops, such feedback loops, iterative optimization, andmany others. For example, in the context of a marketplace workflow, adigital twin of the workflow may depict the status and/or outcomes ofdifferent stages in the workflow, the inputs to each stage, the outputsfrom each stage, the processing operations of each stage, and the like.In some implementations, the market orchestration system platform 20500may receive data from various sources (e.g., IIoT sensors, video, datafrom smart home devices, computing devices, or the like) and may updatea digital twin involving or related to the process to reflect thereceived data. For example, the market orchestration system platform20500 may receive data from sensors deployed in a shipping facility maybe used to update the digital twin of a delivery process for an assetpurchased from a marketplace to reflect the received data, such as totrigger a transactional term conditioned on whether an item has beenshipped.

In embodiments, the digital twin system 21126 includes a digital twindata optimization system for enabling data minimization modeling for thepurpose of determining minimum threshold data requirements for digitaltwin modeling. In embodiments, data sources, data types, and the likemay be scored and/or ranked by their determined relevance, cost, ease ofuse, simplicity, and the like for multiple instances and/or types ofdigital twins. For example, when simulating the orchestration of a newmarketplace, the data types thought to be of the highest value, lowestcost, least complex, and most readily obtained may be selected for use.In embodiments, artificial intelligence system of the intelligentservices system 20243 may be leveraged for data selection for use in adigital twin. In embodiments, new data types and/or sources may betested and scored and/or ranked by the digital twin data optimizationsystem.

In embodiments, the market orchestration system platform 20500 may beconfigured to perform simulations using and/or with respect to one ormore digital twins. In embodiments, digital twins may be configured tobehave in accordance with a set of constraints, such as laws of nature,laws of physics, mechanical properties, material properties, economicprincipals, chemical properties, and the like. In this way, the marketorchestration system platform 20500 may vary one or more parameters of adigital twin and may execute a simulation within the digital twin thatconforms with real-word conditions. In embodiments, the marketorchestration system platform 20500 allows users to perform simulationsin a marketplace entity (e.g., an asset or a marketplace). For example,a potential buyer considering whether or not to bid on an automobilepart listed in a marketplace may subject the digital twin of theautomobile part to various simulations prior to making the purchase.Continuing the example, the market orchestration system platform 20500may vary the conditions (e.g., different temperatures, humidity,motions, forces, and the like) of the environment of the automobilepart. In this way, the simulation may be run to help a user decidewhether to place a bid. Furthermore, in some embodiments, digital twinsmay be leveraged to perform simulations to predict future states of thething or group of things and/or modeling behaviors to extrapolate statesof the thing or group of things. For example, the market orchestrationsystem platform 20500 allows users to simulate performance of an assetor a set of assets under different economic conditions by varying theeconomic conditions (e.g., labor market conditions, economic confidence,and the like). In examples, the market orchestration system platform20500 may receive sensor readings from temperature sensors, humiditysensors, and fan speed sensors deployed throughout an environment wherethe environment is listed in a marketplace for physical storage fortemperature-sensitive materials. The market orchestration systemplatform 20500 may apply one or more thermodynamics equations to thereceived sensor readings and the dimensions of the environment to modelthe thermodynamic behavior of the environment to determine temperaturesin areas that do not have temperature sensors.

Digital twins can be helpful for visualizing the current state of asystem, running simulations on those systems, and modeling behaviors,amongst other uses. Depending on the configuration of the digital twin,however, it may not be useful for different users, as the configurationof the digital twin dictates the data that is depicted/visualized by thedigital twin. Thus, in some embodiments, the market orchestration systemplatform 20500 is configured to generate role-based digital twins.Role-based digital twins may refer to digital twins of one or moreaspects of a marketplace, where the one or more aspects and/or thegranularity of the data represented by the role-based digital twin aretailored to a particular role within the marketplace. In embodiments,the role-based digital twins include trader digital twins, marketplacehost digital twins, and broker digital twins.

In some of these embodiments, the market orchestration system platform20500 generates different types of market orchestration digital twinsfor users having different roles within the marketplace. In some ofthese embodiments, the respective configuration of each type of marketorchestration digital twin may be predefined with default digital twindata types and default granularities. In some embodiments, a user maydefine the types of data depicted in the different types of marketorchestration digital twins and/or the granularities of the differenttypes of market orchestration digital twins. Granularity may refer tothe level of detail at which a particular type of data or types of datais/are represented in a digital twin. For example, a marketplace hostdigital twin may depict information related to execution quality,percentage of orders price improved, net improvement per order,liquidity multiple, execution speed, effective spread over quotedspread, and the like, but may optionally omit depiction of an assetwatch list. In examples, the trader digital twin may depict accountbalance information, an asset watch list, and performance data for anasset listed in a marketplace. The foregoing examples are not intendedto limit the scope of the disclosure. Additional examples andconfigurations of different market orchestration digital twins aredescribed throughout the disclosure.

In some embodiments, market orchestration digital twins may allow a user(e.g., a trader, a marketplace host, a broker, or the like) to increasethe granularity of a particular state depicted in the digital twin (alsoreferred to “drilling down into” a state of the digital twin). Forexample, a trader digital twin may depict varying granularity snapshotsof an asset watch list. Continuing the example, a trader digital twinmay depict a stock symbol, market capitalization, and stock price in amarketplace for stocks. In embodiments, a user (e.g., a buyer in amarketplace) may opt to drill down into the asset watch list data via aclient application 20312 depicting the trader digital twin. For example,a trader in a marketplace for stocks may select a symbol associated withassets on the asset watch list. In response, the market orchestrationsystem platform 20500 may provide higher resolution watch list data forthe particular stocks, such as opening price, high price, low price,volume, P/E/ market cap, 52-week high price, 52-week low price, averagevolume, and the like. In embodiments, market orchestration digital twinsmay include visual indicators of different states of the marketplaceand/or marketplace entities. For example, a red icon may indicate awarning state, a yellow icon may indicate a neutral state, and a greenicon may indicate a satisfactory state. As another example, varyingcolors or other indicators may indicate varying volatility measures fora set of defined time periods, such as hourly, daily, weekly, monthly,quarterly, annually, or the like, including volatility of price, tradingvolume, trade size, and others. Volatility may or may not be desirablein the context of a user's strategy; for example, day traders may seekhigh-volatility asset classes, while fundamental investors may becautious about volatility (such as by using asset allocation acrossasset classes of varying volatility). Accordingly, indicators forvolatility measures (or other measures) may be (optionallyautomatically) configured based on an identified trading strategy orrole of a user, such as by showing more volatile asset classes in greenfor an interface for a day trader or other volatility-seeking strategyand showing them in yellow for an interface for a volatility-cautiousstrategy or role. In embodiments, the digital twin may depictstrategy-aware indicators (e.g., volatility) as display elements in adigital twin. In embodiments, the digital twin may depict differentcolored icons to differentiate a condition (e.g., current and/orforecasted condition) of a respective data item. For example, themarketplace host digital twin may depict a red icon with execution speeddata to indicate a warning state if execution speeds are slow. This mayinclude execution speed information for the user's trading system, forthe marketplace as a whole, or the like. In response, the marketplacehost digital twin may depict one or more different data streams relatingto the selected data item. In examples, a trader digital twin may depicta green icon with a trending asset. In yet other examples, a traderdigital twin may highlight and/or depict green icons with assetsrecommended for purchase and highlight and/or depict red icons withassets recommended for sale, wherein the recommendations may begenerated by machine learning and/or artificial intelligence, such astrained on outcomes and/or by supervised, semi-supervised, orunsupervised learning.

In some embodiments, the market orchestration system platform 20500supports rolled-up real-time reporting. In some of these embodiments,data from IoT systems, edge and network devices (such as located on orin various premises of business operation or other points of use,located in data centers that support marketplace or other businessoperations, located in telecommunications networks, and/or located inother locations), wearable devices, enterprise software systems, feeds,event streams, and/or other data sources of the various types describedherein or in the documents incorporated herein by reference may undergoone or more data fusion operations (at the platform and/or an edgedevice), and an AI-based intelligent agent 20234 may report results ofanalytics performed on the fused data and/or process the fused datatypes, such as for machine learning, decision support, automation,control operations, or other uses described herein. For instance, a setof sensors deployed on machines or equipment may report characteristicsof various components of the machines or equipment. Edge devices may beconfigured to fuse the sensor data from an environment (e.g., a factory)with other data collected with respect to the environment, whereby thefused data is fed to the digital twin. The market orchestration systemplatform 20500 may then update the digital twin with the fused data, andan AI system may analyze the digital twin and/or the fused data toidentify data items to report.

In embodiments, the market orchestration system platform 20500 isconfigured to provide a set of marketplace tools that allow forcommunication/negotiation between users (e.g., buyers, sellers, brokers,and the like). In some embodiments, the marketplace tools allow users tocommunicate with respect to and/or within one or more marketorchestration digital twins. In some embodiments, users cancommunicate/negotiate while viewing the same digital twin or multipledigital twins. In embodiments, a twin, or an interface thereto, mayshare underlying data while offering configurations that allow each userto view information relevant to the user's particular role,organization, type, category, or the like. For example, an appraiser maybe offered a view representing information relevant to an appraisal ofan item of property that is subject to a transaction (such as physicalstatus data, location data and historical price information forcomparable assets), while a buyer may be presented with additionalinformation, such as information setting proposed terms and conditionsfor a transaction (e.g., price, interest rate, timing, escrowrequirements, insurance requirements, or the like).

In embodiments, the marketplace tools include a video conferencingservice. In some embodiments, the video conferencing service allowsusers to participate in video conferences within a digital twin. Inembodiments, information from video conferences may be used to populatean order request. In embodiments, information from video conferences maybe used to populate a smart contract. For example, users may access anenvironment digital twin via a VR-head set, whereby the participants mayview the environment digital twin and see avatars of other participantswithin the “in-twin” video conference.

In embodiments, marketplace tools include chat and/or instant messagingservices. In embodiments, information from chats and/or instantmessaging services may be used to populate an order request. Inembodiments, information from chats and/or instant messaging servicesmay be used to populate a smart contract request.

In embodiments, users (e.g., a marketplace host) may define the types ofmarket orchestration digital twins that may be generated by the digitaltwin system 20208. In embodiments, the user may select different typesof digital twins that will be supported for the marketplace by themarket orchestration system platform 20500 via a GUI presented by themarketplace configuration system 20302. For example, the user may selectdifferent types of role-based digital twins from a menu of digital twintypes, wherein the different types of role-based digital twins mayinclude asset digital twins, marketplace host digital twins, traderdigital twins, company digital twins (e.g., companies associated withassets), agent digital twins, appraiser digital twins, assessor digitaltwins, advertiser digital twins, and/or broker digital twins. Inembodiments, trader digital twins may include buyer and/or sellerdigital twins. In some embodiments, each type of market orchestrationdigital twin has a predefined set of states that are depicted in therespective market orchestration digital twin and predefined granularitylevels for each state of the set of states. In some embodiments, the setof states that are depicted in the market orchestration digital twinand/or the granularity of each state may be customized (e.g., by theuser). In these embodiments, a user may define the different states thatare represented in each type of market orchestration digital twin and/orthe granularity for each of the states depicted in the digital twin. Forexample, a trader in a marketplace may wish to have more historicalmarket data depicted in the trader digital twin, such that thehistorical market data is displayed at a higher granularity. In thisexample, the trader digital twin may be configured to depict the desiredhistorical market data fields at a granularity level defined by a user(e.g., the historical market data may include historical contractchanges data, historical time and sales data, and the like). Inexamples, a marketplace host may wish to view marketplace performancedata at a lower granularity level. In this example, the marketplace hostdigital twin may be configured to show visual indicators that indicatewhether any of the states are at a critical condition, an exceptionalcondition, or a satisfactory condition. For instance, if execution speedis slow, the marketplace host digital twin may depict that themarketplace performance-state is at a critical level. In thisconfiguration, the marketplace host may select to drill down into themarketplace performance-state, where she may view the execution speed,percentage of orders price improved, net improvement per order,liquidity multiple, and the like.

In embodiments, a user may connect one or more data sources 20224 to themarket orchestration system platform 20500. Examples of data sources20224 that may be connected to the market orchestration system platform20500 may include, but are not limited to, a sensor system 20274 (e.g.,a set of IoT sensors), news sources 20278 (e.g., news websites or CNBCprogramming), the market data 20280 (e.g., level 1 and level 2 data),the fundamental data 20282 (e.g., asset performance data), referencedata 20284 (e.g., marketplace identifiers), historical data 20288 (e.g.,historical contract change data), third party data sources 20290,discussion forum data 20235, social network data 20298, regulatory data20294, and network/edge devices 20292. The data sources 20224 mayinclude additional or alternative data sources without departing fromthe scope of the disclosure. Once the user has defined the configurationof each respective market orchestration digital twin, where theconfiguration includes the selected states to be depicted and/or thegranularity of each state, the user may then define the data sources20224 that are fed into the respective market orchestration digitaltwin. In some embodiments, data from one or more of the data sources maybe fused and/or analyzed before being fed into a respective digitaltwin.

In some embodiments, the user may select other types of marketorchestration digital twins that are supported for the marketplace,including asset digital twins, environment digital twins, and/or processdigital twins. In some of these embodiments, the user may define thedata sources used to generate and/or update these digital twins. Inembodiments, the user may define any physical locations to berepresented as an environment digital twin. For example, the user maydefine trading floors, geofences, jurisdictions, manufacturingfacilities (e.g., factories), asset locations (e.g., shippingfacilities, warehouses, and the like), locations of business operation(e.g., office buildings), locations of consumer use, retail locations,and the like. Each may be given a location identifier and a name orother logical indicator. In embodiments, the marketplace configurationsystem 20302 may assign an identifier to each item and may associate thelocation of the item with the identifier. In embodiments, the user maydefine the types of objects that are included in an environment and/ormay be found within the environment. For example, the user may definethe types of machines (e.g., factory machines, robots, and the like)that are in the environment, the types of products that are made in,stored in, sold from, and/or received in the environment, the types ofsensors/sensor kits that are used to monitor the environment, the typesof networking or edge devices that are used in the environment, and thelike.

In embodiments, the digital twin system 20208 is configured to generate,update, and serve market orchestration digital twins of a marketplace20226. In some embodiments, the digital twin system 20208 is configuredto generate and serve role-based digital twins on behalf of amarketplace and may serve the role-based digital twins to themarketplace participant user device 20218 (e.g., a mobile device, atablet, a personal computer, a laptop, or the like). As discussed,during the configuration phase, a user may define the different types ofdata and the corresponding data sources and data sets that are used togenerate and maintain each respective type among the different types ofmarket orchestration digital twins. Initially, the digital twin system20208 may configure the data structures that support each type of marketorchestration digital twin, including any underlying databases or otherdata sources (e.g., SQL databases, distributed databases, graphdatabases, relational databases, object databases, blockchain-baseddatabases, and the like) that store data that is ingested by therespective market orchestration digital twins. Once the data structuresthat support a digital twin are configured, the digital twin system20208 receives data from one or more data sources 20224. In embodiments,the digital twin system 20208 may structure and/or store the receiveddata in one or more databases. When a specific digital twin is requested(e.g., by a user via a client application 20312 or by a softwarecomponent of the market orchestration system platform 20500), thedigital twin system may determine the views that are represented in therequested digital twin and may generate the requested digital twin basedon data from the configured databases and/or real-time data received viaan API. The digital twin system 20208 may serve the requested digitaltwin to the requestor (e.g., the client application 20312 or a backendsoftware component of the market orchestration system platform 20500).After a market orchestration digital twin is served, some marketorchestration digital twins may be subsequently updated with real-timedata received via the API system 20238.

In embodiments, the digital twin system 20208 may be further configuredto perform simulations and modeling with respect to the marketorchestration digital twins. In embodiments, the digital twin system20208 is configured to run data simulations and/or environmentsimulations using a digital twin. For example, a user may, via a clientdevice, instruct the digital twin system 20208 to perform a simulationwith respect to one or more states depicted in a digital twin. Thedigital twin system 20208 may run the simulation on one or more itemsrepresented in the digital twin and may depict the results of thesimulation in the digital twin. In this example, the digital twin mayneed to simulate at least some of the data used to run the simulation ofthe environment, so that there is reliable data when performing therequested environment simulation. The digital twin system 20208 isdiscussed in greater detail throughout the disclosure.

In embodiments, the exchange suite 20204 provides a set of variousmarketplace tools that may be leveraged by various users of amarketplace. The marketplace tools may include “in twin” collaborationtools (e.g., “in twin” video conferencing tools, “in-twin” chatservices, and the like), an “in twin” strategies tool, an “in twin”trading practice tool, an “in twin” news tool, an “in twin” screenertool, an “in twin” market monitoring tool, an “in twin” entity profiletool, an “in twin” account management tool, an “in twin” charting tool,an “in twin” order request tool, and an “in twin” smart contract tool.In embodiments, an “in-twin” collaboration tool allows multiple users toview and collaborate within a digital twin. For example, multiple usersmay be granted access to view an asset digital twin representing atractor available for lease in a marketplace via the in-twincollaboration tool. Once viewing the tractor digital twin, the users maythen change one or more features of the tractor depicted in the tractordigital twin and/or may instruct the digital twin system to perform asimulation. In this example, the results of the simulation may bepresented to the users in the digital twin. If the buyer is satisfiedwith the results of the simulation, he or she may generate a smartcontract to lease the tractor via the “in twin” smart contract requesttool. The “in twin” smart contract request tool may enable a user (e.g.,the buyer) to define tractor leasing terms, which may include lease type(e.g., capital lease or operating lease), lease duration, financialterms, payment due to the lessor, market value of the equipment, taxresponsibility, and cancellation provisions. Users may collaborate inadditional manners with respect to a digital twin, as will be discussedthroughout the disclosure. In some embodiments, the exchange suite 20204interfaces with third-party applications, whereby data may be importedto and/or from the third-party application. For example, a first user(e.g., the buyer in a marketplace) may request certain information(e.g., additional photos or videos of an asset listed in themarketplace, sensor information (such as from one or more scanningsystems), or the like) from a second user (e.g., the seller in themarketplace) via the market orchestration digital twin configured forthe first user (e.g., the trader digital twin). In response, the seconduser may upload/export the requested data to the digital twin system20208, which may then update the asset information in the trader digitaltwin configured for the first user. Additional examples and descriptionsof the exchange suite 20204 and underlying collaboration tools arediscussed throughout the disclosure.

In embodiments, the market orchestration system platform 20500 supports“in-twin” marketplaces. In embodiments, an in-twin marketplace may beaccessible via visual market orchestration digital twins (e.g.,marketplace digital twins, asset digital twins, trader digital twins,broker digital twins, and company digital twins). In embodiments, anin-twin marketplace may be accessible via visual digital twins ofthird-party organizations. In these embodiments, the visual digitaltwins may access the market orchestration system platform 20500 via anAPI and may allow users that are viewing the respective visual digitaltwins to participate in one or more marketplace transactions. Inembodiments, users may issue a purchase offer for assets and/or servicesvia a third-party digital twin (e.g., request to purchase an asset orservice), purchase assets and/or services via the third-party digitaltwin (e.g., accepting an offer made by another party), view availabletransactions via the third-party digital twin, negotiate via thethird-party digital twin, set proposed terms and conditions for atransaction (optionally including by smart contract configuration) inthe digital twin, execute a transaction (such as by executing acceptanceof a transaction, including one configured by a smart contract) in thedigital twin, search for a transaction offer in the digital twin, placea transaction offer in the digital twin, search for a counterparty inthe digital twin, search for an asset, asset type, asset class, or thelike in the digital twin, view account information in the digital twin,and/or the like. In embodiments, a user's ability to view specific typesof data within a digital twin and/or to engage in transactions on behalfof an organization is governed by the level of clearance of the user. Inembodiments, a clearance of a user may include data access rights (e.g.,whether the user can view detailed asset data of third-party assets thatare involved in a marketplace and granted permissions (e.g., permissionsto order items or services)). For example, with respect to a digitaltwin of a marketplace, an employee/user (e.g., manager) with sufficientclearance may have data access rights to view particular types of data,including the available inventory assets that may be used intransactions (such as or trades or as collateral) and to view thestatuses of various assets. In this example, the manager may havepermissions to undertake transactions for a defined subset of assets butcannot exceed that subset without authorization of a higher-rankingexecutive. In examples, with respect to a digital twin of a workflow, anemployee/user (e.g., manager) may have access rights to view dataobtained from entities involved in the workflow. The foregoing areexamples of clearance levels of different types of employees definedwith respect to specific types of digital twins. As can be appreciated,different clearance levels may be granted to different users dependingon the role of the user, the data types that are available in aparticular type of digital twin, and/or the types of marketplaces thatare accessible via the digital twin. Furthermore, in some embodiments,some marketplaces are accessible via digital twins that do not requireclearances or permissions. In these embodiments, any user can access thesame types of data with a particular digital twin and engage in any typeof transaction that is supported in the digital twin. For example, in adigital twin of a shopping mall, users (e.g., customers) can examine allthe products within the shopping mall and can engage in transactions forthose products.

In some embodiments, the market orchestration system platform 20500includes an SDK where digital twin platforms can enable developers toincorporate specific marketplaces into a respective type of digitaltwin. In some of these embodiments, a digital twin (vis-à-vis theapplication presenting the digital twin) may be configured to access oneor more features of one or more marketplaces by defining themarketplace(s) (e.g., via a URL or other mechanism) that are accessiblefrom a respective view of the digital twin and defining the one or morefeatures that are available when viewing the respective view the digitaltwin. In some embodiments, the digital twin may request marketplace datafrom the market orchestration system platform 20500, whereby the requestmay include parameters that the market orchestration system platform20500 uses to identify the most relevant marketplace data, such as atype of data being presented in the digital twin and parameters thatprovide additional insight on which transactions to serve to the digitaltwin (e.g., product specifications, marketplace specifications, allowedand/or disallowed transaction partners, certification requirements,and/or the like). In response, the market orchestration system platform20500 may identify relevant marketplace data (e.g., offers to sellrelevant assets or services and/or providers of relevant assets orservices that may receive offers to buy their respective assets orservices) and may serve the relevant marketplace data to the digitaltwin. The digital twin may receive the marketplace data and may presentthe marketplace data in the digital twin (e.g., in proximity to thecorresponding portion of the digital twin). The user may then initiatetransactions via the marketplace using the marketplace data. Forexample, the user may initiate a purchase of an asset or service, mayprovide an offer to purchase an asset or service, may begin negotiationsfor an asset or service, or the like. Furthermore, as digital twintechnology enables the execution of complex simulations, users may runsimulations corresponding to real world environments and processes of anorganization. In this way, a user may view predicted/simulated futurestates of the digital twin, which may be used to drive decisions withrespect to a transaction. For example, in running simulations ofdifferent purchasing strategies, a user may view different simulatedoutcomes for different purchasing strategies (e.g., simulated outcomesfrom different combinations and sequences of purchases of tranches ofvarious sizes) associated with an organization's purchasing processes(e.g., multi-market purchasing). In response to each differentstrategies, the digital twin may obtain and present marketplace datacorresponding to each respective strategy, including vendors thatprovide goods or services that fulfill at least a portion of thestrategy and, if available, offers from the vendors to provide goods orservices (which may include pricing and additional data, such astimelines, certifications, licenses, and/or the like). In the absence ofoffers from a service provider, the user may be provided an interface torequest a quote or to provide an offer to the service provider for thegoods or services (as well as any requirements, such as timelines,certifications, licenses, and/or the like). In this way, the user mayview the outcomes of the different strategies and then may initiatetransactions to execute a selected strategy from the digital twin of thepurchasing process.

In a non-limiting example of an in-twin marketplace, a digital twin of amanufacturing factory (or “factory twin”) may depict, inter alia, thereal-time inventory levels of all the parts used to manufacture goods,including raw materials (sheet metal, paints, or the like), singlecomponent parts (e.g., screws, springs, belts, chains, tires, or thelike), and/or preassembled parts (e.g., engines/electric motors, struts,shocks, axles, infotainment systems, or the like) that are manufacturedat different locations and/or purchased from third-parties and shippedto the factory. In this example, the digital twin may be configured toaccess a marketplace for ordering parts via an API of the marketorchestration system platform 20500, where suppliers can offer to sellrespective parts and/or receive offers to sell respective parts. In thisexample, the digital twin may be configured to provide a request to aspecified marketplace (e.g., a part-supplies marketplace powered by themarket orchestration system platform 20500) that indicatesspecifications for the parts (e.g., product type, product identifier,product dimensions, material types, required certifications, approvedvendors, a number of units needed, and/or other suitablespecifications), and the marketplace (e.g., via the market orchestrationsystem platform 20500) may return transaction options for the parts(e.g., parts currently for sale by one or more different suppliersand/or different suppliers that produce the respective parts). Inembodiments, a transaction option with respect to a respective suppliermay indicate various attributes of the transaction option, such as adescription of the parts made by the respective supplier, the amount ofavailable parts from the respective supplier, the estimated shippingtime of the parts from the respective supplier, a rating of thesupplier, a price (e.g., total price and/or price-per-unit) for the partfrom the supplier (if an offer to sell), and/or other suitableattributes. In this way, a user with sufficient clearance to viewexisting inventory of a particular part and to order more inventory canview the existing inventory levels for the particular part and, if theinventory levels are low, may view transaction options for theparticular part. The user may then initiate a transaction by selectingone or more of the transaction options. For example, the user may selectan offer by a seller to sell a defined number of units at a predefinedprice or may generate an offer to buy a set number of units at apredefined price. It is noted that the offers to sell or buy may includeadditional information, such as proposed delivery dates, delivery types,product specifications, indemnifications, warranties, disclaimers, orother suitable information. Continuing this example, the user mayleverage the digital twin to perform a simulation of the manufacturingprocess to determine when certain parts will likely need to bereplenished given the factories' throughput, projected sales, predicteddowntimes, and the like. In this way, the user can assess differenttransaction options to find the best available transaction given when acertain shipment of parts needs to be delivered by.

In embodiments, the statuses of individual pieces of equipment or otherassets may be determined from sensor data derived from a set of sensorsthat are part of, affixed to, and/or proximate to the piece of equipmentor asset. Furthermore, in some embodiments, the status of the equipmentmay be derived by running simulations. In embodiments, the status of theequipment may indicate to the viewer/user whether a piece of equipmentcurrently requires service, is likely to require service, or is inworking condition. In embodiments, the digital twin may be configured toautomatically request transaction options from the market orchestrationsystem platform 20500 in response to a determination that a piece ofequipment requires service or may require service. In embodiments, atwin may generate a request that indicates information to obtain atransaction request, such as the location of the equipment or otherasset, the type of equipment, the type of issue that needs to beresolved, and the like. In response, the market orchestration systemplatform 20500 identifies transaction requests that match or bestcorrespond to the information provided in the request. For example, themarket orchestration system platform 20500 may return transactionoptions from services/technicians that specialize in the type ofmachinery and/or type of issue. In this example, the twin may presentthe transaction options to the user in relation to the piece ofequipment, whereby the user can initiate a transaction from the factorytwin.

In embodiments, a marketplace orchestration digital twin may interactwith a logistics digital twin. In one example, a logistics twin maypresent an option to sublease logistics space, such as warehouse space.In this example, the logistics twin may issue a request to the marketorchestration system platform 20500 to generate an offer to sublease thewarehouse space over a period of time via a specified marketplace. Inresponse, the market orchestration system platform 20500 generates theoffer and posts the offer on the specified marketplace. In a similarexample, the device manufacturer may have shipped an additional tencontainers that were presold prior to shipping. During transit, thepurchaser reneges on the deal, thereby requiring temporary storagespace. In this example, the marketplace digital twin may, based oninformation from a logistics twin or other logistics system, provide awarning to the user that an unsold shipment of ten containers iscurrently in transit to the United States without a storage plan inplace. The twin may further request transaction options from the marketorchestration system platform 20500, such as for temporary storagespace, revision of delivery terms and conditions, modification ofinsurance, or the like, whereby the request may indicate informationrelevant to the same. In response, the market orchestration systemplatform 20500 may identify a set of transaction options for temporarywarehouse space near the port of entry and may provide the transactionoptions to the twin. In response, the twin may present options to theuser via the twin, whereby the user may select one or more of thetransaction options. In this way, the user may resolve the issue inreal-time, such as to ensure that the shipment of devices is stored uponarrival in a cost-effective manner.

In embodiments, the market orchestration system platform 20500 supportsin-twin smart contracts. In-twin smart contracts may refer to smartcontracts that can be accessed and committed to via a digital twin, thatshare data structures with a digital twin, that can be parameterized bydata of a digital twin system, that can be presented and/or configuredwithin a digital twin, that are integrated with workflows of a digitaltwin, or the like. In these embodiments, transaction options may bepresented to a user via a digital twin, where one or more of thetransaction options are associated with a respective smart contract. Inthese embodiments, the user may commit to a transaction via the digitaltwin. For example, the user may select a user interface element withinthe digital twin that commits the user to the transaction. In responseto the user selection, the market orchestration system platform 20500may commit the user to the smart contract. In some embodiments, themarket orchestration system platform 20500 may commit the user to asmart contract by parameterizing the smart contract with informationobtained from the user. For instance, the market orchestration systemplatform 20500 may provide an identifier of the party, an amount/type ofcurrency in the transaction, and any other required information (e.g., alocation for a delivery or service to be performed, a deliverydate/contract expiration date/completion date, a start date, and/or thelike).

In embodiments, the market orchestration system platform 20500 trainsand deploys intelligent agents on behalf of marketplace users. Inembodiments, an intelligent agent is an AI-based software system, suchemploying robotic process automation, such as in the form of a bot, thatperforms tasks on behalf of and/or suggests actions to a respective userhaving a defined marketplace role. In embodiments, the intelligent agentmay be trained by the market orchestration system platform 20500 basedon interactions of the user with a client application 20312, such asactions taken by a user with respect to a market orchestration digitaltwin, interactions with sensor data or other data collected by themarket orchestration system platform 20500, interactions with one ormore software systems that performs or enables a marketplace relatedtask (such as a trading system, an analytic system, a pricing system, asmart contract configuration system, a template-based contractingsystem, a payments system, an ordering system, an e-commerce system, acryptocurrency system, a wallet, a register or other point-of-salesystem, a fulfillment system, and many others), interactions withhardware or physical systems, and the like. Training may be unsupervisedtraining (such as based on outcome data using a wide variety of feedbackmetrics, such as outcome data showing profitability of tradingactivities, purchasing activities, lending activities, sellingactivities, and many others), supervised training, or semi-supervisedtraining. In embodiments, an intelligent agent may be a trader agenttrained for trader roles and workflows, such as identifying favorabletrading strategies or trade opportunities (such as arbitrageopportunities), placing bids, accepting bids, configuring and/ornegotiating a contract (such as a smart contract), setting trade sizes,and setting orders (including limit orders, call orders, positioncovering orders, hedge-based orders and many others), including any ofthe roles and workflows described herein or in the documentsincorporated herein by reference. In embodiments, an intelligent agentmay be a buyer agent trained for buyer roles and workflows, such asidentifying buying opportunities within an asset class, determining aset of orders required to satisfy a strategic rule or criterion (such asan asset allocation criterion), negotiating terms and conditions of acontract (such as a smart contract, such as relating to price, quantity,timing, delivery terms, insurance coverage, warranties, and manyothers), finding and/or executing undervalued items, bargains, or thelike, and many others, including any of the roles and workflowsdescribed herein or in the documents incorporated herein by reference.In embodiments, an intelligent agent may be a seller agent trained forseller roles and workflows, such as identifying prospective buyers,configuring contract terms and conditions (such as for smart contracts,such as auction rules, prices, offer size, offer timing, offer volume,promotions, incentives, discounts (e.g., based on volume or timing),delivery terms, fulfillment terms, maintenance and update terms,warranty and liability terms, insurance coverage, and many others),including any of the roles and workflows described herein or in thedocuments incorporated herein by reference. In embodiments, anintelligent agent may be a broker agent trained for broker roles, suchas identifying sellers, identifying buyers, matching buyers to sellers,negotiating commissions and other contractual terms and conditions (suchas in smart contracts), identifying service providers, and many others,including any of the broker roles and workflows described herein or inthe documents incorporated herein by reference. In embodiments, anintelligent agent may be a marketplace host agent trained formarketplace host roles, such as setting marketplace participation rules,setting rules for configuration of transactions (such as auction rules,bid/ask rules, order types, asset types, and many others), configuringand/or negotiating contracts for marketplace participation (such assmart contracts, such as contracts governing permitted tradingactivities, permitted participants, and others), setting exchange rates,setting and/or configuring media of exchange (such as fiat orcryptocurrencies, tokens, points, and others), and many others,including any of the host roles and workflows described herein or in thedocuments incorporated herein by reference. In embodiments, the marketorchestration system platform 20500 trains intelligent agents 20234 forother roles within a marketplace, such as a valuation role, an analystrole, a delivery role, an asset inspection role, and the like.

In embodiments, the market orchestration system platform 20500 trainsintelligent agents 20234 based on training data that includes actionstaken by users and features relating to the circumstances surroundingthe action (e.g., the type of action taken, the scenario that promptedthe action, and the like). In embodiments, the market orchestrationsystem platform 20500 receives telemetry data from a client application20312 associated with a particular user and learns the workflowsperformed by the particular user based on the telemetry data and thesurrounding circumstances. For example, the user may be a buyer in amarketplace that is presented an asset digital twin. Among the actionsof the buyer may be to run simulations on asset digital twins whereinthe asset digital twins represent assets for sale in the marketplace.The states depicted in the asset digital twin may include the conditionof the asset digital twin as a result of one or more simulations. Inthis example, the buyer may buy the asset via the asset digital twinwhen the asset digital twin is determined to be in a first condition(e.g., a good condition) as a result of one or more simulations and maydecline to purchase the asset and search for other assets for sale whenthe asset digital twin is determined to be in a second condition (e.g.,a critical condition) as the result of one or more simulations. Theintelligent agent may be trained to identify the buyer's tendenciesbased on the buyer's previous interaction with the asset digital twin.Once trained, the intelligent agent may automatically buy assets forsale when a particular asset's digital twin is determined to be in thefirst condition as the result of one or more simulations and mayautomatically decline to buy the asset and search for other assets ifthe asset digital twin is in the second condition as the result of oneor more simulations. In embodiments, simulations may be based uponand/or incorporate behavioral models that predict behavior of assets(such as physical models that predict physical conditions (such as basedon physical, chemical and biological principles), economic models thatpredict economic behavior (such as models that predict behavior ofpurchasers, sellers, prices, trading patterns, or the like), humanbehavioral models (such as psychological models, demographic models,population models, sociological models, game-theoretic models, and manyothers)), and many others, including any of the models described hereinor in the documents incorporated herein by reference and includinghybrids and combinations of the foregoing. As one example among manypossible models, in a marketplace for wine (or other asset that canimprove or deteriorate over time), a simulation may include a physicalmodel that uses sensor data from a storage environment for a unit, achemical model that predicts the effects of the passage of time underthe sensed storage conditions, and an economic model that predicts thevalue of a unit of a given level of quality based on historical pricingpatterns for similar goods. The results may yield an expected value ofthe asset, as well as a simulation of the price of the asset atdifferent points of time, which an intelligent agent may use asreference information in comparison to a current price and/or apredicted future price, such as to determine automatically (aftertraining upon a set of interactions of users with similar comparisons)whether to hold, buy, or sell. While reference is made to an intelligentagent being trained for a particular user, it is understood that anintelligent agent may be trained using the actions of one or moredifferent users and may be used in connection with users that were notinvolved in training the intelligent agent. Further discussion ofintelligent agents is provided throughout the disclosure.

In embodiments, the intelligent agent system 20210 trains intelligentagents 20234 that perform/recommend actions on behalf of a user. Anintelligent agent may be a software module that implements and/orleverages artificial intelligence services to perform/recommend actionson behalf of or in lieu of a user. In embodiments, an intelligent agentmay use, link to, integrate with, and/or include one or moremachine-learned systems or models (e.g., neural networks, predictionmodels, classification models, Bayesian models, Gaussian models,decision trees, random forests, and the like, including any describedherein or incorporated herein by reference) that performmachine-learning tasks in connection with a defined role. Additionallyor alternatively, an intelligent agent may be configured with artificialintelligence rules that determine actions in connection with a definedrole. The artificial intelligence rules may be programmed by a user ormay be generated by the intelligent agent system 20210. An intelligentagent may be executed at the marketplace participant user device 20218and/or may be executed by the market orchestration system platform20500. In the latter embodiments, the intelligent agent may be accessedas a service (e.g., via an API). In embodiments, where an intelligentagent is at least partially executed at a client device, the marketorchestration system platform 20500 may train an intelligent agent andmay serve the trained intelligent agent to a client application 20312.In embodiments, an intelligent agent may be implemented as a container(e.g., a Docker container) that may execute at the client device 20340or at the market orchestration system platform 20500. In embodiments,the intelligent agent is further configured to collect and report datato the intelligent agent system 20210, which the intelligent agentsystem 20210 uses to train/reinforce/reconfigure the intelligent agent.In embodiments, the intelligent agent is integrated into or with amarketplace orchestration digital twin system, such as involving ashared set of data resources, a shared set of computational resources, ashared set of artificial intelligence resources, a shared data schema, ashared user interface, a shared set of workflows, a shared set ofapplications or services, or the like. In embodiments, integration iswithin a shared microservices architecture, where intelligent agentservices and digital twin services are managed within a commonmicroservices framework.

In some embodiments, the intelligent agent system 20210 (working inconnection with the intelligent services system 20243) may trainintelligent agents 20234 (e.g., trader agents, buyer agents, selleragents, broker agents, marketplace host agents, regulatory agents, andother intelligent agents) using robotic process automation techniques toperform one or more executive actions on behalf of respective agents. Insome of these embodiments, a client application 20312 may execute on themarketplace participant user device 20218 (e.g., a user device, such asa tablet, a VR headset, a mobile device, or a laptop, an embeddeddevice, or the like) associated with a user (e.g., a buyer, a seller, abroker, a role-based expert, a marketplace host, or any other suitableaffiliate). In embodiments, the client application 20312 may record theinteractions of a user with the client application 20312 and may reportthe interactions to the intelligent agent system 20210. In theseembodiments, the client application 20312 may further record and reportfeatures relating to the interaction, such as any stimuli or sets ofstimuli that were presented to the user, what the user was viewing atthe time of the interaction, the type of interaction, the role of theuser, the role of the individual that requested the interaction, and thelike. The intelligent agent system 20210 may receive the interactiondata and related features and may train an intelligent agent basedthereon. In embodiments, the interactions may be interactions by theuser with a market orchestration digital twin (e.g., an asset digitaltwin, a trader digital twin, a broker digital twin, a marketplacedigital twin, an environment digital twin, a process digital twin, andthe like). In embodiments, the interactions may be interactions by theuser with sensor data (e.g., vibration data, temperature data, pressuredata, humidity data, radiation data, electromagnetic radiation data,motion data, and/or the like) and/or data streams collected formphysical entities (e.g., machinery, a building, a shipping container, orthe like). For example, a user may be presented with sensor data from aparticular piece of equipment and, in response, may determine that asmart contract request action be taken with respect to the piece ofequipment. In this example, the intelligent agent may be trained on theconditions that cause the user to generate a smart contract to sell anasset as well as instances where the user did not generate a contract tosell an asset. In this example, the intelligent agent may learn thecircumstances in which a smart contract request action is taken. Inembodiments, the intelligent agent system 20210 may train intelligentagents based on user interactions with other marketplace entities (suchas network entities and computation entities). For example, theintelligent agent system 20210 may train an intelligent agent to learnthe manner by which a trader identifies and engages with a counterparty.In this example, the intelligent agent may be trained to learn the stepsundertaken by the trader to identify a counterparty, engage with thecounterparty, and any actions undertaken by the trader to pursue atransaction with a counterparty.

In embodiments, an intelligent agent may be implemented as a robot thatperforms asset inspection actions, asset retrieval actions, paymentactions, asset delivery actions, asset servicing actions, asset testingactions, asset valuation actions, asset testing actions, and the like.

In embodiments, the types of actions that an intelligent agent may betrained to perform/recommend include: selection of an asset, pricing ofan asset, listing an asset in a marketplace, uploading informationrelated to an asset, identifying counterparties, selectingcounterparties, identifying opportunities, selecting opportunities,identifying marketplaces, digitally inspecting an asset, physicallyinspecting an asset, physically delivering an asset, physicallyretrieving an asset, configuring a marketplace, configuring a digitaltwin, placing an order request, generating a smart contract, ordermatching, selection of a strategy, selection of a task, setting of aparameter, selection of an object, selection of a workflow, triggeringof a workflow, ordering of a product, ordering of a process, ordering ofa workflow, cessation of a workflow, selection of a data set, selectionof a design choice, creation of a set of design choices, identificationof a problem, selection of a human resource, providing an instruction toa human resource, amongst other possible types of actions. Inembodiments, an intelligent agent may be trained to perform other typesof tasks, such as: reporting on an asset, reporting on a counterparty,reporting on a trader, reporting on a status, reporting on an event,reporting on a context, reporting on a condition, reporting on atransaction, determining a model, configuring a model, populating amodel, designing a system, engineering a product, maintaining a system,maintaining a device, maintaining a process, maintaining a network,maintaining a computational resource, maintaining equipment, maintaininghardware, repairing a system, repairing a device, repairing a network,repairing a computational resource, repairing equipment, repairinghardware, assembling a system, assembling a device, assembling aprocess, assembling a network, assembling a computational resource,assembling equipment, assembling hardware, setting a price, physicallysecuring a system, physically securing a device, physically securing aprocess, physically securing a network, physically securing acomputational resource, physically securing equipment, physicallysecuring hardware, cyber-securing a system, cyber-securing a device,cyber-securing a process, cyber-securing a network, cyber-securing acomputational resource, cyber-securing equipment, cyber-securinghardware, detecting a threat, detecting a fault, tuning a system, tuninga device, tuning a process, tuning a network, tuning a computationalresource, tuning equipment, tuning hardware, optimizing a system,optimizing a device, optimizing a process, optimizing a network,optimizing a computational resource, optimizing equipment, optimizinghardware, monitoring a system, monitoring a device, monitoring aprocess, monitoring a network, monitoring a computational resource,monitoring equipment, monitoring hardware, configuring a system,configuring a device, configuring a process, configuring a network,configuring a computational resource, configuring equipment, configuringhardware, monitoring technology, replications and partitioning of data,index creation on underlying databases, alteration of runtimeparameters, allocation of additional CPU, memory, and disk in virtualenvironments and physical environments, allocation of additional marketorchestration engines, clustering of environments, distribution ofenvironments, coordination of environments between physical locations,monitoring of the dark web, management of regulatory interfaces,management and extension of third party interfaces, geographic setup oflocal markets, management of remote administration tooling, building ofserverless components, alternative front end trading tooling (embeddedclients, alternative platforms, SMS trading tools, phone trading tools),and the like.

As discussed, an intelligent agent is configured to determine an actionand may output the action to a client application 20312. Examples of anoutput of an intelligent agent may include a recommendation, aclassification, a prediction, a control instruction, an input selection,a protocol selection, a communication, an alert, a target selection fora communication, a data storage selection, a computational selection, aconfiguration, an event detection, a forecast, and the like.Furthermore, in some embodiments, the intelligent agent system 20210 maytrain intelligent agents 20234 to provide training and/or guidancerather in addition to or in lieu of outputting an action. In theseembodiments, the training and/or guidance may be specific for aparticular individual or role or may be used for other individuals.

In embodiments, the intelligent agent system 20210 is configured toprovide benefits to experts that participate in the training ofintelligent agents 20234. In some embodiments, the benefit is a rewardthat is provided based on the outcomes stemming from the user of anintelligent agent trained by the expert user. In some embodiments, thebenefit is a reward that is provided based on the productivity of theintelligent agent. In some embodiments, the benefit is a reward that isprovided based on a measure of expertise of the intelligent agent. Insome embodiments, the benefit is a share of the revenue or profitgenerated by the work produced by the intelligent agent. In someembodiments, the benefit is tracked using a distributed ledger (e.g., ablockchain) that captures information associated with a set of actionsand events involving the intelligent agent. In some of theseembodiments, a smart contract may govern the administration of thereward to the expert user.

In some embodiments, the intelligent agent system 20210 and/or a clientapplication 20312 can monitor outcomes related to the user'sinteractions and may reinforce the training of the intelligent agentbased on the outcomes. For example, each time the user performs a buyingaction, the intelligent agent system 20210 may determine the outcome(e.g., whether the outcome is a positive outcome or a negative outcome).The intelligent agent system 20210 may then retrain the intelligentagent based on the outcome. Examples of outcomes may include datarelating to at least one of a financial outcome, a profitabilityoutcome, an operational outcome, an order cancellation outcome, a faultoutcome, a success outcome, a performance indicator outcome, an outputoutcome, a consumption outcome, an energy utilization outcome, aresource utilization outcome, a behavioral outcome (such as attentionbehavior, mobility behavior, purchasing behavior, selling behavior, orothers), a cost outcome, a profit outcome, a revenue outcome, a salesoutcome, a warranty claim outcome, an insurance claim outcome, a lendingoutcome (e.g., a default or repayment outcome), a collateralizationoutcome, and a production outcome. In these embodiments, the intelligentagent system 20210 may monitor data obtained from the various datasources after an action is taken to determine an outcome (e.g., profitincreased/decreased and by how much, purchased asset condition,purchased asset performance, whether desired counterparty behavior wasinduced, and the like). The intelligent agent system 20210 may includethe outcome in the training data set associated with the actionundertaken by the user that resulted in the outcome.

In some embodiments, the intelligent agent system 20210 receivesfeedback from users regarding respective intelligent agents 20234. Forexample, in some embodiments, a client application 20312 that leveragesan intelligent agent may provide an interface by which a user canprovide feedback regarding an action output by an intelligent agent. Inembodiments, the user provides the feedback that identifies andcharacterizes any errors by the intelligent agent. In some of theseembodiments, a report may be generated (e.g., by the client application20312 or the market orchestration system platform 20500) that indicatesthe set of errors encountered by the user. The report may be used toreconfigure/retrain the intelligent agent. In embodiments, thereconfiguring/retraining of an intelligent agent may include removing aninput that is the source of the error, reconfiguring a set of nodes ofthe artificial intelligence system, reconfiguring a set of weights ofthe artificial intelligence system, reconfiguring a set of outputs ofthe artificial intelligence system, reconfiguring a processing flowwithin the artificial intelligence system (such as placing gates on arecurrent neural network to render it a gated RNN that balances learningwith the need to diminish certain inputs in order to avoid explodingerror problems), reengineering the type of the artificial intelligencesystem (such as by modifying the neural network type among aconvolutional neural network, a recurrent neural network, a feed forwardneural network, a long-term/short-term memory (LSTM) neural network, aself-organizing neural network, or many other types and combinations),and/or augmenting the set of inputs to the artificial intelligencesystem.

In embodiments, the intelligent agent may be configured to, at leastpartially, operate as a double of the user having a role within amarketplace. In these embodiments, the intelligent agent system 20210trains an intelligent agent based on a training data set that includes aset of interactions by a specific user during the performance of theirrespective role within the marketplace. For example, the set ofinteractions that may be used to train the intelligent agent may includeinteractions of the user with the entities of a marketplace,interactions of the user with other users of the marketplace,interactions of the user with assets listed in the marketplace,interactions of the user with a digital twin, interactions of the userwith sensor data obtained from a sensor system, interactions of the userwith data streams generated by physical entities, interactions of theuser with the computational entities of the marketplace, and the like.In some embodiments, the intelligent agent system 20210 parses thetraining data set of interactions to identify a chain of reasoning ofthe user upon a set of interactions. In some of these embodiments, thechain of reasoning may be parsed to identify a type of reasoning of theuser, which may be used as a basis for configuring/training theintelligent agent. For example, the chain of reasoning may be adeductive chain of reasoning, an inductive chain of reasoning, apredictive chain of reasoning, a classification chain of reasoning, aniterative chain of reasoning, a trial-and-error chain of reasoning, aBayesian chain of reasoning, a scientific method chain of reasoning, andthe like. In some embodiments, the intelligent agent system 20210 parsesthe training data set of interactions to identify a type of processingundertaking by the user in analyzing the set of interactions. Forexample, types of processing may include audio processing in analyzingaudible information, tactile or “touch” processing in analyzing physicalsensor information, textual information processing in analyzing text,motion processing in analyzing motion information, visual processing inanalyzing visual information, spatiotemporal processing in processingspatiotemporal information, mathematical processing in mathematicallyoperating on numerical data, creative processing when derivingalternative options, analytic processing when selecting from a set ofoptions, and the like. In embodiments, identification of a type ofreasoning and/or a type of processing may be informed by undertakingbrain imaging, such as functional MRI or other magnetic imaging,electroencephalogram (EEG), or other imaging, such as by identifyingbroad brain activity (e.g., wave bands of activity, such as delta,theta, alpha and gamma waves), by identifying a set of brain regionsthat are activated and/or inactive during the set interactions of theuser that are being used for training of the intelligent agent (such asneocortex regions, such as Fp1 (involved in judgment and decisionmaking), F7 (involved in imagination and mimicry), F3 (involved inanalytic deduction), T3 (involved in speech), C3 (involved in storage offacts), T5 (involved in mediation and empathy), P3 (involved in tacticalnavigation), O1 (involved in visual engineering), Fp2 (involved inprocess management), F8 (involved in belief systems), F4 (involved inexpert classification), T4 (involved in listening and intuition), C4(involved in artistic creativity), T6 (involved in prediction), P4(involved in strategic gaming), O2 (involved in abstraction), and/orcombinations of the foregoing) or by other neuroscientific,psychological, or similar techniques that provide insight into howhumans upon which the intelligent agent is trained are solvingparticular types of problems that are involved in workflows for whichintelligent agents are deployed. In embodiments, an intelligent agentmay be configured with a neural network type, or combination of types,that is selected to replicate or simulate a processing activity that issimilar to the activity of the brain regions of a human expert that isperforming a set of activities for which the intelligent agent is to betrained. As one example among many possible, a trader may be shown touse visual processing region O1 and strategic gaming region P4 of theneocortex when making successful trades, and a neural network may beconfigured with a convolutional neural network to provide effectivereplication of visual pattern recognition and a gated recurrent neuralnetwork to replicate strategic gaming. In embodiments, a library ofneural network resources representing combinations of neural networktypes that mimic or simulate neocortex activities may be configured toallow selection and implementation of modules that replicate thecombinations used by human experts to undertake various activities thatare subjects of development of intelligent agents, such as involvingrobotic process automation. In embodiments, various neural network typesfrom the library may be configured in series and/or in parallelconfigurations to represent processing flows, which may be arranged tomimic or replicate flows of processing in the brain, such as based onspatiotemporal imaging of the brain when involved in the activity thatis the subject of automation. In embodiments, an intelligent softwareagent for agent development may be trained, such as using any of thetraining techniques described herein, to select a set of neural networkresource types, to arrange the neural network resource types accordingto a processing flow, to configure input data sources for the set ofneural network resources, and/or to automatically deploy the set ofneural network types on available computational resources to initiatetraining of the configured set of neural network resources to perform adesired intelligent agent/automation workflows. In embodiments, theintelligent software agent used for agent development operates on aninput data set of spatiotemporal imaging data of a human brain, such asan expert who is performing the workflows, and uses the spatiotemporalimaging data to automatically select and configure the selection andarrangement of the set of neural network types to initiate learning.Thus, a system for developing an intelligent agent may be configured for(optionally automatic) selection of neural network types and/orarrangements based on spatiotemporal neocortical activity patterns ofhuman users involved in workflows for which the agent is trained. Oncedeveloped, the resulting intelligent agent/process automation system maybe trained as described throughout this disclosure.

In embodiments, a system for developing an intelligent agent 20234(including the aforementioned agent for development of intelligentagents) may use information from brain imaging of human users to infer(optionally automatically) what data sources should be selected asinputs for an intelligent agent. For example, for processes whereneocortex region O1 is highly active (involving visual processing),visual inputs (such as available information from cameras, or visualrepresentations of information like price patterns, among many others)may be selected as favorable data sources. Similarly, for processesinvolving region C3 (involving storage and retrieval of facts), datasources providing reliable factual information (such as blockchain-baseddistributed ledgers) may be selected. Thus, a system for developing anintelligent agent may be configured for (optionally automatic) selectionof input data types and sources based on spatiotemporal neocorticalactivity patterns of human users involved in workflows for which theagent is trained.

In embodiments, the intelligent agents 20234 are trained to outputactions on behalf of a trader, a buyer, a seller, a broker, amarketplace host and/or an affiliate of a buyer, a seller, a broker, ormarketplace host. In these embodiments, an intelligent agent may betrained for marketplace roles, such that a user in a particularmarketplace role can train the intelligent agent by performing theirrespective role. For example, an intelligent agent may be trained forperforming actions on behalf of or recommending actions to a user in aparticular role within a marketplace. In some of these embodiments, theclient application 20312 may provide the functionality of the marketorchestration system platform 20500. For example, in some embodiments,users may view market orchestration digital twins and/or may use theexchange suite tools via the client application 20312. During the use ofthe client application 20312, a buyer may use a screener tool to filterassets by setting criteria via the graphical user interface. Each timethe user interacts with the client application 20312, the clientapplication 20312 may monitor the user's actions and may report theactions back to the intelligent agent system 20210. Over time, theintelligent agent system 20210 may learn how the particular userresponds to certain situations. For instance, if the user is the sellerand each time the price of an asset is within a specific range, theseller places an order request to sell assets, the intelligent agentsystem 20210 may learn to automatically sell assets when pricing forthose assets is within the specific range. Further implementations ofthe intelligent agent system 20210 are discussed further in thedisclosure.

In embodiments, the market orchestration system platform 20500 includesthe marketplace databases 20216 that store data on behalf ofmarketplaces. In embodiments, each marketplace may have an associateddata lake that receives data from various data sources 20224, anassociated set of blockchains that store transactional data, such as ina set of distributed ledgers, or the like. In some embodiments, themarketplace databases 20216 receive the data via one or more API Systems20238. For example, in embodiments, the API may be configured to obtainreal-time sensor data from one or more sensor systems 20274. The sensordata may be collected in a data lake, a set of blockchains, or the likeassociated with the marketplace. The digital twin system 20208 and theintelligent services system 20243 may structure the data in the dataresources and may populate one or more respective market orchestrationdigital twins based on the collected data. In some embodiments, the datasources 20224 may include an edge device 20292 that collects sensor datafrom the sensor system 20274 and/or other suitable IoT devices. In someof these embodiments, the edge devices 20292 may be configured toprocess sensor data (or other suitable data) collected at a “networkedge” of the enterprise. Edge processing of enterprise data may includesensor fusion, data compression, data structuring, and/or the like. Insome embodiments, the edge device 20292 may be configured to analyze thecollected sensor data and to adjust a sensor data stream based on thecontents of the collected sensor data. For example, an edge device 20292may stream sensor data that is considered anomalous without compressionand may compress and stream sensor data that is considered to be withina tolerance range. In this way, the edge device 20292 may providesemi-sentient data streams. In embodiments, the market orchestrationsystem platform 20500 may store the data streams in the data lake and/ormay update one or more market orchestration digital twins with some orall of the received data.

In embodiments, the marketplace participant user device 20218 mayexecute one or more client applications 20312 that interface with themarket orchestration system platform 20500. In embodiments, a clientapplication 20312 may request and display one or more marketorchestration digital twins. In some of these embodiments, a clientapplication 20312 may depict a market orchestration digital twincorresponding to the role of the user. For example, if the user is atrader, the market orchestration system platform 20500 may provide atrader digital twin to the user. In some of these embodiments, the userdata stored at the market orchestration system platform 20500 and/or theclient device may indicate the role of the user and/or the types ofmarket orchestration digital twins the user has access to.

In embodiments, the client application 20312 may display the requestedmarket orchestration digital twin and may provide one or more options toperform one or more respective operations corresponding to the marketorchestration digital twin and the states depicted therein. Inembodiments, the operations may include one or more of “drilling down”into a particular state, exporting a state or set of states intocollaborative documents (e.g., into a word processor document, aspreadsheet, a PowerPoint document, an annual report, or the like),performing a simulation, inspecting an asset, or the like. For example,a marketplace host may view a marketplace host digital twin. Amongst thestates that may be depicted in the marketplace host digital twin mayinclude notifications of potential issues with marketplace performance.In viewing the marketplace host digital twin, the user may wish to“drill down” into marketplace performance information. In this example,the client application 20312 depicting the marketplace host digital twinmay allow the user to view higher granularity marketplace performanceinformation, including execution speed, percentage of orders priceimproved, net improvement per order, liquidity multiple, and the like.

Referring to FIG. 208 , in embodiments, the digital twin system 20208 isexecuted by a computing system (e.g., one or more servers) that mayinclude a processing system 20802 that includes one or more processors,a storage system 20834 that includes one or more computer-readablemediums, and a network interface 20860 that includes one or morecommunication units that communicate with a network (e.g., the Internet,a private network, and the like). In embodiments, a processing system20802 may execute one or more of a digital twin configuration system20810, digital twin I/O system 20812, a data structuring system 20814, adigital twin generation system 20818, a digital twin perspective builder20820, a digital twin access controller 20822, a digital twininteraction manager 20824, an environment simulation system 20828, adata simulation system 20830, a digital twin notification system 20832,and a digital twin simulation system 20804. The processing system 20802may execute additional or alternative components without departing fromthe scope of the disclosure. In embodiments, the storage system 20834may store marketplace data, such as a marketplace data lake 20858, adigital twin data store 20838, and/or a behavior datastore 20840. Thestorage system 20834 may store additional or alternative data storeswithout departing from the scope of the disclosure. In embodiments, thedigital twin system 20208 may interface with the other components of themarket orchestration system platform 20500, such as the marketplaceconfiguration system 20302, the exchange suite 20204, the intelligentagent system 20210, and/or the intelligent services system 20243.

In embodiments, the digital twin configuration system 20810 isconfigured to set up and manage the market orchestration digital twinsand associated metadata of market orchestration digital twins and toconfigure the data structures and data listening threads that power themarket orchestration digital twins. In embodiments, the digital twinconfiguration system 20810 receives the types of digital twins that willbe supported for the marketplace, as well as the differentstates/objects that will be depicted in each type of digital twin. Foreach type of digital twin, the digital twin configuration system 20810identifies the types of data that feed or otherwise support each statethat is depicted in the respective type of digital twin and maydetermine any internal or external software requests that are requiredto support the identified data types. In some embodiments, the digitaltwin configuration system 20810 determines internal and/or externalsoftware requests that support the identified data types by analyzingthe relationships between the different types of data that correspond toa particular state/granularity. In embodiments, the digital twinconfiguration system 20810 determines and manages the data structuresneeded to support each type of digital twin. For example, for an assetdigital twin representing real estate listed in a marketplace, thedigital twin configuration system 20810 may instantiate a database(e.g., a graph database that defines the ontology of the property andthe objects existing (or potentially existing) within the property andthe relationships therebetween), whereby the instantiated databasecontains and/or references the underlying data that powers the realestate digital twin (e.g., sensor data and analytics, 3D maps, physicalasset twins, and the like). In some embodiments, the different types ofmarket orchestration digital twins may be configured in accordance witha set of preference settings and taxonomy settings. In some embodiments,the digital twin configuration system 20810 may utilize pre-definedpreferences (e.g., default preference templates for different types ofmarket orchestration digital twins) and taxonomies (e.g., defaulttaxonomies for different types of market orchestration digital twins)and/or receive custom preference settings and taxonomies from aconfiguring user. Examples of role-specific templates that are used toconfigure a role-based digital twin may include a trader template, abroker template, and a marketplace host template. Similarly, examples oftaxonomies that are used to configure different types of role-baseddigital twins may include a trader taxonomy, a broker taxonomy, and amarketplace host taxonomy.

In embodiments, the digital twin configuration system 20810 mayconfigure the databases that support each respective marketorchestration digital twin (e.g., role-based digital twins, assetdigital twins, market orchestration digital twins, and the like), whichmay be stored on the digital twin data store 20838. In embodiments, foreach database configuration, the digital twin configuration system 20810may identify and connect any external resources needed to collect datafor each respective data type. For example, in order to collect datafrom one or more edge devices 20292, the configuration system 20810 mayinitiate a process of granting access to the edge devices 20292 to theAPIs of the market orchestration system platform 20500.

In embodiments, the digital twin I/O system 20812 is configured toobtain data from a set of data sources. In some embodiments, the digitaltwin I/O system 20812 (or other suitable components) may provide agraphical user interface that allows a user affiliated with amarketplace to upload various types of data that may be leveraged togenerate the market orchestration digital twins. For example, the usermay upload 3D scans, images, LIDAR scans, blueprints, 3D floor plans,object types (e.g., products, sensors, machinery, furniture, and thelike), object properties (e.g., materials, physical properties,descriptions, price, and the like), output type (e.g., sensor units),and the like. In embodiments, the digital twin I/O system 20812 maysubscribe to or otherwise automatically receive data streams (e.g.,publicly available data streams, such as RSS feeds, sensor systemstreams, and the like) on behalf of a marketplace or marketplaceentities. Additionally or alternatively, the digital twin I/O system20812 may periodically query and/or receive data from a connected datasource, such as the sensor system 20274 having sensors that sensor datafrom facilities (e.g., manufacturing facilities, shipping facilities,agricultural facilities, resource extraction facilities, computingfacilities, and the like), and/or other physical entities, financialdatabases, surveys, third-party data sources, and/or third partydatastores that store third party data, and edge devices 20292 thatreport data relating to physical assets (e.g., smartmachinery/manufacturing equipment, sensor kits, autonomous vehicles,wearable devices, and the like). In embodiments, the digital twin I/Osystem 20812 may employ a set of web crawlers to obtain data. Inembodiments, the digital twin I/O system 20812 may include listeningthreads that listen for new data from a respective data source.

In some embodiments, the digital twin I/O system 20812 is configured toserve the obtained data to instances of market orchestration digitaltwins (which is used to populate digital twins) that are executed by aclient device or the market orchestration system platform 20500. Inembodiments, the digital twin I/O system 20812 receives data streamfeeds and/or collects on behalf in a marketplace and stores at least aportion of the streams into a data lake or other data resourceassociated with the marketplace.

In embodiments, the data structuring system 20814 builds data into aformat and grain that can be consumed by a market orchestration digitaltwin. In embodiments, the data structuring system 20814 may leverage ETL(extract, transform, load) tools, data streaming, and other dataintegration tooling to structure the data. In embodiments, the datastructuring system 20814 structures the data according to a digital twindata model that may be defined by the digital twin configuration system20810 and/or a user. A data model may refer to an abstract model thatorganizes elements of marketplace-related data and standardizes themanner by which those elements relate to one another and to theproperties of digital twin entities. For instance, a digital twin datamodel of a vehicle fleet (e.g., a vehicle fleet listed in a marketplace)may specify that the data element representing a vehicle be composed ofa number of other elements which represent sub-elements or attributes ofthe vehicle (the color of the vehicle, the dimensions of the vehicle,the engine of the vehicle, the engine parts of the vehicle, the owner ofthe vehicle, and the like). In this example, the digital twin modelcomponents may define how the physical attributes are tied to respectivephysical locations on the vehicle. In embodiments, a digital twin modelmay define a formalization of the objects and relationships found in aparticular application domain. For example, a digital twin model mayrepresent the asset components and how they relate to each other withinthe various digital twins. Additionally or alternatively, a digital twindata model may define a set of concepts (e.g., entities, attributes,relations, tables, and/or the like) used in defining such formalizationsof data or metadata. For example, a “digital twin data model” used inconnection with a banking application may be defined using theentity-relationship “data model” and how it is then related to thevarious market orchestration digital twin views.

In embodiments, the digital twin generation system 20818 serves marketorchestration digital twins. In some instances, the digital twingeneration system 20818 receives a request for a specific type ofdigital twin from a client application 20312 being executed by themarketplace participant user device 20218 (e.g., via an API).Additionally or alternatively, the digital twin generation system 20818receives a request for a specific type of digital twin from a componentof the market orchestration system platform 20500 (e.g., the digitaltwin simulation system 20804). The request may indicate the marketplace,the type of digital twin, and the user (whose access rights may beverified or determined by the digital twin access controller 20822). Insome embodiments, the digital twin generation system 20818 may determineand provide the client device 20340 with the data structures, metadata,ontology, and information on hooks to data feeds as well as the digitaltwin constructs. This information may be used by the client to generatethe digital twin in the end user device (e.g., an immersive device, suchas AR devices or VR devices, tablet, personal computer, mobile, or thelike). In embodiments, the digital twin system 20208 may determine theappropriate perspective for the requested digital twin (e.g., via theperspective builder 20820), any data restrictions that the user may have(e.g., via the digital twin access controller 20822), and in response tothe perspective and data restrictions, may generate the requesteddigital twin. In some embodiments, generating the requested digital twinmay include identifying the appropriate data structure given theperspective and obtaining the data that parameterizes the digital twin,as well as any additional metadata that is served with the marketorchestration digital twin.

In embodiments, the digital twin generation system 20818 may deliver themarket orchestration digital twin to the requesting client application20312. In embodiments, the digital twin generation system 20818 (oranother suitable component) may continue to update a served digital twinwith real-time data (or data that is derived from real-time data) as thereal-time data is received and potentially analyzed, extrapolated,derived, predicted, and/or simulated by the market orchestration systemplatform 20500.

In some embodiments, the digital twin generation system 20818 may obtaindata streams from various data sources, such as relational databases,object-oriented databases, distributed databases, blockchains, Hadoopfile stores, graph databases, and the like that underlie operational andreporting tooling in the environment. In embodiments, the digital twingeneration system 20818 may obtain data streams that are associated withthe structural aspects of the data, such as the layout and 3D objectswithin facilities or the hierarchical design of a system of accounts. Inembodiments, the data streams may include metadata streams that areassociated with the nature of the data and data streams containingprimary data (e.g., sensor data, sales data, IoT device data,point-of-sale data, behavioral data, survey data, and many others). Forexample, the metadata associated with a physical facility may includethe types and layers of data that are being managed, while the primarydata may include the instances of objects that fall within each layer.

In embodiments, the digital twin perspective builder 20820 leveragesmetadata, artificial intelligence, and/or other data processingtechniques to produce a definition of information required forgeneration of the digital twin in the digital twin generation system20818. In some embodiments, different relevant datasets are hooked to adigital twin (e.g., an asset digital twin, a trader digital twin, amarketplace digital twin, a marketplace host digital twin, or the like)at the appropriate level of granularity, thereby allowing for thestructural aspects of the data (e.g., system of accounts, pricing data,sensor readings, or the like) to be a part of the data analyticsprocess. One aspect of making a perspective function is that the usercan change the structural view or the grain of data while potentiallyforecasting future events or changes to the structure to guide control.In embodiments, the term “grain of data” may refer to a single line ofdata. Examples of “grains of data” may include a detailed record of atransaction or a single vibration reading from a vibration sensor. Grainis a characteristic governing to some extent how the data can becombined to form different aggregations. For example, if data isaggregated by whole days, then it is not readily broken down with highaccuracy by time of day. Generally, role-based and other marketorchestration digital twins benefit from finer levels of data, as theaggregations on such data can be dynamic in nature. It is noted thatdifferent types of digital twins, or workflows therein, may involvedifferent “sized” grains of data. For example, the grains of data thatfeed a marketplace host digital twin may be at a higher granularitylevel than the grains of data that feed a trader digital twin, or viceversa, depending on the particular workflow involved. In someembodiments, however, a marketplace host may drill down into a state ofthe marketplace host digital twin and the granularity for the selectedstate may be increased.

In embodiments, the digital twin perspective builder 20820 adds relevantperspective to the data underlying the digital twin, which is providedto the digital twin generation system 20818. For example, a traderdigital twin may link in various other types of fuzzy data with marketdata and depict the potential impacts of market forces or other forceson a simulated digital twin. These different perspectives generated bythe digital twin perspective builder 20820 may combine with the datasimulation system 20830 to render relevant simulations of howscenario-based future states might be handled, such as ones involving anasset, an asset class, a workflow, or the like. The digital twinsimulation system 20804 provides recommendations related to enhancingthe digital twin-represented entity, such as to meet the needs of theanticipated future states.

In embodiments, a digital twin model is based on a combination of dataand its relationship to the digital twin environments and/or processes.In embodiments, different digital twins may share the same data anddifferent digital twin perspectives can be the results of a set ofmetadata built on top of a digital twin data model or data environment.In embodiments, the digital twin data model provides the details of theinformation to be stored and it is used to build a layered system wherethe final computer software code is able to represent the information inthe lower levels in a form that is appropriate for the digital twinperspective being used.

In embodiments, the digital twin access controller 20822 informs thedigital twin generation system 20818 of specific constraints around theroles of users able to view the digital twin as well as providing fordynamically adjustable digital twins that can adapt to, constrain, orrelease views of the data specific to each user role. For example,sensitive marketplace performance data might be obfuscated from mostusers when viewing a market orchestration digital twin, but themarketplace host may be granted access to view the marketplaceperformance information directly. In embodiments, the digital twinaccess controller 20822 may receive a user identifier and one or moredata types. In response, the digital twin access controller 20822 maydetermine whether the user indicated by the user identifier has accessto the one more data types. In some of these embodiments, the user'spermissions and restrictions may be indicated in a user database.

In embodiments, the digital twin interaction manager 20824 manages therelationship between the structural view of the data in a marketorchestration digital twin (e.g., as depicted/represented by the clientapplication 20312) and the underlying data streams and data sources. Inembodiments, this interaction layer makes the digital twin into a windowinto the underlying data streams through the lens of the structure ofthe data. In embodiments, the digital twin interaction manager 20824determines the types of data that are being fed to an instance of amarket orchestration digital twin while the instance is being executedby a client application 20312. Put another way, the digital twininteraction manager 20824 determines and serves data for an in-usedigital twin. In embodiments, the digital twin interaction manager 20824feeds raw data received from a data source to the digital twin. Forexample, vibration sensor readings of a machine listed in a marketplacefor machine capacity may be fed directly to the executing digital twinof the machine. In embodiments, the digital twin interaction manager20824 obtains data and/or instructions that are derived by anothercomponent of the market orchestration system platform 20500. Forexample, the digital twin interaction manager 20824 may obtainanalytical data from the intelligent services system 20243 that isderived from incoming financial data, markets data, transaction data,asset performance data, operational data, sensor data, and the like. Inthis example, the digital twin interaction manager 20824 may then feedthe analytical data to a market orchestration digital twin (e.g., traderdigital twin), whereby the analytical data may be conveyed to the user.In examples, the digital twin interaction manager 20824 may receivesimulated pricing data from the digital twin simulation system 20804 toconvey pricing with respect to different assets, whereby the simulateddata is derived using historical markets data. In this example, thedigital twin interaction manager 20824 may receive requests fordifferent assets from a client device 20340 depicting a marketorchestration digital twin and may initiate the simulations for each ofthe assets. The digital twin interaction manager 20824 may then servethe results of the simulation to the requesting client application20312.

In embodiments, the digital twin interaction manager 20824 may manageone or more workflows that are performed via a market orchestrationdigital twin. For example, the market orchestration system platform20500 may store a set of marketplace workflows, where each marketplaceworkflow corresponds to a role within a marketplace and includes one ormore stages. Workflows may include marketplace design workflows,marketplace set-up workflows, marketplace execution workflows, pricingand/or discounting workflows, trading workflows, currency conversionworkflows, payment processing workflows, fulfillment workflows,advertising and promotion workflows, appraisal workflows, governanceworkflows, transactional workflows (such as smart contract workflows),compliance workflows, policy workflows, authentication workflows,reporting workflows, and many others. In embodiments, the digital twininteraction manager 20824 may receive a request to execute a workflow.The request may indicate the workflow and a user identifier. Inresponse, the digital twin interaction manager 20824 may retrieve therequested workflow and may provide specific instructions and/or data tothe client device 20340.

In embodiments, the digital twin simulation system 20804 receivesrequests to run simulations using one or more digital twins. Inembodiments, the request may indicate a set of parameters that are to bevaried and/or one or more simulation outcomes to output. In embodiments,the digital twin simulation system 20804 may request one or more digitaltwins from the digital twin generation system 20818 and may vary a setof different parameters for the simulation. In embodiments, the digitaltwin simulation system 20804 may construct new digital twins and newdata streams within existing digital twins. In embodiments, the digitaltwin simulation system 20804 may perform environment simulation and/ordata simulations. The environment simulation is focused on simulation ofthe digital twin ontology rather than the underlying data streams. Inembodiments, the data simulation system 20830 generates simulated datastreams appropriate for respective digital twin environments. Thissimulation allows for real world simulations of how a digital twin willrespond to specific events such as changes in the asset pricing and/orchanges in the demand of an asset.

In embodiments, the digital twin simulation system 20804 implements aset of models (e.g., physical mathematical forecasts, logicalrepresentations, or process diagrams) that develop the framework wheredata and the response of the digital twin can be simulated in responseto different situational stimuli or sets of stimuli. In embodiments, thedigital twin simulation system 20804 may include or leverage acomputerized model builder that constructs a predicted future state ofeither the data and/or the response of the digital twin to the inputdata. In some embodiments, the computerized model library may beobtained from a behavior datastore 20840 that stores one or morebehavior models that defines economic and/or scientific formulas orprocesses. The computerized digital twin model calculates the results ofthe model to build an interactive environment where users can watch andmanipulate the simulated environment seeing how the entire systemresponds to specific changes in the environment.

In embodiments, digital twin behavior models may be updated and improvedusing results of actual experiments and real-world events. The use ofsuch digital twin mathematical models and their simulations avoidsactual experimentation, which can be costly and time-consuming. Instead,mathematical knowledge and computational power is used to solvereal-world problems cheaply and in a time-efficient manner. As such, thedigital twin simulation system 20804 can facilitate understanding ofmarket behavior without actually testing the system in the real world.

In embodiments, simulations may be based upon and/or incorporatebehavioral models that predict the behavior of assets and/or othermarketplace-related entities (such as physical models that predictphysical conditions (such as based on physical, chemical and biologicalprinciples)), economic models that predict the economic behavior ofassets (such as models that predict the price of assets, the volume ofassets traded, transactions involving the assets, or the like) andmarketplace-related entities (such as models that predict the behaviorof buyers, sellers, traders, companies, government entities, regulatoryentities, or the like), human behavioral models that predict thebehavior of humans (such as psychological models, physiological models,demographic models, population models, sociological models,game-theoretic models, and many others), and many others, including anyof the models described herein or in the documents incorporated hereinby reference and including hybrids and combinations of the foregoing.

In some embodiments, behavioral models may be configured to predict theoutcome of one or more transactions. In these embodiments, thebehavioral models may be configured for select fields (e.g., behavioralmodels configured for software-as-a-service (SaaS) transactions,behavioral models configured for medical device transactions, behavioralmodels configured for pharmaceutical transactions, behavioral models forreal estate transactions, behavioral models for a vehicle repair servicemarketplace, and the like).

In embodiments, the simulations may include transaction simulations,buying simulations, selling simulations, trading strategy simulations,supply chain simulations, marketplace configuration simulations,marketing or advertising simulations, Federal Reserve interest ratechange simulations, latency simulations, lending simulations, mergersimulations, acquisition simulations, regulatory action simulations,workforce-related simulations, government policy and/or spendingsimulations, asset health simulations, asset performance simulations,off-chain activity simulations, new market entrant simulations, assetaging simulations, recession simulations, inflation simulations,influencer commentary simulations (e.g., commentary from financial newsshow hosts, chief executive officers, social media accounts or thelike), legal outcome simulations (e.g., simulations involving the legaloutcome of contract disputes, U.S. Supreme Court decisions, or thelike), geopolitical simulations (e.g., war, sanctions, or the like) andmany others.

In one non-limiting example, executing simulations of transactions mayinclude varying the number of transactions, the types of transactions,the frequency of transactions, the timing of transactions, the partiesinvolved in the transactions, and the like.

In embodiments, data collected and/or monitored via digital twinsimulations may be used to optimize asset allocation, optimize marketingresources, optimize marketplace configuration, and many others. Thedigital twins and/or the behavioral models may be configured tocontinuously adapt in real-time as new real-world data is collected.

In embodiments, simulation environments may be constructed using a modelcapable of predicting future state. These models include deep learning,regression models, quantum prediction engines, and other forms ofmodeling engines that use historical data to build a future stateprediction. In some embodiments, a consideration in making the digitaltwin models' function is the ability to also show the response of theperspective based digital twin structural elements, (e.g., defining thedeformation of the axle of a tractor in response to different sizeloads). For example, the resultant digital twin representation can thenbe presented to the user in a virtual reality or augmented realityenvironment where specific perspectives are shown in their digital twinform.

In embodiments, the digital twin notification system 20832 providesnotifications to users via market orchestration digital twins associatedwith the respective users. In some embodiments, digital twinnotifications are an important part of the overall interaction. Thedigital twin notification system may provide the digital twinnotifications within the context of the digital twin setting so that theperspective view of the notification is set up specifically to enableenlightenment of how the notification fits into the general digital twinrepresented ontology.

In embodiments, executive digital twins may include, but are not limitedto, trader digital twins 20842, broker digital twins 20844, marketplacehost digital twins 20850, marketplace digital twins 20852, asset digitaltwins 20854, and the like. The discussion of the different types ofdigital twins is provided for example and not intended to limit thescope of the invention. It is understood that in some embodiments, usersmay alter the configuration of the various market orchestration digitaltwins based on the needs of the marketplace, the reporting structure ofthe marketplace, and the like.

In embodiments, market orchestration digital twins are generated usingvarious types of data collected from different data sources. Asdiscussed, the data may include the market data 20280, the fundamentaldata 20282, historical data 20288, reference data 20284, data collectedfrom sensor systems 20274, news sources 20278, third party data sources20290, edge devices 20292, analytics data 20227, simulation/modeled data20229, and marketplace databases 20280. In embodiments, the sensor datamay be collected from one or more IIoT sensor systems (which may beinitially collected by edge devices of the enterprise). In embodiments,historical data 20288 may refer to any data collected by the marketplaceand/or on behalf of the marketplace and/or marketplace entities in thepast. This may include sensor data collected from sensor systems,account data, transaction data, pricing data, smart contract data, orderdata, reference data, fundamental data, market data, maintenance data,purchase data, asset data, leasing data, and the like. Analytics data20227 may refer to data derived by performing one or more analyticsprocesses on data collected by and/or on behalf of the marketplace.Simulation/modeled data 20229 may refer any data derived from simulationand/or behavior modeling processes that are performed with respect toone or more digital twins. The marketplace databases 20216 may be a datalake that includes data collected from any number of sources. Inembodiments, the market data 20280 may include data that is collectedfrom disparate data sources concerning or related to marketplaces. Themarket data 20280 may be collected from many different sources and maybe structured or unstructured. In embodiments, the market data 20280 maycontain an element of uncertainty that may be depicted in a digital twinthat relies on such market data 20280. It is appreciated that thedifferent types of data highlighted above may overlap. For example,historical data 20288 may be obtained from the market data 20280 and/orthe marketplace databases 20216 may include analytics data 20227,simulated/modeled data 20229, and/or the market data 20280. Additionalor alternative types of data may be used to populate a marketorchestration digital twin.

In embodiments, the data structuring system 20814 may structure thevarious data collected by and/or on behalf of the marketplace and/ormarketplace entities. In embodiments, the digital twin generation system20818 generates the market orchestration digital twins. As discussed,the digital twin generation system 20818 may receive a request for aparticular type of digital twin (e.g., a trader digital twin 20842) andmay determine the types of data needed to populate the digital twinbased on the configuration of the requested type of digital twin. Inembodiments, the digital twin generation system 20818 may then generatethe requested digital twin based on the various types of data (which mayinclude structured data structured by the data structuring system20814). In some embodiments, the digital twin generation system 20818may output the generated digital twin to a client application 20312,which may then display the requested digital twins.

In embodiments, a trader digital twin 20842 is a digital twin configuredfor a trader e.g., buyer and/or seller) in a marketplace. Inembodiments, the trader digital twin 20842 may work in connection withthe market orchestration system platform 20500 to provide simulations,predictions, statistical summaries, and decision-support based onanalytics, machine learning, and/or other AI and learning-typeprocessing of inputs (e.g., pricing data, counterparty data, asset data,order data, news, discussion boards, and the like). In embodiments, atrader digital twin 20842 may provide functionality including, but notlimited to, identifying counterparties, identifying assets, bidding onassets, buying assets, listing assets for sale, removing assets from alisting, selling assets, trading assets, inspecting assets, generatingorder requests, cancelling order requests, generating smart contracts,strategy generation, risk management, and other trader-relatedactivities.

In embodiments, the types of data that may populate a trader digitaltwin 20842 may include, but are not limited to: account data,macroeconomic data, microeconomic data, forecast data, demand planningdata, analytic results of AI and/or machine learning modeling (e.g.,financial forecasting), prediction data, asset data, recommendationdata, securities-relevant financial data (e.g., earnings,profitability), industry analyst data (e.g., Gartner quadrant),strategic competitive data (e.g., news and events regarding industrytrends and competitors), discussion board data, business performancemetrics by business unit that may be relevant to evaluating performanceof a company's business units (e.g., P&L, head count, factory health,R&D metrics, marketing metrics, and the like), and the like. Inembodiments, the digital twin system 20208 may obtain financial datafrom, for example, publicly disclosed financial statements, third-partyreports, tax filings, public news sources, and the like. In embodiments,the digital twin system 20208 may obtain strategic competitive data frompublic news sources, from publicly disclosed financial reports, and thelike. In embodiments, macroeconomic data may be derived analyticallyfrom various financial and operational data collected by the marketorchestration system platform 20500. In embodiments, the businessperformance metrics may be derived analytically, based at least in parton real time operations data, by the intelligent services system 20243and/or provided from other users and/or their respective trader digitaltwins.

In embodiments, a trader digital twin 20842 may include high-level viewsof different states of the marketplace, including account summaryinformation, asset pricing, order activity, real-time representations ofassets, historical representations of assets, projected representationsof assets (e.g., future states), real-time representations of companies,historical representations of companies, projected representations ofcompanies, news and/or television data, economic sentiment data, assetsentiment data, social media data, discussion board data, charts,countdown to close information, lease terms, smart contract terms, orderinformation, contract terms, and any other mission-critical information.In embodiments, a trader digital twin 20842 may allow a user to accessand/or interact with asset digital twins. In embodiments, a traderdigital twin 20842 may allow a user to interact with another traderdigital twin 20842 and/or a broker digital twin 20844. The traderdigital twin 20842 may initially depict the various states at a lowergranularity level. In embodiments, a user that is viewing the traderdigital twin 20842 may select to drill down into a selected state andview the selected state at a higher level of granularity. For example,the trader digital twin 20842 may initially depict a subset of thevarious states of a listed asset at a lower granularity level, includinga pricing state (e.g., a visual indicator indicating pricing for anasset). In response to a selection, the trader digital twin 20842 mayprovide data, analytics, summary, and/or reporting including, but notlimited to, real-time, historical, aggregated, comparison, and/orforecasted pricing data (e.g., real-time, historical, simulated, and/orforecasted revenues, liabilities, and the like). In this way, the traderdigital twin 20842 may initially present the user (e.g., the buyer orseller) with a view of different aspects of the asset (e.g., differentindicators to indicate different “health” levels of an asset) but mayallow the user to select which aspects require more of her attention. Inresponse to such a selection, the trader digital twin 20842 may requesta more granular view of the selected state(s) from the marketorchestration system platform 20500, which may return the requestedstates at the more granular level.

In embodiments, a trader digital twin 20842 may be configured tointerface with the exchange suite 20204 to specify and provide a set ofmarketplace tools that may be leveraged by the trader. The marketplacetools may include an “in-twin” strategies tool, an “in-twin” tradingpractice tool, an “in-twin” news tool, an “in-twin” screener tool, an“in-twin” market monitoring tool, an “in-twin” entity profile tool, an“in-twin” account management tool, an “in-twin” charting tool, an“in-twin” order request tool, an “in-twin” smart contract system, and“in-twin” collaboration tools. The collaboration tools may include videoconferencing tools, “in-twin” collaboration tools, presentation tools,word processing tools, spreadsheet tools, and the like, as describedherein. In embodiments, the collaboration tools include various toolsthat allow communication between marketplace entities. In embodiments,the collaboration tools include digital-twin enabled video conferencing.In these embodiments, the market orchestration system platform 20500 maypresent participants in the video conference with the requested view ofa market orchestration digital twin (e.g., an asset digital twin). Forexample, during a potential trade, a seller proposing to sell an assetmay present the asset digital twin to the potential buyer. In thisexample, the seller may illustrate the results of simulations performedon the asset.

In embodiments, the trader digital twin 20842 may be configured tosimulate one or more aspects of the marketplace. Such simulations mayassist the user (e.g., the trader, buyer, and/or seller) in makingbuying, selling, and/or trading decisions. For example, simulations of aproposed asset purchase may be tested using the modeling, machinelearning, and/or AI techniques, as described herein, by simulatingeconomic factors (e.g., interest rates, inflation, unemployment, GDPgrowth), simulating fundamental factors (earnings, sales, cash flow,book value, enterprise value, dividends), simulating market sentiment,simulating asset physical performance, and/or other suitablemarketplace-related parameters. In embodiments, the digital twinsimulation system 20804 may receive a request to perform a simulationrequested by the trader digital twin 20842, where the request indicatesone or more parameters that are to be varied in one or more marketorchestration digital twins. In response, the digital twin simulationsystem 20804 may return the simulation results to the trader digitaltwin 20842, which in turn outputs the results to the user via the clientdevice display. In this way, the user may be provided with variousoutcomes corresponding to different parameter configurations. Forexample, a user may request a set of simulations to be run to testdifferent trading strategies to see how the different strategies affectthe overall impact on profits and losses. The digital twin simulationsystem 20804 may perform the simulations by varying the differenttrading strategies and may output the financial forecasts for eachrespective trading strategy. In some embodiments, the user may select aparameter set based on the various outcomes and iterate simulationsbased at least on the varied prior outcomes. Drawing from the previousexample, the user may decide to select the trading strategy thatmaximizes financial forecasts. In some embodiments, an intelligent agentmay be trained to recommend and/or select a parameter set based on therespective outcomes associated with each respective parameter set.

In embodiments, a trader digital twin 20842 may be configured to store,aggregate, merge, analyze, prepare, report, and distribute materialrelating to pricing, assets, financial reporting, ratings, rankings,financial trend data, income data, or other data related to amarketplace. A trader digital twin 20842 may link to, interact with, andbe associated with external data sources, and able to upload, download,aggregate external data sources, including with the market orchestrationsystem platform 20500's internal data, and analyze such data, asdescribed herein. Data analysis, machine learning, AI processing, andother analysis may be coordinated between the trader digital twin 20842and an analytics team based at least in part on using the intelligentservices system 20243. This cooperation and interaction may includeassisting with seeding marketplace-related data elements and domains inthe digital twin data store 20838 for use in modeling, machine learning,and AI processing to identify an optimal trading strategy or some othermarketplace-related metric or aspect, as well as identification of theoptimal data measurement parameters on which to base judgment of atrading strategy's success. In embodiments, the digital twin system20208 abstracts the different views (or states) within the digital twinto the appropriate granularity. For instance, the digital twin system20208 may have access to all the sensor data collected on behalf of themarketplace as well as access to real-time sensor data streams. In thisexample, if the sensor readings from a particular physical asset listedin a marketplace (e.g., a piece of manufacturing equipment) areindicative of a potentially critical situation (e.g., failure state,dangerous condition, or the like), then the analytics that indicate thepotentially critical situation may become very important to the trader.Thus, the digital twin system 20208, when building the appropriateperspective for the trader, may include a state indicator of thephysical asset in the trader digital twin 20842. In this way, the tradercan drill down into the state indicator of the physical asset to viewthe potentially critical situation at a greater granularity (e.g., themachinery and an analysis of the sensor data used to identify thesituation).

In embodiments, a trader digital twin 20842 may be configured to reporton the performance of assets traded in the marketplace. As describedherein, reporting may include financial performance metrics, physicalperformance metrics, data regarding resource usage, or some other typeof reporting data. In embodiments, an intelligent agent trained by theuser may be trained to surface the most important reports to the user.For example, if the user (e.g., the trader) consistently views andfollows up on P&L but routinely skips over reports relating to economicsentiment, the executive agent may automatically surface reports relatedto P&L to the user while suppressing economic sentiment data.

In embodiments, a trader digital twin 20842 may be configured tomonitor, store, aggregate, merge, analyze, prepare, report, anddistribute material relating to marketplace counterparties or namedentities of interest. In embodiments, such data may be collected by themarket orchestration system platform 20500 via data aggregation, webscraping, or other techniques to search and collect counterpartyinformation from sources including, but not limited to, information oninvestment and/or acquisitions, press releases, SEC or other financialreports, or some other publicly available data. For example, a userwishing to monitor a certain counterparty may request that the traderdigital twin 20842 provide materials relating to the certaincounterparty. In response, the market orchestration system platform20500 may identify a set of data sources that are either publiclyavailable or to which the trader has access (e.g., internal datasources, licensed 3^(rd) party data, or the like).

In embodiments, the client application 20312 that executes the traderdigital twin 20842 may be configured with an intelligent agent 20234that is trained on the trader's actions (which may be indicative ofbehaviors, and/or preferences). In embodiments, the intelligent agent20234 may record the features relating to the actions (e.g., thecircumstances relating to the user's action) to the intelligent agentsystem 20210. For example, the intelligent agent 20234 may record eachtime the user executes a trade (which is the action) as well as thefeatures surrounding the trade (e.g., the type of action, the type ofasset, the price of the asset, the counterparty or counterparties, thequantity of assets, market sentiment in relation to the asset, shippingand/or delivery information, and the like). The intelligent agent 20234may report the actions and features to the intelligent agent system20210 that may train the intelligent agent 20234 on the manner by whichthe intelligent agent 20234 can undertake or recommend trading tasks inthe future. Once trained, the intelligent agent 20234 may automaticallyperform actions and/or recommend actions to the user. Furthermore, inembodiments, the intelligent agent 20234 may record outcomes related tothe performed/recommended actions, thereby creating a feedback loop withthe intelligent agent system 20210.

In embodiments, a trader digital twin 20842 may provide an interface fora trader to perform one or more trader-related workflows. For example,the trader digital twin 20842 may provide an interface for a trader toperform, supervise, or monitor strategy-generating workflows, assetlisting workflows, asset inspection workflows, counterpartyidentification workflows, screening workflows, order workflows, smartcontract workflows, shipping and/or delivery workflows, regulatoryworkflows, and the like.

In some embodiments, an AI-reporting tool may be configured to monitorone or more user-defined marketplace assets and/or marketplace assetproperties. Examples of marketplace asset properties may include, butare not limited to, price, opening price, high price, low price, volume,P/E/ market cap, 52-week high price, 52-week low price, average volume,and the like.

In embodiments, intelligent agents 20234 are expert agents that aretrained to perform tasks on behalf of users (e.g., traders). Asdiscussed, in some embodiments, a client application 20312 may monitorthe use of the client application 20312 by a user when using the clientapplication 20312. In these embodiments, the client application 20312may monitor the states of a market orchestration digital twin that theuser drills down into, decisions that are made, and the like. As theuser uses the client application 20312, the intelligent agent system20210 may train one or more machine-learned models on behalf of theparticular user, such that the models may be leveraged by an intelligentagent 20234 to perform tasks on behalf of or recommend actions to theuser.

In embodiments, the marketplace suite includes a trading practice tool20233, which may include software tools that may be leveraged to train auser. In embodiments, the trading practice tool 20233 may leveragedigital twins to improve training for trading in a marketplace. Forexample, a trading practice tool 20233 may provide real-world examplesthat are based on the data collected from the marketplace and may enablea user to train on the real-world examples using virtual pretend moneyprovided by the trading practice tool 20233. The trading practice tool20233 may present the user with different scenarios via a marketorchestration digital twin 20220 and the user may take actions. Based onthe actions, the trading practice tool 20233 may request a simulationfrom the market orchestration system platform 20500, which in turnreturns the results to the user. In this way, the user may be trained onscenarios that are based on the actual marketplace of the user.

In embodiments, strategy tools 20240 are software tools that leveragedigital twins to assist users to create trading strategies for amarketplace. In embodiments, a strategy tool 20240 may be configured toprovide a graphical user interface that allows a trader to createtrading strategies. In some embodiments, the strategy tool 20240 may beconfigured to request a simulation from the market orchestration systemplatform 20500 given the parameters set in the created strategy. Inresponse, the market orchestration system platform 20500 may return theresults of the simulation and the user can determine whether to adjustthe strategy. In this way, the user may iteratively refine the strategyto achieve one or more objectives. In embodiments, an intelligent agent20234 may monitor the track of the actions taken while the strategy isbeing refined by the user so that the intelligent agent system 20210 maytrain the intelligent agent 20234 to generate or recommend strategies tothe user in the future.

In embodiments, an exchange host digital twin 20848 is a digital twinconfigured for a marketplace host. In embodiments, the marketplace hostdigital twin 20850 may work in connection with the market orchestrationsystem platform 20500 to provide simulations, predictions, statisticalsummaries, decision-support, and configuration and control support basedon analytics, machine learning, and/or other AI and learning-typeprocessing of inputs (e.g., operations data, trader data, broker data,asset data, order data, regulatory data, fee data, and the like). Inembodiments, a marketplace host digital twin 20850 may providefunctionality including, but not limited to, configuring amarketplace/exchange, optimizing a marketplace/exchange, controlling amarketplace/exchange, performing regulatory reporting, risk management,compliance, optimizing profitability, optimizing volume, promotion ofthe marketplace to traders and other parties, and other marketplacehost-related activities.

In embodiments, the types of data that may populate a marketplace hostdigital twin 20850 may include, but are not limited to: order data,marketplace/exchange performance data (e.g., execution speed, liquiditymultiple, percentage of orders price improved, net improvement perorder), asset data, demand planning data, trader data, broker data,analytic results of AI and/or machine learning modeling (e.g.,marketplace configuration support), prediction data, asset data,recommendation data, securities-relevant financial data (e.g., earnings,profitability), discussion board data, social media data, fee data,regulatory data, and many others.

The marketplace host digital twin 20850 may include high-level views ofdifferent states and/or marketplace-related data, including trader data(e.g., number of traders), trader account activity data, transactiondata, asset data, regulatory data, fee data, commission data, brokerdata, execution speed data, execution price data, price improvementdata, gross price improvement data, percentage of orders price improveddata, net improvement per order data, liquidity data, liquidity multipledata, market share data, latency data, spread data, at or better (AOB)than the National Best Bid or Offer (NBBO) data, effective spread data,effective spread over quoted spread (EFQ) data, savings per order data,order size data, and many other types of data.

The marketplace host digital twin 20850 may initially depict the variousstates at a lower granularity level. In embodiments, a user that isviewing the marketplace host digital twin 20850 may select a state todrill down into the selected state and view the selected state at ahigher level of granularity. For example, the marketplace host digitaltwin 20850 may initially depict a subset of the various states ofmarketplace performance at a lower granularity level, including amarketplace performance state (e.g., a visual indicator indicatingoverall performance for a marketplace). In response to a selection, themarketplace host digital twin 20850 may provide data, analytics,summary, and/or reporting including, but not limited to, real-time,historical, aggregated, comparison, and/or forecasted performance data(e.g., real-time, historical, simulated, and/or forecasted executionspeed, liquidity multiples, number of marketplace participants, and thelike). In this way, the marketplace host digital twin 20850 mayinitially present the user (e.g., the marketplace host) with a view ofdifferent aspects of the marketplace (e.g., different indicators toindicate different “health” levels of a marketplace) but may allow theuser to select which aspects require more of her attention. In responseto such a selection, the marketplace host digital twin 20850 may requesta more granular view of the selected state(s) from the marketorchestration system platform 20500, which may return the requestedstate(s) at the more granular level.

In embodiments, the marketplace host digital twin 20850 may beconfigured to simulate one or more aspects of the marketplace. Suchsimulations may assist the user in making decisions, including, but notlimited to, marketplace configuration decisions, fee decisions, and/oroperational decisions. For example, simulations of a proposedmarketplace configuration may be tested using the modeling, machinelearning, and/or AI techniques, as described herein, by simulatingmarketplace configuration parameters 20306 (e.g., supported asset types,listing requirements, fees, and the like), simulating economic factors,simulating market participation, and/or other suitablemarketplace-related parameters. In embodiments, the digital twinsimulation system 20804 may receive a request to perform a simulationrequested by the marketplace host digital twin 20850, where the requestindicates one or more parameters that are to be varied in one or moremarket orchestration digital twins. In response, the digital twinsimulation system 20804 may return the simulation results to themarketplace host digital twin 20850, which in turn outputs the resultsto the user via the client device display. In this way, the user may beprovided with various outcomes corresponding to different marketplaceconfiguration parameters. For example, a user may request a set ofsimulations to be run to test different fee strategies to see how thedifferent strategies affect the overall impact on profits. Thesimulation system may perform the simulations by varying the differentfee strategies and may output the profit forecasts for each respectivefee strategy. In some embodiments, the user may select a parameter setbased on the various outcomes and iterate simulations based at least onthe varied prior outcomes. Drawing from the previous example, the usermay decide to select the fee strategy that maximizes profit forecasts.In some embodiments, an intelligent agent may be trained to recommendand/or select a parameter set based on the respective outcomesassociated with each respective parameter set.

In embodiments, a marketplace host digital twin 20850 may be configuredto store, aggregate, merge, analyze, prepare, report, and distributematerial relating to marketplace performance, marketplace activity,traders, brokers, profits, or other data related to a marketplace.

A marketplace host digital twin 20850 may link to, interact with, and beassociated with external data sources, and able to upload, download,aggregate external data sources, including with the MOS's internal data,and analyze such data, as described herein. Data analysis, machinelearning, AI processing, and other analysis may be coordinated betweenthe marketplace host digital twin 20850 and an analytics team based atleast in part on using the intelligent services system 20243. Thiscooperation and interaction may include assisting with seedingmarketplace-related data elements and domains in the digital twin datastore 20838 for use in modeling, machine learning, and AI processing toidentify an optimal marketplace configuration or some othermarketplace-related metric or aspect, as well as identification of theoptimal data measurement parameters on which to base judgment of atrading strategy's success.

In embodiments, a marketplace host digital twin 20850 may be configuredto report on the performance of the marketplace. As described herein,reporting may include financial performance metrics, physicalperformance metrics, data regarding resource usage, or some other typeof reporting data. In embodiments, an intelligent agent trained by theuser may be trained to surface the most important reports to the user.For example, if the user (e.g., the marketplace host) consistently viewsand follows up on execution speed but routinely skips over reportsrelating to liquidity multiple, the executive agent may automaticallysurface reports related to execution speed to the user while suppressingother data.

In embodiments, the client application 20312 that executes themarketplace host digital twin 20850 may be configured with anintelligent agent 20234 that is trained on the marketplace host'sactions (which may be indicative of behaviors and/or preferences). Inembodiments, the intelligent agent 20234 may record the featuresrelating to the actions (e.g., the circumstances relating to the user'saction) to the intelligent agent system 20210. For example, theintelligent agent 20234 may record each time the user configures a newmarketplace (which is the action), as well as the features surroundingthe configuration (e.g., the type of assets supported, anonymity rules,listing requirements, fees, supported trading types, shipping and/ordelivery rules, and the like). The intelligent agent 20234 may reportthe actions and features to the intelligent agent system 20210 that maytrain the intelligent agent 20234 on the manner by which the intelligentagent 20234 can undertake or recommend marketplace configuration tasksin the future. Once trained, the intelligent agent 20234 mayautomatically perform actions and/or recommend actions to the user.Furthermore, in embodiments, the intelligent agent 20234 may recordoutcomes related to the performed/recommended actions, thereby creatinga feedback loop with the intelligent agent system 20210.

In embodiments, a trader digital twin 20842 may provide an interface fora marketplace host to perform one or more marketplace host-relatedworkflows. For example, the marketplace host digital twin 20850 mayprovide an interface for a marketplace to perform, supervise, or monitorregulatory workflows, exchange configuration workflows, and the like.

The market orchestration digital twins may be leveraged and/or mayinterface with other software applications without departing from thescope of the disclosure.

FIGS. 205-207 illustrate embodiments of the market orchestration systemplatform 20500 including a robotic process automation (RPA) system 20502configured to automate internal marketplace workflows based on roboticprocess automation. The RPA system 20502 may develop a programmaticinterface to a user interface of an external system 20504 such asdevices, programs, networks, databases, and the like. The RPA system20502 is configured to allow the market orchestration system platform20500 to interface with an external system 20504 without using anapplication programming interface (API), or in addition to an API. TheRPA system 20502 may develop an action list by watching a user perform atask in a graphical user interface (GUI) and recording the tasks in theaction list. The RPA system 20502 may automate a workflow by repeatingtasks of the action list in the GUI.

In some embodiments, the RPA system 20502 may include and/or communicatewith an RPA AI system 20506 configured to perform robotic processautomation processes. The RPA AI system 20506 may employ one or moremachine learning techniques to develop one or more machine learnedmodels. The machine learned models may be capable of developing,defining, and/or implementing RPA-based programmatic interfaces tofacilitate interfacing of the market orchestration system platform 20500with one or more external devices.

The RPA system 20502 may be necessary for the market orchestrationsystem platform 20500 to communicate with an external system 20504 thatdoes not have an API or that has an outdated API. For example, the RPAsystem 20502 may allow the market orchestration system platform 20500 tointerface with an older external device that does not include an API orthat has an outdated API. The RPA system 20502 may allow the marketorchestration system platform 20500 to interface with an external system20504 similarly to how a user would interface with the external system20504, such as via a user interface of the external system 20504. Insome embodiments, the RPA system 20502 allows the market orchestrationsystem platform 20500 to emulate an action and/or a series of actionsperformable by a user to interface with an external system 20504.Examples of programmatic interfacing by the RPA system 20502 tointerface with an external system 20504 include manipulation of markuplanguage such as HTML, emulating computer mouse movements and/or“clicking on” one or more elements of a user interface, enteringinformation into Tillable fields and submitting the information via aclient program and/or portal, and transmitting digital signals to anexternal system 20504 that appear to be from sent from a user device.

In some embodiments, the RPA system 20502 may be configured tofacilitate communicating with new and/or updated external systems 20504.When a new external system is developed or an external system isupdated, the RPA system 20502 may develop a new and/or updatedprogrammatic interface to facilitate interfacing with the new and/orupdated external system by the market orchestration system platform20500 in a manner that is consistent with interfacing with an outdatedexternal device, i.e., the external device prior to release of the newand/or updated external system. For example, the RPA system 20502 may beconfigured to provide inputs to the outdated external device, provideinputs to the new and/or updated external device, compare relatedoutputs, and adjust inputs to the new and/or updated external devicesuch that the market orchestration system platform 20500 may interfacewith the new and/or updated external device in a manner consistent withhow the market orchestration system platform 20500 interfaced with theoutdated external device.

In some embodiments, the RPA system 20502 may act as an API to outdatedand/or external systems 20504. The RPA system 20502 may be configuredsuch that the market orchestration system platform 20500 is externallyrepresented as having an API capable of interfacing with one or moreexternal devices or otherwise being capable of programmatically handlingsignals transmitted by external devices, wherein the RPA system 20502has developed a programmatic interface for handling such requests otherthan an API. For example, an outdated external system may be configuredto communicate via a series of signals understood by an outdated API.The RPA system 20502 may configure the market orchestration systemplatform 20500 to act as if the market orchestration system platform20500 includes the outdated API.

In some embodiments, the RPA system 20502 may be configured to provide auser interface for use by one or more users of the market orchestrationsystem platform 20500. The RPA AI system 20506 may, by one or moremachine learning methods, create a user interface that allows a user tointerface with one or more components and/or functions of the marketorchestration system platform 20500. The RPA system 20502 may userobotic process automation techniques to operate the user interfacecreated by the RPA AI system 20506. The RPA AI system 20506 maydynamically create and/or adjust the user interface according tovariables such as changing market conditions, new and/or modifiedfunctions of the market orchestration system platform 20500, new and/ormodified conditions of systems external to the market orchestrationsystem platform 20500, and the like. Examples of new and/or modifiedconditions of systems external to the market orchestration systemplatform 20500 may include changes to product offerings, changes toproduct availability, changes to selling and/or buying options, newbuying and/or selling parties participating in a marketplace, and thelike.

In some embodiments, the RPA system 20502 may be configured to performrobotic process automation for multiple market systems in parallel. Forexample, the market orchestration system platform 20500 may beconfigured to manage a plurality of marketplaces, each of which requireinterfacing with users. The RPA system 20502 may manage the plurality ofmarketplaces substantially simultaneously and may compare input commandsand related outputs from each market of the plurality of markets by aplurality of users in parallel. Management may, in one non-limitingexample, include setting exchange rates, such as for trading betweennative currencies of each marketplace, such as among fiat currencies,cryptocurrencies, tokens, in-kind asset exchanges, and other mechanismsof exchange of value. Management may, in another non-limiting example,include identification of discrepancies in value, such as ones thatcreate large arbitrage opportunities across marketplaces, andconfiguring marketplace rules, execution or the like to harmonize themarketplaces or otherwise mitigate adverse effects.

In some embodiments, the RPA system 20502 may be configured to avoiddetection of robotic process automation by systems external to themarket orchestration system platform 20500. Some of the external systems20504 may be designed to attempt to detect when the external system iscommunicating with a system using robotic process automation, such asthe market orchestration system platform 20500. Upon detecting that themarket orchestration system platform 20500 is using robotic processautomation, the external system may restrict, eliminate, or modifycommunication capabilities of the market orchestration system platform20500 with the external system. The RPA system 20502 may emulate humaninterfacing with the external system to “trick” the external system intobelieving that the RPA system 20502 is a human user to avoid detectionof the robotic process automation and avoid restriction or eliminationof communication by the external system. The RPA system 20502 may avoiddetection by, for example, dynamically changing paths of interactionwith the external system, interacting with user interface elements withinconsistent timing, making human-like errors such as “mis clicks” or“typos,” and the like.

In some embodiments, the RPA AI system 20506 may be configured to createa machine learned model for avoiding detection of robotic processautomation. The machine learned model may be created by using data frominteraction with one or more graphical interfaces by real human beingsand developing robotic process automation techniques that emulate waysin which real humans' interface with the one or more graphical userinterfaces. For example, training data may include mouse and/or touchtimings and accuracy, typing speed and accuracy, elements of thegraphical user interface used, and the like.

In some embodiments, the RPA system 20502 may be configured to validatedata transmitted to and/or received from external systems 20504. The RPAsystem 20502 may validate one or more of data transmitted to the marketorchestration system platform 20500 by users of the external system,data transmitted to the market orchestration system platform 20500 byusers of the market orchestration system platform 20500, and/or datatransmitted to the external system by users of the market orchestrationsystem platform 20500. The RPA system 20502 may validate data by one ormore of performing optical character recognition, performing imagerecognition and/or processing, identifying data stored on webpages,receiving data from a backend database of the external system, receivingdata from a backend database of the market orchestration system platform20500, and the like.

In some embodiments, the RPA AI system 20506 may be configured todevelop one or more machine learned models for data validation. Forexample, the RPA AI system 20506 may use data transmitted by usersand/or data received from one or more databases and/or sources externalto the market orchestration system platform 20500 as training data to“learn” to identify valid data. The RPA AI system 20506 may transmit theone or more machine learned models for data validation to the roboticprocess automation system 20502. The robotic process automation system20502 may implement the one or more machine learned models for datavalidation.

In some embodiments, the RPA system 20502 may be configured tofacilitate validation of processes performed by the RPA system 20502.The RPA system 20502 may create a plurality of process validation logsas the RPA system 20502 performs one or more processes related to themarket orchestration system platform 20500 and/or external systems 20504on behalf of one or more users. The process validation logs may includeone or more of timestamps, transaction receipts, user interfacescreenshots, or any other suitable data entry, file, and the like forproviding validation of processes performed by the RPA system 20502. TheRPA system 20502 may store the process validation logs in one or moredatabases and may transmit the process validation logs to the marketorchestration system platform 20500 and/or users of the marketorchestration system platform 20500. The RPA system 20502 may transmitthe process validation logs automatically according to a schedule, upondemand by a user of the market orchestration system platform 20500, uponone or more conditions being true, and the like.

In some embodiments, the RPA system 20502 may be configured to adjustbehavior of the robotic process automation in response to feedbackacquired via one or both of data validation and process validation. Auser of the market orchestration system platform 20500 may viewvalidations of data provided by the RPA system 20502 and, in response tothe validations of data, instruct the RPA system 20502 to adjustbehavior of the robotic process automation system 20502. A user of themarket orchestration system platform 20500 may view one or more of theprocess validation logs and, in response to the one or more processvalidation logs, instruct the RPA system 20502 to adjust behavior of therobotic process automation system 20502. Adjustment of behavior of theRPA system 20502 may include using different robotic process automationtechniques to perform features of the RPA system 20502, such as, forexample, changing RPA-based user interface elements presented to usersof the market orchestration system platform 20500, adjusting how the RPAsystem 20502 interfaces with one or more external systems 20504, and anyother suitable adjustment by the RPA system 20502.

In some embodiments, the RPA AI system 20506 may use data validationinformation and/or feedback, process validation logs, or a combinationthereof as training data. The RPA AI system 20506 may train one or moremachine learned models to influence, adjust, and/or otherwise controlbehavior of the RPA system 20502 based upon the data validationinformation and/or feedback, process validation logs, or a combinationthereof.

In some embodiments, the RPA system 20502 may be configured to performimage processing to recognize images in graphical user interfaces withwhich the RPA system 20502 interfaces. Graphical user interfaces ofexternal systems 20504 with which the RPA system 20502 interfaces may bechanged and/or updated, thereby potentially disrupting robotic processautomation-based interfacing with the GUI. The RPA system 20502 mayautomatically detect changes to the GUI via image recognition and/orimage processing. The RPA system 20502 may automatically update roboticprocess automation-based interfacing with the updated GUI to facilitatecontinued interfacing with the updated GUI and avoid errors orinterruptions in communication with the external system.

In some embodiments, the RPA AI system 20506 may use image processoptimization to use one or more machine learned models to automaticallycorrect robotic process automation-based interfacing with the externalsystem of the RPA system 20502. For example, the RPA AI system 20506 mayuse a plurality of GUIs having images as training data to create amachine learned model capable of automatically detecting changes in GUIsof external systems 20504 and determining how to adjust robotic processautomation of the RPA system 20502 such that the RPA system 20502 mayautomatically continue interfacing with the GUI in light of a change tothe GUI.

In some embodiments, the RPA system 20502 may be configured to develop ahuman training system for instructing humans to interface with one ormore user interfaces of the market orchestration system platform 20500and/or one or more external systems 20504. The human training system mayteach one or more human users a plurality of actions and/or techniquesemployed by the RPA system 20502 to interface with the one or more userinterfaces such that the human users may perform tasks similarly to theRPA system 20502. The human training system may include one or moredocuments, videos, tutorials, and the like for facilitating humanlearning of actions and/or techniques for interfacing with the userinterfaces.

In some embodiments, the RPA system 20502 may be configured to processand document success criteria of robotic process automation implementedby the RPA system 20502. The processed and documented success criteriais descriptive such that a human user of the market orchestration systemplatform 20500 and/or the RPA system 20502 may use the processed anddocumented success criteria to understand one or more process stepsand/or algorithms used by the RPA system 20502 to facilitate interfacingwith external systems 20504 and/or to automate internal marketplaceworkflows of the market orchestration system platform 20500.

In some embodiments, the RPA system 20502 may implement gamification ofrobotic process automation capabilities of the market orchestrationsystem platform 20500. The gamification of robotic process automationcapabilities may include awarding points to users for performing tasksdesirable to operation of the market orchestration system platform 20500and/or desirable for improvement of robotic process automationoperations of the market orchestration system platform 20500. Forexample, points may be awarded for augmentation of a robotic processautomation algorithm. Users who have been awarded points may competewith one another, and digital and/or physical prizes may be awarded tousers who have achieved one or more point thresholds and/or have rankedabove one or more other users on a points leaderboard.

FIG. 206 illustrates embodiments of the market orchestration systemplatform 20500 including an edge device 20292 configured to perform edgecomputation and intelligence. In some embodiments, edge computation andintelligence may include performing one or both of data processing anddata storage in an area that is physically close to where the processedand/or stored data is needed. In some embodiments, the marketorchestration system platform 20500 may include a plurality of edgedevices 20292. By way of example, the edge device 20292 may be a router,a routing switch, an integrated access device, a multiplexer, a localarea network (LAN) and/or wide area network (WAN) access device, anInternet of Things device, and/or any other suitable device. In someembodiments, edge computation and intelligence may include performingdata processing and/or data filtering. The processed and/or filtereddata may be transmitted directly to devices that will use the processedand/or filtered data. The processed and/or filtered data may betransmitted along transmission paths with less congestion thangeneral-purpose or high-traffic data transmission paths. Transmission ofthe processed and/or filtered data may use lower bandwidth than wouldtransmission of unprocessed and/or unfiltered data.

In some embodiments, the edge device 20292 may implement local edgeintelligence to anticipate market-driving factors using data received byand/or stored by the edge device 20292. The edge device 20292 may bedirected to collecting and processing data related to one or more of aparticular buyer and/or seller, product, class of products, class ofbuyers and/or sellers, market, class of markets, and the like. In someembodiments, the edge device 20292 may be situated physically near to aremote market and/or trading area. For example, the edge device 20292may be positioned and configured to collect data regarding transactionsrelated to a particular type of product in a geographical region. Theedge device may perform data processing, analytics, filtering, trendfinding, prediction making, etc. related to the data and may sendprocessing results, analytics, filtered data, trends, predictions, etc.or portions thereof to a more centralized server, processor and/or datacenter within the market orchestration system platform 20500.

In some embodiments, the edge device 20292 may be configured to performdecision making while being physically and/or electronically isolatedfrom some or all other components of the market orchestration systemplatform 20500. Herein, electronic isolation may mean or include beingtemporarily unable to communicate with one or more other systems,devices, components, etc. The edge device 20292 may make decisions basedupon outputs and/or conclusions drawn from the data processing,analytics, filtering, trend finding, prediction making, etc. related todata received by the edge device 20292. Examples of decisions made bythe edge device 20292 include whether to validate one or more pieces ofdata, whether to validate a user of the market orchestration systemplatform 20500 or a portion thereof, whether a transaction has beenperformed, whether to perform a transaction, and the like. The edgedevice 20292 may transmit data related to decisions made by the edgedevice 20292 to other components of the market orchestration systemplatform 20500.

In some embodiments, in cases where the edge device 20292 is temporarilyelectronically isolated from other components of the marketorchestration system platform 20500, the edge device 20292 may makedecisions on behalf of other components of the market orchestrationsystem platform 20500, and may have the decisions audited, evaluated,and/or recorded by other components of the market orchestration systemplatform 20500 upon being reconnected with the other components of themarket orchestration system platform 20500. The edge device 20292 may berestricted from making some decisions in absence of connection to and/oroversight by other components of the market orchestration systemplatform 20500. Examples of restricted decisions may include decisionsrelated to transactions where confidentiality and/or security are ofconcern, where intellectual property, trade secrets, and/or proprietaryinformation are to be transmitted, and the like.

In some embodiments, the edge device 20292 may store a copy of adistributed ledger, the distributed ledger containing informationrelated to one or more marketplaces and/or transactions managed by themarket orchestration system platform 20500. The distributed ledger maybe a cryptographic ledger, such as a blockchain. The edge device 20292may write blocks to the distributed ledger containing marketorchestration information and may have the blocks verified by comparisonwith copies of the distributed ledger stored on other components of themarket orchestration system platform 20500.

In some embodiments, the distributed ledger may be configured to manageownership of property such as physical goods, digital goods,intellectual property, and the like. An initial owner of property, suchas a seller, may be recorded in a block of the distributed ledger. Thedistributed ledger may record changes in ownership as ownership of theproperty changes, such as from a seller to a buyer, from a manufacturerto a retailer to a buyer, etc.

In some embodiments, the market orchestration system platform 20500 mayinclude a ledger management system configured to manage a network ofdevices, such as edge devices, that store copies of the distributedledger. The devices that store copies of the distributed ledger may beconfigured to transmit copies stored thereon to the ledger managementsystem for aggregation, comparison, and/or validation. The ledgermanagement system may establish a whitelist of trusted parties and/ordevices, a blacklist of untrusted parties and/or devices, or acombination thereof. The ledger management system may assign permissionsto particular users, devices, and the like. Versions of the distributedledger may be compared to prevent duplicate transactions such as thesale of multiple copies of a unique good. In embodiments, where themarket orchestration system platform 20500 includes a plurality of edgedevices 20292, edge devices 20292 of the plurality of edge devices 20292may each store a copy of the distributed ledger and may compare copiesagainst one another with respect to validation of blocks and addition ofnew blocks by some and/or all of the edge devices 20292.

In some embodiments, the market orchestration system platform 20500 mayimplement one or more distributed update management algorithms forupdating distributed devices such as the edge device 20292. Thedistributed update management algorithm may include one or moreprocedures for how and when to roll out updates to the distributeddevices. The market orchestration system platform 20500 may manageversions of market orchestration and/or edge computation software viathe distributed update management algorithms. The distributed devicesmay receive updates directly from the market orchestration systemplatform 20500, may transmit updates to one another, or a combinationthereof.

In some embodiments, wherein the market orchestration system platform20500 includes a plurality of edge devices 20292, the edge devices 20292may communicate with one another to record and/or validate transactions.The edge devices 20292 may also communicate data with one anotherrelated to one or more markets, products, regions, users, traders,buyers, sellers, third parties, and the like. An edge device 20292 ofthe plurality of edge devices 20292 may communicate such informationwhen able in cases where an edge device 20292 is electronically isolatedfrom other edge devices 20292 and/or other components of the marketorchestration system platform 20500.

In some embodiments, a first edge device 20292A that is electricallyisolated and is assigned to orchestrate a particular trade and/or marketmay be supported by a second edge device 20292B. The second edge device20292B may be assigned to orchestrate the same particular trade and/ormarket in case the first edge device 20292A fails to orchestrate thetrade and/or market and/or is out of communication with other componentsof the market orchestration system platform 20500 for an extended periodof time such that orchestration of the trade and/or market by the firstedge device 20292A is unverifiable. Upon reentering communication range,the first edge device 20292A may update the second edge device 20292Band/or other components of the market orchestration system platform20500 with transactions and/or other orchestration operations that tookplace while the first edge device 20292A was electronically isolated.Similarly, edge devices 20292C, 20292D may act as supports for otheredge devices.

In some embodiments, the market orchestration system platform 20500 mayimplement a hardware failure algorithm configured to make decisions whenone or more components of the market orchestration system platform20500, such as the edge device 20292, ceases operation and/or isotherwise unable to completely operate properly. The hardware failurealgorithm may include, for example, assigning an edge device 20292 toovertake operations that had been previously assigned to a nowmalfunctioning or nonfunctioning edge device 20292.

In some embodiments, the market orchestration system platform 20500 mayimplement a data routing algorithm configured to optimize flow of datatransmitted to and/or from the edge device 20292, other components ofthe market orchestration system, external systems 20504, or acombination thereof. The edge device 20292 may include one or moresignal amplifiers, signal repeaters, digital filters, analog filters,digital-to-analog converters, analog-to-digital converter, and/orantennae configured to optimize the flow of data. In some embodiments,the network enhancement system may include a wireless repeater systemsuch as is disclosed by U.S. Pat. No. 7,623,826 to Pergal, the entiretyof which is hereby incorporated by reference. The edge device 20292 mayoptimize the flow of data by, for example, filtering data, repeatingdata transmission, amplifying data transmission, adjusting one or moresampling rates and/or transmission rates, and implementing one or moredata communication protocols. In embodiments, the edge device 20292 maytransmit a first portion of data over a first path of the plurality ofdata paths and a second portion of data over a second path of theplurality of data paths. The edge device 20292 may determine that one ormore data paths, such as the first data path, the second data path,and/or other data paths, are advantageous for transmission of one ormore portions of data. The edge device 20292 may make determinations ofadvantageous data paths based upon one or more networking variables,such as one or more types of data being transmitted, one or moreprotocols being suitable for transmission, present and/or anticipatednetwork congestion, timing of data transmission, present and/oranticipated volumes of data being or to be transmitted, and the like.Protocols suitable for transmission may include transmission controlprotocol (TCP), user datagram protocol (UDP), and the like. In someembodiments, the edge device 20292 may be configured to implement amethod for data communication such as is disclosed by U.S. Pat. No.9,979,664 to Ho et al., the entirety of which is hereby incorporated byreference.

In some embodiments, the edge device 20292 may implement a hostiletrading detection algorithm configured to detect and protect the marketorchestration system platform 20500 against external systems 20504attempting to fraudulently interact with the market orchestration systemplatform 20500. Examples of attempting to fraudulently interact with themarket orchestration system platform 20500 include submitting falsetransaction information, false product information, false user and/orparty information, attempting to make the market orchestration systemplatform 20500 perform and/or orchestrate a fraudulent transaction, andthe like. The hostile trading detection algorithm may be implemented viaa machine learning model trained to detect and/or protect againstfraudulent interaction.

In some embodiments, the edge device 20292 may implement gamification ofdistributed computing capabilities of the market orchestration systemplatform 20500. The gamification of distributed computing capabilitiesmay include awarding points to users for performing tasks desirable tooperation of the market orchestration system platform 20500 and/ordesirable for improvement of robotic process automation operations ofthe market orchestration system platform 20500. For example, points maybe awarded for trading goods of a particular type and/or within aparticular region. Users who have been awarded points may compete withone another, and digital and/or physical prizes may be awarded to userswho have achieved one or more point thresholds and/or have ranked aboveone or more other users on a points leaderboard.

FIG. 207 illustrates embodiments of the market orchestration systemplatform 20500 including a digital twin system 20208 configured toreceive data from the edge device 20292 and create a digital replica,i.e., a digital twin, from the received data. The digital replicacreated by the digital twin system 20208 may be a digital replica of oneor more of a market, a product, a seller, a buyer, a transaction, andthe like and may be created using any or all of the data received fromthe edge device 20292. The edge device 20292 may transmit marketorchestration-related data, such as data related to a market, a product,a seller, a buyer, a transaction, and the like, or a combinationthereof. In embodiments, where the market orchestration system platform20500 includes a plurality of edge devices 20292, the digital twinsystem 20208 may create the digital replica based on data received frommultiple of the plurality of edge devices 20292.

In some embodiments, the digital twin system 20208 may be configured topresent a simulation of a market, a product, a seller, a buyer, atransaction, or a combination thereof via the digital replica. Thedigital replica may be a two-dimensional or three-dimensional simulationof a market, a product, a seller, a buyer, a transaction, and the like.The digital replica may be viewable on a computer monitor, a televisionscreen, a three-dimensional display, a virtual-reality display and/orheadset, an augmented reality display such as AR goggles or glasses, andthe like. The digital replica may include one or more graphicalcomponents. The digital replica may be configured to be manipulated by auser of the market orchestration system platform 20500. Manipulation bythe user may allow the user to view one or more portions of the digitalreplica in greater or lesser detail. In some embodiments, the digitaltwin system 20208 may be configured such that the digital replica maysimulate one or more potential future states of a market, a product, aseller, a buyer, a transaction, etc. The digital replica may simulatethe one or more potential future states of a market, a product, aseller, a buyer, a transaction, etc. based on simulation parametersprovided by the user. Examples of simulation parameters include aprogression of a period of time, potential actions by parties such asbuyers or sellers, increases in supply and/or demand of products,resources, etc., changes in government regulations, and any othersuitable parameter for simulation of a market or related to marketorchestration.

In some embodiments, the edge device 20292 may be configured tofacilitate pre-calculation and aggregation of data for a set ofuser-configured reports. The user-configured reports may be integratedinto the digital replica created by the digital twin system 20208. Auser of the market orchestration system platform 20500 may define one ormore parameters of the user-configured report to be included in thedigital replica. The edge device 20292 may implement one or more dataprocessing and/or filtering according to the parameters of theuser-configured report. The edge device 20292 may transmit processedand/or filtered data relevant to the user-configured report parametersto the digital twin system 20208. Upon receiving the processed and/orfiltered data, the digital twin system 20208 may create the digitalreplica including the user-configured report using the received data andpresent the digital replica to the user.

Referring to FIG. 205 , in some embodiments, the edge device 20292 maybe configured to collect and process data for use by one or moreartificial intelligence (AI) systems. The AI systems 20508 may includethe RPA AI system 20506, one or more artificial intelligence systemsconfigured to facilitate creation of the digital replica by the digitaltwin system 20208, and/or any other artificial intelligence systemsconnected to and/or included in the market orchestration system platform20500. The edge device 20292 may be configured to collect and processand/or filter data such that the data is suitable for use by the one ormore AI systems 20508. An example of processed and/or filtered datacollected by the edge device 20292 for use by the one or more AI systems20508 is training data for use in training one or more machine learnedmodels.

In some embodiments, the edge device 20292 may be configured to locallystore data related to creation of the digital replica by the digitaltwin system 20208. In cases where the digital replica is related to aparticular region, seller, buyer, market, business, etc., the edgedevice 20292 may be particularly positioned to collect and store datafor use in populating the digital replica, for example, by beingpositioned nearby to the particular region, seller, buyer, market,business, etc. The edge device 20292 may receive, process, filter,organize, and/or store data prior to transmission of the data to thedigital twin system 20208 such that the data is relevant to and/orsuitable for population of the digital replica. In some embodiments, theedge device 20292 may be configured to organize timing of transmissionof data used to populate the digital replica. The edge device 20292 mayimplement one or more algorithms configured to measure and/or predictcongestion of one or more network paths and/or routes and may performorganization of timing of transmission data based on the measurementsand/or predictions of the congestion. The edge device 20292 may in somecases prioritize transmission of some types of data over others, such asaccording to priorities set by a user or by the digital twin system20208. For example, the edge device 20292 may schedule regulartransmissions of low-priority information during evening hours, whencongestion is low, and may transmit high-priority informationsubstantially immediately upon receiving the high-priority informationand/or receiving a request for the high-priority information. In someembodiments, the edge device 20292 may be configured to select a dataprotocol for transmission of data used to populate the digital replica.The edge device 20292 may implement one or more algorithms configured toselect one or more optimal network paths and/or routes and may selectthe data transmission protocol based on the measurements and/orpredictions of the congestion.

In some embodiments, the edge device 20292 may be in communication withand receive data from a plurality of sensors. The edge device 20292 maybe configured to intelligently multiplex alternative sensors amongavailable sensors in a shipping environment for the digital replica.

In embodiments, the intelligent services system 20243 provides aframework for providing intelligence services to the marketorchestration system platform 20500. In some embodiments, theintelligent services system 20243 framework may be adapted to be atleast partially replicated in the market orchestration system platform20500. In these embodiments, intelligence service clients 8836,including the market orchestration system platform 20500, may includesome or all of the capabilities of the intelligent services system20243, whereby the intelligent services system 20243 is adapted for thespecific functions performed by the market orchestration system platform20500. Additionally or alternatively, in some embodiments, theintelligent services system 20243 may be implemented as a set ofmicroservices, such that the market orchestration system platform 20500may leverage the intelligent services system 20243 via one or more APIsexposed to the market orchestration system platform 20500. In theseembodiments, the intelligent services system 20243 may be configured toperform various types of intelligence services that may be adapted fordifferent intelligence clients 8836. In either of these configurations,the market orchestration system platform 20500 and/or other intelligenceservice clients 8836 may provide an intelligence request to theintelligent services system 20243, whereby the request is to perform aspecific intelligence task (e.g., a decision, a recommendation, areport, an instruction, a classification, a prediction, a trainingaction, an NLP request, or the like). In response, the intelligentservices system 20243 executes the requested intelligence task andreturns a response to the intelligence service client 8836. Additionallyor alternatively, in some embodiments, the intelligent services system20243 may be implemented using one or more specialized chips that areconfigured to provide AI assisted microservices such as imageprocessing, diagnostics, location and orientation, chemical analysis,data processing, and so forth.

In embodiments, an artificial intelligent services system 20243 receivesan intelligence request and any required data to process the requestfrom the market orchestration system platform 20500. In response to therequest and the specific data, one or more implicated artificialintelligence system perform the intelligence task and output an“intelligence response” to the market orchestration system platform20500.

In embodiments, the market orchestration system platform 20500 mayprovide access to and/or integrate artificial intelligence system, whichmay provide access to and/or integrate a robotic process automation(RPA) module 8816. The RPA module may facilitate, among other things,computer automation of producing and validating market orchestrationworkflows (e.g., transactional workflows, market configurationworkflows, regulatory workflows, and many others). The RPA moduleprovides automation of tasks performed by humans, such as reviewingoffers (e.g., offers to sell, offers to buy, and many others),researching assets and/or services listed in a marketplace, executingtransactions (e.g., approving a set of transactions, shopping, buying,selling, trading, making payments, receiving payments, and many others)and/or otherwise participating in a marketplace (e.g., disapproving aset of transactions, listing items for sale, negotiating with buyersand/or sellers, and the like), managing a financial account and/ordigital wallet, identifying assets that could be listed for sale, lease,or the like (e.g., items that have not been used, collectibles that haveincreased significantly in value, items produced by humans, itemsproduced by 3D printers, and many others), identifying resources thatcould be listed for sale, lease, or the like (e.g., use of a vacationhome, excess energy collected from solar panels, use of an otherwiseidle fleet of robots, and many others), identifying the need for anasset and/or resource (e.g., a grocery item, an outfit for an upcomingevent, a replacement part for a vehicle, a haircut, and many others),scheduling services (e.g., appointments, reservations, and the like),receiving and reviewing written information, reviewing receipts,entering data into user interfaces, converting or otherwise processingdata such as files or records, recording observations, generatingdocuments such as reports or tax filings, communicating with other usersby mechanisms such as email, tracking a package, detecting and/orresolving transactional issues (e.g., incorrect amount of money charged,incorrect amount of money received, nonpayment, incorrect itemsreceived, damage to received items, partial performance of a service,problems with shipment, fraud, and the like), and many others.

As an example, a user may receive an offer for a transaction, such as apurchase or sale of goods or services. The RPA module may receive theoffer on behalf of the user and respond to the transaction based oninformation regarding how the user has previously responded to similaroffers. As a first example, the RPA module may determine whether theuser has previously considered similar offers (e.g., by opening orreading a message regarding an offer, or acting on the message, such asfollowing a hyperlink included in the message) or has declined toconsider similar offers (e.g., by deleting the message, declining toread the message, or declining to act on the message). Upon receiving anoffer that is similar to previous offers, the RPA module may determinethat the user may consider the offer and therefore automatically sharethe message with the user, or determine that the user is unlikely toconsider the offer and therefore automatically discard the message. As asecond example, the RPA module may determine the steps that a user haspreviously taken to consider similar offers, such as searching for moreinformation about a product or service associated with an offer, readingthird-party reviews of a product or service associated with an offer,discussing the offer with another person, or assessing the user'scurrent inventory or patronage of a product or service associated withthe offer. Upon receiving an offer that is similar to previous offers,the RPA module may automatically perform some of the steps that the userhas previously taken to aid the consideration of the offer by the user,such as automatically retrieving and presenting to the user someadditional information about a product or service associated with theoffer, automatically suggesting or initiating a discussion between theuser and another person regarding the offer, or automatically notifyingthe user of the user's current inventory or patronage of a product orservice associated with the offer. As a third example, the RPA modulemay determine the steps that a user has previously taken upon decidingto accept an offer, such as responding to the message, initiating afinancial exchange associated with the offer, recording the transactionin a data source, allocating capacity for the good or service associatedwith the offer, or creating an entry in a calendar regarding a deliveryof the good or provision of the service associated with the offer. Upondetermining an acceptance of the offer by the user, the RPA module mayautomatically take some steps to accept or perform the offer, such asautomatically responding to the message, automatically initiating thefinancial transaction, automatically creating a record of thetransaction in the data source, automatically allocating capacity forthe good or service, or automatically updating the user's calendar tocreate an entry for the delivery of the good or the provision of theservice. In some cases, the RPA module may suggest some of theaforementioned steps to a user, and then perform the steps on behalf ofthe user upon receiving confirmation from the user. Alternatively oradditionally, the RPA module may perform some of the aforementionedsteps on behalf of the user without soliciting and receivingconfirmation from the user.

In some cases, the tasks involve a workflow that includes a number ofinterrelated steps, contextual information that relates to the task, andinteractions with other applications and humans. The RPA module can beconfigured to receive or learn one or more such workflows on behalf ofthe human and in a manner similar to the actions and logic of the human,and can thereafter perform such workflows in response to varioustriggers such as events. For example, the RPA module can be configuredto receive or learn one or more workflows related to approving atransaction (e.g., authorizing payment and the like) on behalf of thehuman and in a manner similar to the actions and logic of the human inresponse to receiving an offer for the sale of a set of assets. As afirst example, the RPA module may receive, from a user, a request toobserve and record a set of steps by which the user receives, considers,accepts, and/or performs an offer for the sale of a set of assets. TheRPA module may record the steps performed by the user and replicate thesteps upon request of the user in regard to another transaction, or mayautomatically perform the steps to process another transaction in asimilar manner. As a second example, the RPA module may receive, from auser, a set of instructions by which the RPA module can receive,process, and complete a transaction. In some embodiments, theinstructions are provided by the user through a no-code or low-codegraphical user interface, such as a block-based development environmentin which the user arranges a sequence of blocks to specify a sequence ofinstructions for performing a task associated with an offer. Theinstructions can comprise a script that can be executed by the RPAmodule to perform a workflow associated with the task. At the request ofthe user to process a subsequent offer, the RPA module can perform theinstructions of the workflow to process the offer in the mannerspecified by the user. Alternatively or additionally, upon theoccurrence of a condition or trigger involving an offer (e.g., a receiptof the offer through a messaging channel and/or a determination of apersonal need of the user for a good or service), the RPA module canperform the instructions of the workflow to process or initiate theoffer in the manner specified by the user. As a third example, the RPAmodule may observe a set of actions taken by a user over a period oftime. The RPA module may group one or more actions taken by the userinto one or more workflows that are associated with an offer (e.g., apattern of actions that the user takes in response to a condition ortrigger that is associated with an offer). Upon receiving or identifyinganother offer that is similar to previous offers handled by the user,the RPA module may perform a similar set of actions to process theorder.

In some embodiments, the RPA module performs a workflow in cooperationwith a human or another workflow. For example, a workflow can includeone or more human portions to be performed by a human and one or moreautomated portions to be performed by the RPA module. In someembodiments, the RPA module can first perform an automated portion anddeliver a result of the automated portion to the human so that the humancan perform a human portion based on the result. In some embodiments,the RPA module can receive a result of a human portion of the workflowand can perform an automated portion of the workflow on the result ofthe human portion of the workflow. In some embodiments, the RPA modulecan perform a first automated portion of the workflow, present a resultof the first automated portion to a human for review and validation, andcan perform a second automated portion of the workflow based on thereview and validation of the result of the first automated portion basedon a result of the review and validation by the human. For example, theRPA module may review an offer for sale of an asset and deliver arecommendation to proceed with the asset purchase to the human, and upontransaction authorization by the human, the RPA module may then executethe asset purchase by entering payment information into an applicationuser interface and arrange for delivery of the asset. In someembodiments, the RPA module may coordinate with another person and/oranother RPA module to complete a task associated with an offer. Forexample, in order to complete a transaction on behalf of a user, the RPAmodule may communicate with a partner, colleague, or team member of theuser, such as sending a request to initiate or complete a financialtransaction related to the otter. Alternatively or additionally, the RPAmodule may communicate with another RPA module to initiate or complete afinancial transaction related to the offer, such as communicating withanother RPA module that can authorize the financial transaction onbehalf of a partner, colleague, or team member of the user.

In embodiments, the RPA module may leverage the artificial intelligencesystem to determine when user approval of a transaction is required. Forexample, user approval may be necessary for purchases over a thresholdamount, for non-routine purchases, for the resale and listing ofcollectibles, and/or the scheduling of appointments, while user approvalmay not be necessary for inexpensive purchases, rules-based purchases(e.g., rules specified by the human user), and/or routine purchases. Inembodiments, the RPA module can generate an output communicated to oneor more users (e.g., a visual notification displayed for a user of adigital twin, a visual notification displayed for a user of a digitalwallet, a visual notification displayed for a user of a device (e.g., amobile device, a wearable, an augmented reality headset, a virtualreality headset, an IoT device, or the like), or a message that istransmitted to a user by a communication channel such as email, textmessage, or voice output). In embodiments, the output may include a GUIsuch as to enable the user to approve a transaction.

In some embodiments, the RPA module may use one or more machine learningalgorithms to perform one or more steps associated with an offer for atransaction. As a first example, the RPA module may train a machinelearning model to learn the steps by which the user receives, considers,or completes transactions that are associated with offers fortransactions. In some cases, the RPA module may use a patternrecognition machine learning model to associate a pattern of actionstaken by a user with a workflow that the user performs in regard to aparticular type of offer. The RPA module may use such a trained machinelearning model to receive, consider, or complete a transactionassociated with an offer on behalf of the user. As a second example, theRPA module may use a natural-language processing (NLP) machine learningmodel to understand natural-language communication that is associatedwith an offer, such as a natural-language description of the offer by avendor of a product or a service provider of a service, or to evaluateone or more natural-language reviews of a product or service associatedwith an offer. The RPA module may use information extracted from suchnatural-language communication in order to receive, consider, orcomplete a transaction associated with the offer on behalf of the user.As a third example, the RPA module may use a natural-language synthesismachine learning model to generate natural-language communication inorder to generate communication with the user or other individualsassociated with an offer. For example, the RPA module may suggest, tothe user, a natural-language message that can be sent to accept orperform an offer or to communicate with another individual regarding theoffer. Upon receiving acceptance by the user, the RPA module may sendthe natural-language message to another user as part of theconsideration, acceptance, and/or performance of the offer. As a fourthexample, the RPA module may use one or more control-based machinelearning models to perform an offer on behalf of the user. Inembodiments, after completing a financial transaction regarding aproduct, the RPA module may control one or more devices, robots,autonomous vehicles, or the like to receive, deliver, transport,install, configure, and/or use the product on behalf of the user.

In some embodiments, the RPA module may interface with an extendedreality (XR) environment, such as an augmented reality (AR) environmentor a virtual reality (VR) environment, in order to suggest, evaluate,accept, and/or perform an offer on behalf of a user. As a first example,upon receiving an offer and determining that the offer might be ofinterest to the user, the RPA module may present an indicator of theoffer to the user within the XR environment, such as a location markerto indicate a location where a product or service associated with theoffer is available. As a second example, the RPA module may generate,within the XR environment, one or more indicators of information aboutthe offer, such as a depiction of a product or a result of a service(e.g., altering an avatar of the user or another individual to depictthe avatar wearing a garment that is associated with the offer, orupdating a visual depiction of a virtual environment of the user toinclude a piece of furniture that is associated with the offer). As athird example, the RPA module may initiate and/or conduct, within the XRenvironment, an acceptance or performance of a transaction associatedwith an offer, such as exchanging a virtual currency with anotherindividual, and presenting a depiction or indication of the exchange ofvirtual currency with the other individual within the XR environment.

In embodiments, the market orchestration system platform 20500 includesa policy engine that allows edge-network aware policy execution fortrade instances. The policy engine may be an interface and systemconfigured to enable a user to design, configure, and deploy a set ofpolicies, which can be associated and/or linked to a workload (e.g.,execution of a trade, discovery of a counterparty, and the like) suchthat the workload only completes execution if the set of policies aresatisfied. A policy could state, for example, that no trades may beexecuted with a certain counterparty beyond a threshold volume per day,and trades with that counterparty would execute until the thresholdvolume value was reached for that day. In trading, and particularly highfrequency trading where a trade is determined by an algorithm (i.e.,based on a strategy dictated by a trader and/or trading organization),timing can be critical. Latency in communication networks and other ITinfrastructure can allow faster traders to front run, which negativelyaffects outcomes for slower traders. A policy associated with a tradingworkload could call on an edge device to automatically test a networkconnection for latency (e.g., from the closest edge node to the ITinfrastructure of the exchange) and execute a trading workload only iflatency is below a threshold value. Other example policies includecounterparty blacklists, counterparty whitelists, asset blacklists,asset whitelists, buying an asset only when the price of that asset isin a specified range, only trading when volatility is below a specificthreshold, only trading when market liquidity is above a specificthreshold, only trading certain times of day, only trading whenlikelihood of execution is above a threshold value, and many others.

In embodiments, the market orchestration system platform 20500 mayprovide access to and/or integrate artificial intelligence system, whichmay be configured to recognize and/or understand the stage of amarketplace and automatically output a set of control instructionsrelated to configuration of the marketplace based at least in part onthe recognized and/or understood stage, including any of the parametersof configuration of the marketplace or any other marketplace parametersnoted throughout this disclosure.

In some embodiments, the marketplace stages may be defined by economiccycle theories (e.g., Kondratiev Wave, Elliott Wave Supercycle, and manyothers).

In some embodiments, the marketplace stages may include the followingstages: a new marketplace stage, a growth marketplace stage, a maturemarketplace stage, and a distressed marketplace stage. In theseembodiments, a new marketplace stage may describe the state of amarketplace that has just been launched. A new marketplace stage mayhave extremely low transaction activity and/or number of participants. Agrowth marketplace stage may describe the state of a marketplace wherethe number of participants and/or the number of transactions are rapidlyincreasing. A mature marketplace may describe the state of a marketplacewhere the number of participants and/or the number of transactions havestabilized. A distressed marketplace may describe the state of amarketplace where the number of participants and/or the number oftransactions are decreasing.

In embodiments, an artificial intelligent services system 20243 receivesan intelligence request and any required data to process the requestfrom the market orchestration system platform 20500. In response to therequest and the specific data, one or more implicated artificialintelligence system perform the intelligence task and output aclassification (e.g., a classification of a marketplace stage) and a setof control instructions for the market orchestration system platform20500 based on the classification. In some examples, the set of controlinstructions for new marketplace stage classifications and/or growthmarketplace stage classifications may include deploying an intelligentagent that automatically generates small transactions to maintaintransactional activity, deploying an offering engine that orchestratesparticipation by parties in other similar markets, deploying an enginefor dynamically pricing transactions that decreases trading fees,deploying an intelligent agent to promote engagement by low-activityaccounts, deploying an engine for automatically discovering and linkingofferings from other marketplaces for presentation in the marketplace,deploying an engine for automatically increasing party engagement (suchas by incentives for first trades), adjusting various marketplaceconfiguration parameters, and many others. In embodiments, the set ofcontrol instructions for mature marketplace stage classifications mayinclude deploying an engine for dynamically pricing transactions thatincreases trading fees, detecting distribution of executed trades by IPaddress and/or party relative to a theoretical “fair” distribution toidentify competitive execution advantages and apply automated delay to asubset of trades by that address and/or party, deploying an intelligentagent to promote engagement by low-activity accounts, automaticallypricing and/or eliminating inactive accounts, deploying a governanceengine that dynamically imposes trading limits on parties, adjustingmarketplace configuration parameters to optimize marketplace efficiency,deploying a gamification engine to promote engagement by accounts (e.g.,reward animations, emphasis on trending assets, lottery incentives,visual and/or auditory feedback rewarding users with color, movement,and sound, badges, haptics, points, rankings, gamified imagery, charms,and the like) and many others. In embodiments, the set of controlinstructions for distressed marketplace stage classifications mayinclude managing the termination of the marketplace, transforming themarketplace (e.g., by offering a new product or service), and manyothers.

In some examples, the intelligent services system 20243 may receive datafrom various sources described throughout this document and thedocuments incorporated by reference herein and may generate a set offeature vectors based on the received data. The intelligent servicessystem 20243 may input the set of feature vectors into a machine-learnedmodel (e.g., using a combination of simulation data and real-world data)to categorize or classify marketplace stage and generate a set ofcontrol instructions based at least in part on the marketplace stageclassification by a set of human experts (such as by the user) and/or bythe other systems or models. Data sources and feature vectors used forthe categorization or classification of marketplace stage and thegeneration of control instructions may include user activity data, userprofile data, transaction data, marketplace age data, as well asexternal data sources (transaction data relating to similar marketplacesor user data relating to similar marketplaces), and many others. Suchartificial intelligence systems used for classification orcategorization and/or generation of control instructions, in the presentexample and other examples described herein, may include a recurrentneural network (including a gated recurrent neural network), aconvolutional neural network, a combination of recurrent neural networkand a convolutional neural network, or other types of neural network orcombination or hybrid types of neural network described herein or in thedocuments incorporated by reference herein.

Continuing the example, the intelligent services system 20243 mayreceive an intelligence request and marketplace age data, marketplaceparticipant data, and marketplace transaction data from the marketorchestration system platform 20500. In response to the request and thereceived data, the artificial intelligence system may classify themarketplace as a new marketplace stage and may then, based at least inpart on the new marketplace stage classification, output a controlinstruction to the market orchestration system platform 20500 to use anengine for dynamically pricing transactions to decrease trading fees.

In embodiments, the market orchestration system platform 20500 mayprovide access to and/or integrate artificial intelligence system, whichmay be configured to recognize and/or identify indicators of poor healthin a marketplace and automatically output a set of control instructionsrelated to configuration of the marketplace based at least in part onthe recognized and/or identified indicator(s) of poor health to mitigatethe identified issue(s), including any of the parameters ofconfiguration of the marketplace or any other marketplace parametersnoted throughout this disclosure.

In some examples, a poor health indicator may include a significantlyincreasing average time between transactions over time and mitigationmay include deploying an intelligent agent that automatically generatessmall transactions to maintain market activity.

In some examples, a poor health indicator may include an increasingconcentration of sales by a particular seller or buyer (i.e., a corneredmarket), and mitigation may include deploying a governance engine thatdynamically imposes trading limits on parties and/or deploying anoffering engine that orchestrates participation by parties in othersimilar markets.

In examples, a poor health indicator may include large amounts ofbuy-side and sell-side activity by related entities (i.e., churn), andmitigation may include deploying an engine for dynamically pricingtransactions that increases trading fees in cases where volume ofcounter-positioned trades is high.

In examples, a poor health indicator may include the simultaneousselling and buying of the same asset(s) to create misleading orartificial activity in the marketplace (i.e., wash trading), andmitigation may include suspending user accounts of involved entitiesand/or notifying regulatory authorities.

In examples, a poor health indicator may include the presence of largenumbers of inactive or barely active accounts and mitigation may includeautomatically pricing and/or eliminating inactive accounts, deploying anintelligent agent to promote engagement by low activity accounts, and/ordeploying a gamification engine to promote engagement by accounts (e.g.,reward animations, emphasis on trending assets, lottery incentives,visual and/or auditory feedback rewarding users with color, movement,and sound, badges, haptics, points, rankings, gamified imagery, charms,and the like).

In examples, a poor health indicator may include an absence of newofferings and mitigation may include deploying an engine for automateddiscovery and linking of offerings from other marketplaces forpresentation in the marketplace.

In examples, a poor health indicator may include an absence of newsellers and/or buyers and mitigation may include deploying an engine forautomated party recruitment (such as incentives for first trades).

In examples, a poor health indicator may include an absence of newsellers and/or buyers and mitigation may deploying an engine forautomated party recruitment (such as incentives for first trades).

In examples, a poor health indicator may be detected distribution ofexecuted trades by an IP address and/or party relative to a theoretical“fair” distribution (i.e., front running) and mitigation may includeapplying an automated delay to a subset of trades by the IP addressand/or party such as to restore a theoretically “fair” distribution.

In some embodiments, the intelligent services system 20243 receives anintelligence request and any required data to process the request fromthe market orchestration system platform 20500. In response to therequest and the specific data, one or more implicated artificialintelligence system perform the intelligence task and output anidentification (e.g., an identified poor health indicator) and a set ofcontrol instructions based at least in part on the identification forthe market orchestration system platform 20500.

In examples, the intelligent services system 20243 may receive data fromvarious sources described throughout this document and the documentsincorporated by reference herein and may generate a set of featurevectors based on the received data. The intelligent services system20243 may input the set of feature vectors into a machine-learned model(e.g., using a combination of simulation data and real-world data) toidentify poor health indicators of a marketplace and to output a set ofcontrol instructions by a set of human experts (such as by the user)and/or by the other systems or models. Data sources and feature vectorsused for identification of poor health indicators and generation ofcontrol instructions may include user activity data, user profile data,transaction data, transaction timing data, marketplace age data, tradedistribution data (by party, IP address, or the like), offering data, aswell as external data sources (transaction data relating to similarmarketplaces or user data relating to similar marketplaces), and manyothers. Such artificial intelligence systems used for identification andcontrol instruction generation, in the present example and otherexamples described herein, may include a recurrent neural network(including a gated recurrent neural network), a convolutional neuralnetwork, a combination of recurrent neural network and a convolutionalneural network, or other types of neural network or combination orhybrid types of neural network described herein or in the documentsincorporated by reference herein.

For example, the intelligent services system 20243 may receive anintelligence request and marketplace offering data from the marketorchestration system platform 20500. In response to the request and thereceived data, the artificial intelligence system may generate anidentification of a poor health indicator (such as an absence of newofferings) and may then, based at least in part on the identification,output a control instruction to the market orchestration system platform20500 to deploy an engine for automated discovery and linking ofofferings from other marketplaces for presentation in the marketplace.

In embodiments, a “bet-on-anything” prediction marketplace may beconfigured to enable the trading of instruments related to theoccurrence of an event, satisfaction of a condition, or the like. Inembodiments, events may include real-world events (e.g., weather, thepassage of legislation, the winner of a baseball game, judicialoutcomes, and many others) and/or digital events (e.g., the winner of anonline chess match, the occurrence of an event in the metaverse, theoutcome of a simulation, the outcome of an electronic gaming event, andmany others). In embodiments, the prediction marketplace may be governedby smart contracts. In embodiments, the smart contracts may be based onthe occurrence of an event, the satisfaction of a condition, or thelike. In embodiments, the prediction marketplace may place wageringlimits for trades relating to particular events or conditions. Inembodiments, the prediction marketplace may generate multiple variationsof the same bet, allowing users to increase their bets where wageringlimits are in place. In embodiments, users of the prediction marketplacemay generate instruments that are the subject of trading. Inembodiments, the prediction marketplace may leverage digital oracles toserve as external data providers to the prediction marketplace.

In embodiments, the prediction marketplace includes a system configuredto prevent trading on specific topics (e.g., terrorism, crime, war,natural disaster, disease, or the like) and/or leverages intelligentservices system 20243 (such as artificial intelligence system, RPAmodule, and/or NLP module 8824) to prevent trading on specific topics.

In embodiments, the market orchestration system platform 20500 may beconfigured to provide aggregation services, validation services,valuation services, recommendation services, smart contract services,and/or the like for market orchestration of various classes ofdistributed and/or underutilized assets, resources, capabilities,services, and the like. For example, such classes may include personalcapital, energy resources, used material, used goods, excess finishedgoods, excess unfinished goods, excess manufacturing capacity, and manyothers.

In embodiments, the market orchestration system platform 20500 includesa system configured to provide autonomous identification and valuationof a set of inventoried items held by a set of owners.

In embodiments, the market orchestration system platform 20500 includesa system configured to provide autonomous negotiation and contractingfor aggregated assets.

In embodiments, the market orchestration system includes a systemconfigured to provide recommendations for value-added upgrades (e.g.,manufacturing capabilities).

In embodiments, the market orchestration system platform 20500 includesa system for providing real-time monitoring.

In embodiments, the market orchestration system platform 20500 includesor integrates with a set of edge devices for validation, monitoring,management, or the like of parties, assets, transactions, or the like.Market orchestration of stranded and/or distributed tangible assets mayrequire “trusted” validation and/or authentication for both participantsand assets. Validation may help ensure that buyers, sellers, traders,and the like meet financial, legal, regulatory, and other requirementsnecessary to participate in a particular market. Validation may alsohelp ensure the value of a commodity (such as a raw material),semi-finished goods, finished goods, capabilities (such as amanufacturing capability, a 3D printing capability, or the like), andmay others. Validation may also be used to monitor and reportparticipant data that may be analyzed to autonomously facilitate orrecommend certain transactions or market opportunities.

In embodiments, validation of market participants and/or assets may beaccomplished by biometric authentication, peer and customer reviews,quality measurements, inventory control systems, and many others. Forexample, biometric authentication could be used to validate the identityof users participating in a sports betting marketplace (such as tovalidate the age of participants to meet regulatory requirements) or tovalidate the identity of users participating in a professional servicesmarketplace (such as to validate the credentials of participants). Inembodiments, biometric authentication may be accomplished by retinascanners, fingerprinting, facial recognition, voice recognition, andmany others. Peer and customer reviews may be used, for example, tovalidate a supplier of goods or services. In embodiments, peer andcustomer reviews may be based on satisfaction with contract completion,social media posts, and many others. In embodiments, such peer andcustomer review data could be gathered using automated satisfactionpolling. Quality measurements may be used, for example, to determine thequality of materials produced and/or received. Quality measurements mayinclude tolerance measurements, machine vision observations and/ormeasurements, image recognition, statistical analysis, certificates ofconformity, and many others. Inventory control systems may be used, forexample, to track component parts for a product and to report componentpart inventory as a market orchestration participant. In embodiments,inventory control systems include QR codes, bar scanners, RFID devices,image recognition systems, AI-enhanced inventory control tools, variouscontrol software that could utilize SDKs or other means to integratespecially configured modules that enable use of inventory controlsystems with the market orchestration system platform 20500, and manyothers. In examples, footage from a drone may be processed by imagerecognition software to identify excess valuable timber tracts,including the specific tree species and sizes, and the timber tracts maybe automatically listed for sale in a marketplace.

In embodiments, systems and methods for validation of marketparticipants and assets (such as by using biometric authentication, peerand customer reviews, quality measurements, inventory control systems,and many others) may be used to generate a measure of risk related to aparty, an asset, a transaction, or the like. For example, social mediareviews for a business may be analyzed to generate a measure of riskrelated to transacting with that business.

In embodiments, the market orchestration system platform 20500 may use amachine learning system and/or artificial intelligence system, such asmachine learning system and/or artificial intelligence system includedin the intelligent services system 20243, for identification ofdistributed asset classes and capabilities, aggregation of demand and/orsupply, provision of transaction recommendations, and many others. Morespecifically, machine learning and/or artificial intelligence may beused for mining areas of opportunity based on existing or enhancedsubscriber capabilities, generating recommendations, generatingpredictions (e.g., facilitating ad hoc marketplace creation andmanagement by identifying resources to meet predicted needs that couldnot otherwise be met due to material shortages, logistics constraints,or the like), negotiating, business planning, smart contracting (such asmachine-to-machine negotiation and/or smart contracting), and manyothers.

In embodiments, the machine learning and/or artificial intelligencesystems of the intelligent services system 20243 may be integrated orotherwise connected to a gaming engine. For example, the machinelearning and/or artificial intelligence systems integrated with a gamingengine may be configured to choose the amount of raw material to bepurchased for manufacturing an order, wherein the purchase of certainexcess material could be justified because a quantity discount appliesand because there is a market opportunity to immediately sell the excessmaterial to a nearby company in an unrelated business sector.

In embodiments, the market orchestration system platform 20500 includesan authentication system for authenticating marketplace participants(e.g., buyers, sellers, regulators, and the like). The level ofauthentication required may depend on the marketplace attributes. Forexample, marketplaces for very expensive (e.g., spaceflightreservation), dangerous (e.g., hazardous chemicals), age-restricted(e.g., alcohol), and/or closely regulated products, services, and/orexperiences may require extensive buyer and/or seller authentication. Inembodiments, the authentication system may use biometric authentication,password-based authentication, two-factor authentication, multifactorauthentication, token-based authentication, certificate-basedauthentication, transaction authentication, computer recognitionauthentication, CAPTCHAs, single sign-on, and the like to authenticatemarketplace participants and other platform users.

In embodiments, the platform includes a market liquidity system formanaging marketplace liquidity. In embodiments, the platform may enablehigh value marketplaces to be combined or bundled with lower valuemarketplaces to enable liquidity of marketplace activities.

In embodiments, the market orchestration system platform 20500 maysupport aggregated consumer grouping of unaffiliated consumers based ona confirmed, inferred, and/or predicted product and/or service need. Inembodiments, the platform may be configured to identify a product and/orservice need, identify consumers in close proximity to one another thathave the product and/or service need, identify providers (e.g., serviceproviders, manufacturers, or the like) that are able to fulfill theproduct and/or service need, and identify a timeline for transactionsettlement (e.g., delivery of a product or timing of a service) thatwould fulfill the product and/or service need of the aggregated consumergroup.

For example, if a drought affects a region and water restrictions areimposed, unaffiliated residents of that region may independently beginbuying drip irrigation components. The platform may detect thispurchasing trend (e.g., through point-of-sale data or the like),identify the parties providing the drip irrigation components most indemand, and configure a bulk purchase of these components for theunaffiliated grouping of residents from the region. The platform mayconfigure the details of such a purchase, including any smart contractterms governing the purchase (e.g., purchase amount, deposit, deliverydetails, division of product details, and the like). Continuing theexample, the delivery could be made to a locker-type storage facilityfor individuals to retrieve at their own convenience.

Gaming Engine Platform Embodiments

A gaming engine smart contract process and related technologies now willbe described more fully hereinafter with reference to the accompanyingdrawings, in which illustrative embodiments are shown. The gaming enginesmart contract process and technologies may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the disclosure and inventions to those skilled inthe art. The gaming engine smart contract process may use an integratedgaming engine and distributed ledger platform or system that utilizesblockchain technology for storing gaming engine-integrated smartcontracts and providing convenient and secure control of the smartcontracts.

A platform that comprehensively integrates the capabilities of bothgaming engines and smart contracts offers benefits of security,flexibility, and automation for transaction counterparties, applicableto a wide range of markets and use cases. These use cases includeexisting gaming engine use cases, such as physics simulations, cinema,and media content production, where smart contracts can be integratedwith gaming engines to enable more sophisticated transactions, such asinvolving sets and series of conditional triggers, within theenvironments created by a gaming engine. Likewise, existing smartcontract applications, such as for handling exchanges of cryptocurrencyor other digital tokens, for enabling microtransactions of all types(including lending and insurance, among many others), and the like, canbe enhanced by the introduction of features and dynamics facilitated bygaming engines, including high-quality visualization, customization, andsimulation of transaction features and environments. In addition, otheremerging transaction environments, such as AI-driven digital twins,mixed reality environments, and intelligent wallets may incorporateintegrated gaming engine and smart contract features with other featuresand capabilities to enable entirely novel transaction experiences.

A Platform for Integrating a Set of Gaming Engines and a Set of SmartContract Services in a Common Execution Framework is provided in someembodiments.

Referring to FIGS. 209A, 209B, 209C, 210, and 211 , a gaming enginesmart contract executing platform 20900 is configured to enable methodsand systems for integrating gaming engine and smart contract services ina common execution framework. For example, the execution framework maybe common to different services or systems when the different servicesor systems input and/or output similarly formatted data, share and/ormanipulate similarly formatted data, perform steps of an algorithm withconsideration of steps performed by the other system or service, or whenother framework elements are common between the systems or services. Agaming engine smart contract is a smart contract that is developed,configured, managed, and/or executed within a gaming engine. A gamingengine is a software development environment and architecture thatprovides a set of predefined tools for digital content developers suchas physics, rendering, AI, collision detection, scripting, inputs, andthe like without the need to program them. Gaming engines may bedesigned or optimized to run on any combination of GPUs, CPUs, or othercomputing devices. Gaming engines may be developed for general-purposeapplications, optimized for a special-purpose development environment,or structured to enable a general-purpose set of applications that canbe expanded via additional modules.

The platform 20900 includes a gaming engine system 20902 configured tofacilitate configuring gaming engine services and smart contractservices in response to client requests. A client may be a user, anothercomputer program or function, or some combination of the two thatrequest and receive Platform services. Gaming engine services refer to acapability provided by a gaming engine or gaming engine set such asrendering, visualization decision-tree analyses, physics, or other morespecialized applications. One or more gaming engine services may beemployed and/or deployed by the platform 20900 via one or more gamingengine modules.

In some embodiments, the platform 20900 is configured to provide appliedgaming engine services, including gaming smart contract executionservices. Smart contract services refer to tools, analyses, processing,monitoring, intelligence, etc. provided for the development, management,and execution of smart contracts.

In some embodiments, the gaming engine system 20902 includes gamingengine configuration services 21030 configured to provide, among otherthings, configuration of gaming engines including one or more gamingengine modules per the client requests, such as by application of gamingengine module configuration services 21030 that may rely on gamingengine module analysis services 21034 to facilitate selection andarchitecting of gaming engine modules that may be retrieved from agaming engine module library 21032.

In some embodiments, the gaming engine system 20902 may include a smartcontract system 21040 that may facilitate configuring smart contracts,such as contracts from a smart contract library 21042 with a contractconfiguration service 21044. The contract configuration may be automatedand/or user-interactive (e.g., semi-automated, guided, free form entry,and the like) using, for example, renderings, visualizations or othermeans for contract term/segment review, selection, approval, and thelike. The smart contract system 21040 may include a smart contractmanagement service 21046 configured to manage the configured contracts,contract modules, contract elements, and the like.

In some embodiments, the gaming engine system 20902 includes a gamingengine application management capability 21010 configured to provideapplication of the smart contract system 21040, the gaming engineconfiguration service 21030 individually, and/or in variouscombinations. As an example, a gaming engine application managementsystem 21010 may select and execute a gaming engine application from, inembodiments, a gaming engine application library 21020. The gamingengine application library 21020 may, for example, store validatedgaming engine applications and their associated smart gaming enginecontracts. Gaming engine applications refer to software applicationsthat use a gaming engine to deliver gaming engine services in accordancewith a gaming engine smart contract. The gaming engine applicationmanagement system 21010 may be configured to work with the gaming engineconfiguration service 21030 and with the smart contract system 21040 toidentify and/or develop and validate a gaming engine services/smartcontract set for use with, for example, a gaming engine application. Thegaming engine application management system may further develop a gamingengine application using a validated gaming engine service/smartcontract set.

In some embodiments, the platform 20900 includes an intelligence layer21100. The intelligence layer may include an intelligence layercontroller 21110 configured to coordinate intelligence layer interactionwith various platform elements and sub-elements such as the gamingengine system 20902. The intelligence layer controller 21110 may performcoordination operations through an analysis management module 21116 thatmay interact with analysis modules 21112 and a governance library 21114.In embodiments, the intelligence layer 21100 may further include aplurality of artificial intelligence services 21120. These services mayinclude artificial intelligence services with capabilities such asmachine learning 21122, rules-based systems 21132, robotic processautomation 21128, digital twins 21126, machine vision 21130, NaturalLanguage Processing 21134, neural networks 21124, an analytics system21136, and/or an intelligent agent module 21138. The services may beconfigured to communicate with one another to facilitate analyses,decisions, recommendations, and the like associated with aspects ofdevelopment and execution of gaming engine smart contracts. Theautomation of intelligence services such as artificial intelligenceservices may include, for example: automated smart contract codedevelopment, contract parsing, contract element selection and assemblyto produce a complete contract, contract execution feedback andlearning, selection of gaming engine tools for contract verification,and the like.

In embodiments, the intelligence layer 21100 may be configured toautomatically monitor smart contract execution and provide alerts andnotifications associated with contract terms, governance, fraud, andothers. For example, the governance library 21114 may be configured tofacilitate determination of fairness, regulatory conformance,determination of contract terms compliance, and the like.

In embodiments, the intelligence layer 21100 may be configured tocommunicate with one or more other systems of the platform 20900 andthereby facilitate performance of tasks related to categorizing andgrouping smart contract transactions, contract types, clients associatedwith certain contracts, maintaining, updating, and organizing librariesbased on contract outcomes, such as by using similar transaction data toparse contract requests into an executable contract, and the like.

In embodiments, the digital twin system 21126 may include a digital twinlibrary that can be developed and maintained for smart contract elementsand assemblies for use with a range of intelligence services, includingvarious analysis capabilities.

In some embodiments, the platform 20900 includes a data processingservice 20930 in communication with a plurality of systems of theplatform 20900 and configured to provide shared data processing,storage, and libraries to the plurality of systems. The data processingservice 20930 may be configured to facilitate acquisition and sharing ofreal-time data from integrated external systems, performance ofcalculations, associated intelligence layer client requests, and thelike. The data processing system 20930 may also process and manage dataassociated with the full range of transactions required for smartcontract transactions, including data integration for one or moresimultaneous systems. In embodiments, the data processing system mayinclude one or more of data processing services 20931, data handlingservices 20932, data stores 20933, and libraries 20934.

In some embodiments, the platform 20900 includes an analytics andreporting system 20940. The analytics and reporting system 20940 isconfigured to provide analytic services to platform elements, such asthe intelligence layer services 21100, smart contract system 21040,gaming engine system 20902, gaming engine configuration service 21030,and others. In embodiments, the analytics and reporting system 20940 mayinclude database search services 20941, monitoring and notificationservices 20942, marketing, advertising, research services 20943, and thelike. These ancillary analysis and reporting system 20940 services maybe utilized by or in association with other platform capabilities, suchas a smart contract monitoring capability.

The platform 20900 may further include distributed ledger services20950, such as block chain smart contract transaction services 20951,blockchain smart contract management services 20952, and a range ofother distributed ledger transaction services 20953.

In addition to these function-oriented systems, the platform 20900 mayinclude platform-wide services and systems, such as an interface system20980 that may provide user interface (e.g., GUI) functionality,software development kit capabilities, application programming interface(API) port support, and the like. Another platform-wide system is acommunication system 20970 that may provide communication systems, dataflow management and/or optimization, network management and the like.Yet another platform-wide system comprises a security system 20960 thatmay, among other things, provide authentication, monitoring, service andsoftware access controls, and the like.

In some embodiments, the platform 20900 may be deployed in anenvironment, such as a networked environment in which a network 21070may facilitate interoperation of the platform 20900 with externallicensees and/or subscribers 21050 and external data and relatedservices 21060. In embodiments, licensees and/or subscribers 21050 mayinteract with the platform within a software-as-a-service architecture,an embedded platform access architecture, and the like. Examplelicensees and/or subscribers 21050 may include enterprise resourceplanning system 21051, product lifecycle management system 21052, socialmedia systems 21053, venues 21054, quantum computing systems 21055,underwriting systems 21056, auctioning services 21057, gaming systems21058, entertainment systems 21059, and the like. In examples, theplatform 20900 or portions or derivations thereof (e.g., a gaming enginesmart contract execution framework) could be incorporated into a socialmedia application that allows users to subscribe to immersive visualcontent that is tailored to their requirements and is executed and paidfor in the context of a gaming engine smart contract. The same approachcould be applied to any industry that can benefit from tailoredproposals, visualization, and provision of services. Examples includeinsurance systems, IoT systems, 3D printing systems, and the like.

In embodiments, external data and related services 21060 may include,without limitation, regulatory systems 21061, sensor and vision systems21062, blockchain systems 21063, quantum computing systems 21064, dataservers and services (e.g., public data sources, private data sources,corporate sources and services), and the like. In embodiments, externaldata and related services 21060 may refer to external data sources,computing services, service provider equipment, and so forth that mayinteract with, be required, or used to provide platform 20900 services.These external services, data sources, entities, and associated data maybe integrated using, for example, combinations of the communicationssystem 20970, the security system 20960, the interface system 20980, andthe like.

Referring to FIG. 212 , a cloud-based embodiment 21200 of the platform20900 is depicted. The gaming engine and smart contract services,including configuration, execution, analysis, management, and the like,may be offered as a set of services that could be integrated intocloud-based computing systems. The platform services may be accessed ascloud-based services and data required for use and produced by theplatform (e.g., integrated smart contract gaming engine integrations)may be accessible as cloud-based data. Platform licensees and/orsubscribers 21220 may access the platform services and data remotelythrough a range of application-targeted systems, including mobiledevices, portable devices (e.g., laptop devices and the like), and thelike. In addition to being accessible as cloud-based data, deliverablesof the platform, including deployable gaming engine-based smartcontracts and the like may be delivered to platform licensees forexecution on local systems, such as corporate computing systems (e.g.,cloud-based, and the like).

In the architecture of FIG. 212 , local data and services 21210 may beaccessed by and/or provide resources to the platform through a varietyof configurations, including peer-to-peer computing architectures,distributed storage and computing services, data acquisition systems,and the like. Resources may include smart wallets, machine visionsystems, industrial internet of things sensor systems, and the like.

In embodiments, the platform 20900 can be deployed as part of astand-alone device such as a kiosk with intermittent networkcommunication, as part of a network of connected devices such as a robotfleet with shared computing and storage resources, or as a cloud-baseddistributed service such as VR gaming or visualization, or anycombination of these or other deployment targets. While each type ofplatform implementation may require similar basic capabilities providedby platform elements (e.g., intelligence layer 21100, data processingsystem 20930, etc.), the level of functionality may vary depending onhardware, processing power, connectivity, security requirements,governance, and the like. In embodiments, the platform 20900 may belicensed or subscribed to using a software as a service (SaaS).

FIG. 213 illustrates an embodiment of the gaming engine system. Thegaming engine system works with other platform elements during allphases of operation. For example, the smart contract configurationportion of the gaming engine system may work with the digital twin AIservice in the intelligence layer to evaluate various gamingengine/smart contract outcomes that are presented to a client prior tosmart contract finalization. In embodiments, the gaming engine systemincludes the gaming engine configuration service system 21030, the smartcontract system 21040, the gaming engine application management system21020, and the gaming engine application library 21020.

In embodiments, the gaming engine system works with the intelligencelayer to ensure that smart contracts are managed in accordance withregulatory requirements. In examples, the platform may receive a clientrequest to produce highly detailed renderings of a secure governmentsite. In this case, the gaming engine smart contract analysis moduleperforms an initial compliance screening using the intelligence layercontroller and its associated governance library. The potentialtransaction is flagged as illegal, causing the platform to deny orsuggest an alternative to the client request. The gaming engine systemworks with the intelligence layer and the analytics and reporting systemto identify and report fraudulent/anomalous activity at any stage ofsmart contract development and execution. For example, trend analysesmay compare a generalized set of historical data associated with thesame or similar contracts, and flag anomalies. The gaming engine systemworks with the intelligence layer to implement learning and optimizationbased on a database of generalized gaming engine smart contractoutcomes. This capability can be used to optimize libraries, informmarketing and sales activities, provide client options, and so forth.

FIG. 214 illustrates an exemplary gaming engine and smart contractexecution workflow. The gaming engine system forms the core of platformfunctionality. The gaming engine system configures gaming engines andsmart contracts in response to client requests and when necessary,provides applied gaming engine services, including gaming smart contractexecution. A gaming engine may incorporate one or more smart contractsegments comprising a single contract whose terms and programs aredeveloped autonomously or in combination with a user interface, wherethe output satisfies all parties and is in compliance with existingstandards. A gaming engine may comprise multiple gaming engine modulesworking collectively to deliver a service. Execution of a gaming enginesmart contract involves data gathering and analysis to validate contractexecution according to agreed terms and compliance requirements.Transactions and contract records may be managed using a blockchaindistributed ledger.

FIG. 215 illustrates a client service request flow overview. The gamingengine application management is activated following a client servicesrequest, where depending on the type of request, it may (1) select andexecute a gaming engine application from a library, (2), with help fromthe gaming engine configuration service and the smart contract system,identify and/or develop and validate a gaming engine service/smartcontract set for use with a gaming engine application, and (3) develop agaming engine application using a validated gaming engine service/smartcontract set. Gaming engine smart contracts may be executed locally, indistributed fashion, or using cloud-based services. Gaming engine smartcontract segments may be separately executed by one or more appropriatesubsystems. For example, one segment may initiate a contract elementwith a robot fleet to perform a service, a second portion of thecontract may initiate a gaming engine service that uses external datainputs to perform renderings for verification of contract progress, anda third portion of the contract initiates a cloud-based distributedledger system that manages verification data and handlesblockchain-based transactions. Transactions may include automaticpayments through smart wallets that are included in the contract and arepart of the blockchain distributed ledger.

The gaming engine application library stores validated gaming engineapplications and their associated smart gaming engine contracts. Asshown in FIG. 215 , the library may be expanded when gaming engineservice/smart contract sets are identified and developed into a gamingengine application.

Referring back to FIG. 213 , the gaming engine module library contains arange of gaming engine modules for general-purpose or specializedapplications. Example special-purpose modules include training,auctions, insurance, etc. A library index maintains and optimizes livesets of associations between various gaming engine modules and relatedsmart contract library segments that together comprise valid gamingengine service/smart contract sets. The index may be expanded asnecessary based on learning, expert input, machine learning, etc. Themodule library and its associated index can also be used with the smartcontract system to assemble and configure valid gaming engineservice/smart contract sets when validated sets are not available for aclient service request.

Smart contract rendering is provided in some embodiments.

Smart contract rendering refers to the capability of a gaming engine toproduce realistic images, animations, and similar visual representationsof objects and events in real/near-time. Rendering capability can beused in all phases of contract development, execution, and clientservice delivery. Examples: through a GUI, gaming engine renderingservices may provide gaming engine rendering visualization for a humanuser that illustrates steps and activities in a proposed contract andautomatically update content to match modification of contract elementsuntil a suitable agreement is reached. In a similar fashion, gamingengine rendering services could provide either contract deliverables orverification and blockchain data for contract execution. The gamingengine library may contain one or more gaming engine modules dedicatedto rendering or may use a portion of the base general-purpose gamingengine that contains rendering services.

Visualizations in a smart contract gaming engine are provided in someembodiments.

Smart contract gaming engine visualization is like gaming enginerendering; However, rather than producing realistic renderings, thegaming engine produces visualizations of processes, event sequences,assets, relationships, value chain network entities, and related futureoutcomes, movements, etc. For example, a visualization might include aprocess flow diagram for a series of proposed contract outcomes,displayed to show adjustments as new contract execution data and inputsare received. The gaming engine module library may contain one or moremodules dedicated to visualizations or may use a portion of the basegeneral-purpose gaming engine that contains visualization services.

FIGS. 216A and 216B illustrate a gaming engine/smart contract set flow.Gaming engine module analysis coordinates activities and analyses withinthe gaming engine configuration service, with the smart contract system,and with other platform elements such as the intelligence layer. FIG. 6below illustrates an example process flow, where gaming engine moduleanalysis manages the process of identifying a set of gaming enginemodules that are compatible with a gaming engine smart contract set inresponse to a client request.

In examples, the client is a PLM system that incorporates an embeddedversion of the platform. A product management team wishes to use the PLMsystem to cost, visualize, and enact one or more scenarios associatedwith the world-wide recall of over one million faulty electric motors.The PLM system calls on the platform to provide a service that usesgaming engine visualization and costing for various scenarios that mayinclude any combination of free return shipping with new replacement,same-day repair at a regional service center, new replacement with localrecycling or landfill of the returned motor, and so on. Gaming enginemodule analysis determines that platform libraries do not contain avalid gaming engine service/smart contract set that meets clientcriteria, which triggers an iterative contract configuration sequencewhere smart contract segments are sourced, configured, assembled, andsimulated until a valid set is identified, added to the smart contractlibrary, and indexed with the gaming engine module library. Gamingengine module analysis continues until there is a valid gaming engineservice/smart contract set established for delivery of client services.Example contract segments may incorporate local landfill, shipping,labor rates, distribution concentrations of faulty motors, validation ofreturn or repair, reimbursement transactions, risk, liability, and soon. The selected gaming engine service/smart contract set(s) areprovided to the PLM system for access by the client for downstreamgaming engine applications. After the product team reviews scenariovisualizations and costs, they may choose to initiate one or more of thesmart contract sequences to automatically complete and document this PLMprocess.

Gaming engine module configuration works with the gaming engine smartcontract system to configure selected gaming engine modules for use witha smart contract set. Gaming engine module configuration may adjust,modify, specify, or otherwise configure a gaming engine for executionwith a smart contract set. For example, certain variables or options maybe specified by a platform licensee or subscriber, or other operatingcharacteristics may be optimized for a host processing andcommunications system. Gaming engine module configuration assembles andsequences modules, determines required operating sequences inassociation with the smart contract management module, and coordinatesinputs, including with external data and systems.

Smart contract systems are associated with the gaming platform in someembodiments.

The platform incorporates a smart contract system that is used to selectand/or develop a gaming engine smart contract(s) that meet clientrequirements, adhere to foreseeable compliance requirements, includemeans and criteria for validation, incorporate terms and conditions, andprovide for a digital ledger system (blockchain or other) to managetransactions and associated recordkeeping (see FIG. 213 ). The smartcontract system comprises a smart contract library containing predefinedcontract segments, contract configuration that manages tasks associatedwith contract parsing, assembly, and simulation, and related contractdevelopment tasks, and contract execution that works within a gamingengine application to handle contract verification, transactions,contract data, and linkages to external systems required for execution.

Configuration and management systems associated with a gaming engineplatform are provided in some embodiments.

Contract configuration may be automated or user-interactive, whererenderings, visualizations, or other means provide abstracted or actualcontract segments for review. With coordination from gaming enginemodule analysis, assistance from the intelligence layer, and access toexternal data and services, the contract configuration system parses theclient request to identify contract segments (existing code) that can beassembled to form a complete gaming engine smart contract that workswith a defined set of gaming engine modules. The contract configurationsystem may also include the capability to automatically develop andvalidate new smart contract code and incorporate it into a gaming enginesmart contract. The contract configuration system may provide the meansto develop and validate new code through the interface system. Thiscould be an important capability of a licensed platform implementationembedded as part of another system or service, for example, socialmedia, PLM systems, auctioning systems, etc. With assistance from theintelligence layer and gaming engine module analysis, the configurationsystem develops and simulates gaming engine smart contract options.Selection and approval of the final contract configuration may beautonomous or require client approval through the interface system. Inexamples, user presentation could take the form of an updated version ofthe original contract input request. The contract configuration systemworks with the intelligence layer to provide governance oversightthroughout the contract configuration process.

Simulation systems associated with a gaming engine platform are providedin some embodiments.

Simulation systems comprise various working combinations of platformelements that provide the means to simulate any aspect, phase, or typeof smart contract input, configuration, or execution. Simulation systemsmay include the use of digital twins configured for predictive orreal-time analyses that aid with contract development, execution, andmachine learning. Simulation may employ various gaming engines asembedded filters or processors that aid with the process ofdecision-making. Simulation systems may be configured to interact withend users to display contract options, deliverables, and so forth.Simulation systems can be optimized for specific industries, use cases,etc. Examples include simulations for insurance, tax consequences,hazardous disposal fees, and so on.

A suite of AI tools is incorporated in the intelligence layer, wherethey are available to the smart contract system for tasks associatedwith smart contract development or execution. For example, the smartcontract system may request assistance from the intelligence layer NLPservice to parameterize a voice mail message request for a smartcontract service. In this case, the intelligence service output may be alist of phrases that represent proposed contract requirements that canbe used by the contract configuration system for subsequent contractdevelopment.

Intelligent Agents

In embodiments, the platform 20900 may include an intelligent agentmodule 21138 configured to create, configure, and manage one or moreintelligent agents. The one or more intelligent agents act asintermediaries on behalf of a platform client (or vice versa) to supportor conduct various processes associated with gaming engine and smartcontract development focused on matching specific client and platformcapabilities and requirements with the desired/requested contractdeliverable and/or gaming engine operations. This may involve matchingexisting contract elements and terms such as price, quality, specificindustries and scenarios, and other business considerations. Althoughthese capabilities are inherent in the intelligence layer, specificintelligence agents may be configured for certain transactions, specificusers, select customers, etc. The intelligent agents may make decisionsand/or perform one or more services based on an environment, user input,and experiences. The intelligent agents can autonomously gatherinformation (e.g., on a regular, programmed schedule or upon beingprompted by a user). Examples of tasks that the platform 20900 mayperform via one or more intelligent agents include automatinginteractive and sophisticated processes, performing front officebusiness operations, performing intelligent, contextual, updated clientoutreach, performing communication via email, text, other messagingplatforms, performing negotiation, performing RPA assisted negotiation,provide negotiation terms and alternatives, performing full negotiationbased on a gaming/logic engine, performing social media interaction,responding to client comments on social media, “liking” or otherwiseinteracting with relevant social media posts, performing contentreposting and/or generation, improving end-user experience, monitoringand/or shadowing human-human exchanges, perform actions basedhuman-human exchanges, preparing packages, accounts and/or loans foropening, delivering and/or interacting with off-channel content and/orservices, automating accounting for transactions, automating execution,providing analytics that create sophisticated and accurate frameworks,automating pricing of cross-market products based on comparable pricesfor direct services from competitors, execution of contract terms, andthe like. For example, the platform 20900 may employ an intelligentagent to perform mortgage cross-selling enhanced by IoT data, gamingengine operations, smart contracts, and AI. The intelligent agent mayperform enterprise, churn prediction, and determine preventativenegotiated rates to minimize customer loss. By way of another example,in a healthcare environment including regulations, insurance, and/orfinance management, the platform 20900 may employ an intelligent agentto assist with determining and analyzing a choice of banks, bankaccounts, and bank features, facilitate regulatory handoffs andself-validation. The intelligent agent may assist a service providerwith a portal for deposits, withdrawals, and/or compliance reporting.The intelligent agent may merge health insurance claim streams with bankaccount activity data and user actions. The intelligent agent mayfacilitate creation and management of a health portal for a healthinsurance provider. The health portal may contain highly confidentialinformation which may be managed via blockchain and/or distributedledger. The intelligent agent may assist with bill payment services,such as by handling direct payments and/or automatic payments forapproved claims. The intelligent agent may assist with add-on financialand/or investment services, such as HSA spending management. Theintelligent agent may be configured to create and/or manage a smartwallet. The smart wallet may be configured to manage one or more actionsrelated to a regulated HAS, such as policy and governance of datapresentation, validation without invading privacy, and/or payments forhealth services.

In embodiments, the RPA module, coupled with intelligent agents, canautomate interactive and sophisticated processes, as well as performfront-office business operations. As such, the RPA module can operatewith high-intensity workloads that seamlessly integrate for improvedend-user experience. The intelligent agents can work in synergy withother digital and automation technologies, such as IoT (Internet ofThings) and analytics, to create sophisticated and accurate frameworks.For example, the platform 20900 may enable mortgage cross-sellingenhanced by IoT data, smart contracts, gaming engine operations, and AIvia the RPA module and the intelligent agent module. Thereby, theplatform 20900 may perform enterprise, churn prediction, and predictpreventative negotiated rates to minimize customer loss.

In embodiments, the platform 20900 may be configured to, via one or bothof the RPA module and the one or more intelligent agents, dynamicallyoptimize market conditions, such as prices, liquidity, availability, andthe like of traded assets and/or currencies (cryptocurrencies, fiatcurrencies) based on real-time intelligence. For example, on the lendingside, cost of acquisition of customers and type of loan and quality ofunderwriting (e.g., filters to the incoming funnel) can be adjustedbased on current market conditions of those in the funnel (e.g., datafrom the funnel). Moreover, the need to discount the sale of servicingcan be tied to the acquisition.

In embodiments, the platform 20900 may be configured to, via one or moreintelligent agents, dynamically determine necessary resources to performone or more operations based on real-time intelligence. The intelligentagent module 21138 may, via one or more intelligent agents and/or AIprocesses, determine one or more gaming engine configurations, gamingengine operations, smart contracts, smart contract libraries,intelligence layer services, and/or other intelligent agents that may besuitable to performance of a gaming engine operation and/or desiredclient operation. The one or more intelligent agents may automaticallyproceed with executing, ordering, and/or facilitating cooperation,integration, and/or operation of the determined resources to perform thedesired gaming engine operation, smart contract operation, and/or clientoperation. For example, in response to a received or anticipated desiredclient operation, the one or more intelligent agents may determine thatone or more of the digital twin system 21126, the machine vision 21130,and the rules-based system 21132 are necessary, desirable, and/oroptimal for performance of the client operation. The one or moreintelligent agents may additionally or alternatively determine one ormore other intelligent agents and/or AI/ML systems that may also help orbe necessary to performance of the client operation. The one or moreintelligent agents may additionally or alternatively determine one ormore resources within the gaming engine application library 21020 and/orthe contract configuration module 21044 that may be applicable to ornecessary to performance of the client operation.

In embodiments, the platform 20900 may be configured to, via one or moreintelligent agents, dynamically recognize attention that is being paidto one or more assets within a gaming engine environment and/or anenvironment, market, and/or market ecosystem external to the gamingengine environment. Examples of assets include securities, digitalassets, physical assets, services, goods, persons, personas, avatars,brands, and the like. The one or more intelligent agents may determinethe attention to the one or more assets based on one or more externaldata and/or external services 21060, such as data related to personalattention, views, “clicks,” attention of other intelligent agents, andthe like. Upon determining the attention, the one or more intelligentagents may automatically propose and/or implement a response via thegaming engine system, such as by implementing a gaming engine smartoperation with related smart contract functionality to capitalize on theasset that is receiving the attention.

Robot process automation (RPA) provides tools to deploy and managesoftware bots that emulate human interactions with digital systems andsoftware such as understanding how to navigate systems, identify andextract data, and perform a wide range of defined actions. Robot processautomation (RPA) is incorporated in the intelligence layer, where it isavailable to the smart contract system automating or facilitating tasksassociated with smart contract development or execution. Specific RPAtools can be stored in a database for later retrieval or refinement.

Opportunity mining/discovery provides the automated capability toproduce and/or assemble smart contract elements to deliver contractedservices with the assistance of experts, RPA, machine learning,automated code development, or other methods. The smart contract systemmay also work with the intelligence layer to proactively develop smartcontract configurations based on an anticipated need and opportunitiesidentified using trend analysis or other data predictive tools.

Cython can be used to optimize individual or combined smart contractcode elements. For example, it may use an RPA system or other AI tools,run Python programs, log performance statistics, identify computationalchoke points, identify C libraries that perform the same computationsfaster, and automatically annotate relevant portions of Python sourcecode to render it into and/or parseable by Cython code. Cython can beused for the origination and/or integration of smart contract codesegments that are sourced from existing libraries, code that isautomatically developed by the smart contract configuration system, orcode input through the interface system.

Verification associated with smart contracts and gaming engines isprovided in some embodiments.

Contract management coordinates with contract configuration and otherplatform elements to validate and provide transactional support forgaming engine smart contract execution. Automatic verification of smartcontract execution may be as simple as a user acknowledgment that aservice has been delivered, or more complex where multiple and diversemechanisms are employed, such as third-party compliance systemsinvolving various types and locations of data, waiting for completion ofanother contract (external or sub-contract). Among other capabilities,smart contract gaming engines may provide a contracted service, and/orparticipate directly or indirectly in contract verification. Smartcontract execution also requires a set of notifications that communicatewith contract participants. These may include progress reports,renderings and visualizations through the interface system, paymentnotifications, notifications regarding contract problems or delays, andthe like. Smart contract clients may include actual users,subcontractors, financial institutions, internal processors associatedwith execution of the contract, regulatory agencies, and the like. Smartcontract execution provides tools to improve smart contract performanceusing a performance feedback/machine learning loop that works with thecontract library. The contract execution system provides for real-timecontract management and adjustment as far as it is possible within thesmart contract/compliance context. The contract execution system workswith the intelligence layer to provide governance oversight throughoutthe full contract execution process.

The smart contract library contains a set of validated smart contractsegments and validated sets of assembled smart contract segments thatcan be used as building blocks, and a set of validated and completegaming engine smart contracts. Working with the intelligence layer andsmart contract configuration, the contract library may be expanded andimproved. Working through the interface system and with the intelligencelayer, new library elements may be created, validated, and included inthe library. Working with the analytics and reporting system module thatis focused on advertising, marketing, and research, the smart contractlibrary may be configured to include one or more sets of contracts thatmay be proactively presented to a client for consideration. In a simpleexample, a client may request a contract for a house painting service.In response, the platform may provide a range of complete contractproposals provided by one or more paid advertisers/service providers.The library may be configured and organized to provide groups ofcontract segments and contracts to meet domain-specific, functional, orother requirements. Example groupings may include escrow services,events and experiences, data story development and delivery, training,auctions, etc. Several example descriptions are listed below as focusareas and shown in FIG. 4 .

The smart contract system may configure and execute contracts andassociated gaming engines directed to simulating various activities andmaintaining records associated with training services such as certifiedtraining for professional qualifications, fitness training, hazardousmaterials handling training, etc. Smart contract training modules may bedeveloped and/or stored in the contract library

Product lifecycle management (PLM) comprises one or more tools (mostlysoftware) used to plan and manage a product throughout its life, frominitial specification and design, to manufacturing, to maintenance andupdates, to eventual recycling or disposal. In many cases, PLM systemscan track not just a type of product, but also a specific item. In thecontext of this disclosure, one or more smart contracts designed toassist with PLM services may be incorporated into the library, orconversely, the platform (or subset), plus a specific smart contract,and may be embedded in a PLM system. PLM example implementations mayinclude contracted gaming engine simulations to evaluate designfeatures, manufacturing methods, product costing and paymentsimulations, recycling scenarios and options, etc.

Data stories describe, illustrate, or otherwise provide detailedunderstanding regarding an area of interest, where available data isanalyzed and presented for a specific audience or client. For example, afinancial analysis evaluating different risk scenarios might employ agaming engine for both simulation and visualization for a CFO. In thiscontext, data stories may be provided as a service within the smartcontract system, or possibly included with data analysis and reporting.

Gamification employs game design elements to improve user engagement,even in non-game contexts. Smart contracts with gaming engines may beused to provide this service, either on demand or as an embedded servicefor a range of uses. Gamification commonly employs game design elementsto improve user engagement, organizational productivity, learning andknowledge retention, ease of use, and so forth.

The smart contract configuration system works with the intelligencelayer, integrated external systems, and other execution platformelements to incorporate intelligent brokering and counterparty discoveryto facilitate the contract development process. This capability may alsocoordinate with services such as marketing and advertising, searchfunctions, blockchain for knowledge, etc. Transactions may be associatedwith brokering transactions.

Referring to FIG. 219 , in embodiments, the distributed ledger 21908 maybe any suitable type of electronic ledger 21908, such as a blockchain(e.g., Hyperledger, Solidity, Ethereum, and the like). The distributedledger 21908 may be centralized, decentralized, or a hybridconfiguration where the distributed ledger system 20950 stores a copy ofa distributed ledger 21908 in addition to any number of participantnodes 21916 that store copies of the distributed ledger 21908. Whenreferring to the distributed ledger 21908, the term “distributed ledger”(and/or any logs, records, smart contracts, blocks, tokens, and/or datastored thereon) may refer to a specific instance of a copy of thedistributed ledger 21908 (and/or any logs, records, smart contracts,blocks, tokens, and/or data stored thereon) and/or the collection oflocal copies of the distributed ledger 21908-L stored across any numberof nodes (which may include the distributed ledger system 20950), unlessspecifically indicated otherwise.

In some embodiments, a private network of authorized participants, suchas one or more of the knowledge providers and/or nodes, may establishcryptography-based consensus on one or more items, such that thedistributed ledger system 20950 may provide security, transparency,ability to audit, immutability, and non-repudiation to transactions fordigital knowledge. In some embodiments, a trusted authority (e.g., thedistributed ledger system 20950 or another suitable authority) may issueprivate key and public key pairs to each registered user of thedistributed ledger system 20950. The private key and public key pairsmay be used to encrypt and decrypt data (e.g., messages, files,documents, etc.) and/or to perform operations with respect to thedistributed ledger 21908. In some embodiments, the distributed ledgersystem 20950 (or another trusted authority) may provide two or morelevels of access to users. In some embodiments, the distributed ledgersystem 20950 may define one or more classes of users, where each of theclasses of users is granted a respective level of access. In some ofthese embodiments, the distributed ledger system 20950 may issue one ormore access keys to one or more classes of users, where the one or moreaccess keys each correspond to a respective level of access, therebyproviding users of different levels of access via their respectiveissued access keys. In embodiments, possession of certain access keysmay be used to determine a level of access to the distributed ledger21908. For example, in some embodiments, a first class of users may begranted full viewing access of a block, while a second class of usersmay be granted both viewing access of blocks and an ability to verifyand/or certify one or more instances of digital knowledge containedwithin a block, and while a third class of users may be granted viewingaccess of blocks, an ability to verify and/or certify one or moreinstances of digital knowledge contained within a block, and an abilityto modify the one or more instances of digital knowledge containedwithin the block. In some embodiments, a class of users may be verifiedas being a legitimate user of the distributed ledger 21908 in one ormore roles and allowed related permissions with respect to thedistributed ledger and content stored therein. A user may be verified,for example, as a bona fide knowledge provider 21906 that uses aknowledge provider device 21990, knowledge recipient 21918 that uses aknowledge recipient device 21994, and/or crowdsource 21936 that uses acrowdsource device 21992. There may be any number of each device 21990,21992, 21994. As shown in FIG. 219 , there is one knowledge providerdevice 21990, two crowdsource devices 21992, and one knowledge recipientdevice 21994. In other examples, as it is understood, there may be one,two, three, or more of any device type 21990, 21992, 21994, in anycombination. In other examples, there may be one of each device type(e.g., one knowledge provider device 21990, one crowdsource device21992, and one knowledge recipient device 21994). In other embodiments,these devices 21990, 21992, 21994 may be implemented as one or morecomputing devices and/or server devices (e.g., as part of a serverfarm).

In some embodiments, the distributed ledger system 20950 may include aledger management system 21910. In some embodiments, the ledgermanagement system 21910 manages one or more distributed ledgers (alsoreferred to as “ledgers”). In some embodiments, the ledger managementsystem 21910 may instantiate a distributed ledger for a particularknowledge provider 21906 or group of knowledge providers 21906, such asby instantiating a distributed ledger 21908 that stores instances ofdigital knowledge 21904 provided by the knowledge provider 21906 orgroup of knowledge providers 21906. The distributed ledger system 20950may allow only the particular knowledge provider 21906 or particulargroup of knowledge providers 21906 to host instances of digitalknowledge 21904 (e.g., by using knowledge provider device 21990) on therelated distributed ledger 21908 and/or for each instance of digitalknowledge 21904, such that each distributed ledger 21908 is specific toa respective knowledge provider 21906 and/or an instance of digitalknowledge 21904. In some embodiments, the ledger management system 21910may instantiate a plurality of distributed ledgers 21908, one or more ofthe distributed ledgers 21908 being configured to facilitate hosting,sharing, buying, selling, licensing, or otherwise managing a category ofdigital knowledge 21904. Categories of digital knowledge may be relatedto, for example, one or more industries such as automotive and/orfinancial or one or more types of digital knowledge, such as 3D printingschematics. In some embodiments, the ledger management system 21910 maymaintain a distributed ledger that facilitates management of some or allof the instances of digital knowledge 21904 and/or the knowledgeproviders 21906 for which related data is stored by the distributedledger system 20950.

In some embodiments, a distributed ledger 21908 is any suitable type ofblockchain. Any other suitable types of distributed ledgers may be used,however, without departing from the scope of the disclosure. Thedistributed ledger may be public or private. In embodiments, where thedistributed ledger is private, reading from the ledger and/or validationprivileges by a user such as the knowledge provider 21906 (e.g., usingknowledge provider device 21990) may be restricted to invitees, userswith one or more accounts/passwords, or by any other suitable method ofrestricting access to the distributed ledger 21908. In some embodiments,the distributed ledger 21908 may be at least partially centralized, suchthat a plurality of nodes of the distributed ledger is stored by thedistributed ledger system 20950. In some embodiments, the distributedledgers are federated distributed ledgers, as the distributed ledgersmay be stored on pre-selected or pre-approved nodes that are associatedwith the parties to a management of digital knowledge 21904 via thedistributed ledger system 20950. The techniques described herein may beapplied, however, to publicly distributed ledgers as well. In a publiclydistributed ledger, any suitably configured computing device (personalcomputers, user devices, servers) or set of devices (e.g., a serverfarm) may act as a node 21916 and may store a local copy of adistributed ledger 21908-L, whether the owner of the node otherwiseparticipates in the transactions facilitated by the distributed ledgersystem 20950. In these embodiments, such nodes 21916 may add, validate,or deny new blocks, save new blocks to the distributed ledger 21908 (ifvalidated) to maintain a full copy (or nearly full copy) of thetransaction history relating to the distributed ledger 21908, andbroadcast the transaction history to other participating nodes 21916.

In some embodiments, the ledger management system 21910 (and/or thecollection of participant nodes 21916) may be configured to leverage adistributed ledger 21908 to create an immutable log establishing of achain of work, possession, and/or title of one or more instances ofdigital knowledge 21904, establishing verification of one or moresources of the digital knowledge 21904, and/or facilitatingcollaboration of a plurality of knowledge providers 21906. In someembodiments, the ledger management system 21910 may utilize adistributed ledger to manage a set of permission keys that provideaccess to one or more instances of the digital knowledge 21904 and/orservices associated with the distributed ledger system 20950. In someembodiments, the distributed ledger 21908 provides provable access tothe digital knowledge 21904, such as by one or more cryptographic proofsand/or techniques. In some embodiments, the distributed ledger 21908 mayprovide provable access to the digital knowledge 21904 by one or morezero-knowledge proof techniques. In some embodiments, the ledgermanagement system 21910 may manage the distributed ledger to facilitatecooperation and/or collaboration between two or more knowledge providers21906 with regard to one or more instances of digital knowledge 21904.

FIG. 220 illustrates an exemplary embodiment of the distributed ledger21908, the distributed ledger 21908 being distributed over a ledgernetwork 21070. The ledger network 21070 may include the distributedledger 21908 and a set of node computing devices 21916 that communicatevia one or more communication networks 21070. In some embodiments, thecommunication network 21070 may include the Internet, private networks,cellular networks, and/or the like. In embodiments, the nodes 21916 mayall host a copy of the distributed ledger 21908 (or a portion thereof).For example, the ledger network 21070 may include a first node 21916-1,a second node 21916-2, a third node 21916-3 . . . and an Nth node21916-N that communicate with the distributed ledger system 20950 andwith other nodes 21916 in the ledger network 21070. In some embodiments,the distributed ledger system 20950 is configured to execute the ledgermanagement system 21910 and may store and manage a local copy of adistributed ledger 21908 that is used in connection with facilitatingmanagement of one or more instances of the digital knowledge 21904 viathe distributed ledger system 20950. In some embodiments, thedistributed ledger system 20950 (or the ledger management system 21910executed thereon) may also be thought of and referred to as a node ofthe ledger network 21070. In some embodiments, the ledger managementsystem 21910 may also generate and assign private key and public keypairs to users such as one or more of the knowledge providers 21906and/or one or more receivers 21918 of the digital knowledge 21904 (alsoreferred to as “knowledge recipients”) and/or to each node 21916 in theledger network 21070, such that the private key and public key pairs areused to encrypt data transmitted between nodes 21916 in the ledgernetwork 21070.

In some embodiments, each of the nodes 21916 of the ledger network 21070(other than the distributed ledger system 20950) may be a computingdevice or a set of connected computing devices that are associated withthe knowledge providers 21906 and/or knowledge recipients 21918. In someembodiments, the nodes 21916 may include computing devices of partiesthat are not involved in the providing or receipt of knowledge (e.g.,parties that are associated with neither the knowledge providers 21906nor any of the knowledge recipients 21936). In some embodiments, each ofthe nodes 21916 may store a respective local copy 21908-L of thedistributed ledger 21908. In some embodiments, one or more nodes maystore a partial copy of the distributed ledger 21908. In someembodiments, each of the nodes 21916 may execute a respective agent22020. An agent 22020 may be configured to perform one or more ofmanaging the local copy 21908-L of the distributed ledger 21908associated with the node 21916 that executed the agent 22020, helpingverify blocks that were previously stored on the distributed ledger21908, helping verify requests from other nodes 21916 to store newblocks on the distributed ledger 21908, requesting permission to performoperations relating to the digital knowledge or management thereof onbehalf of a user associated with the node 21916 on which the agentresides, and/or facilitating collaboration between one or more of theknowledge providers 21906 and/or one or more of the knowledge recipients21918 (e.g., using knowledge provider device(s) 21990 and/or knowledgerecipient device(s) 21994, respectively), such as by assisting withvalidation and/or transfer of one or more instances of the digitalknowledge 21904 and/or executing one or more clauses of one or moresmart contracts 21940. It is understood that nodes may performadditional or alternative tasks without departing from the scope of thedisclosure.

In some embodiments, the distributed ledger system 20950 may include asmart contract system 21968 configured to generate smart contracts 21940and deploy the smart contracts 21940 to the distributed ledger 21908. Inembodiments, a smart contract 21940 may refer to a piece of softwarestored on the distributed ledger 21908 and configured to manage one ormore rights associated with one or more instances of the digitalknowledge 21904 and/or one or more knowledge tokens 22038. Inembodiments, the smart contract may be a computer protocol that assistswith negotiation and/or performance of terms in an agreement (e.g.,distributed on blockchain such as Ethereum blockchain). The smartcontract may be used in banking, government, management, supply chain,automobiles, real estate, health care, insurance, etc. In someembodiments, the smart contract 21940 may be contained and/or executedin a virtual machine or a container (e.g., a Docker container). In someembodiments, one or more of the nodes 21916 of the ledger network 21070may provide an execution environment for the smart contract 21940. Inembodiments, a smart contract 21940 may include information, data,and/or logic related to an instance of digital knowledge 21904, one ormore triggering events, one or more smart contract actions to beexecuted in response to detection of one or more of the triggeringevents, and the like. In embodiments, the triggering events may defineconditions that may be satisfied by events performable by one or moreusers, such as the knowledge provider 21906, the knowledge recipient21918, and/or the crowdsource 21936, or by one or more third parties.Examples of the triggering events include payment of one party byanother party, adherence or lack of adherence to one or more terms of asales, licensing, insurance, or other agreement made by one or moreparties, meeting of one or more thresholds or ranges of properties ofone or more pieces of the digital knowledge 21904, such as value, userrating, production amount, or any other suitable property, passage oftime, or any other suitable triggering event. Additionally oralternatively, the triggering events defined in a smart contract 21940may include conditions that may be satisfied independently of an actionor inaction of a human. For example, a triggering event may be when acertain date is reached, when a stock price reaches a certain threshold,when patent rights expire, when a copyright expires, when a naturalevent occurs (e.g., a hurricane, a tornado, a drought, or the like),etc. Triggering events may be defined as different types of triggers.For example, triggers or triggering events may refer to changing states(e.g., state change event) such as where the smart contract is activeupon a set of data states (e.g., state change events). In otherexamples, triggers or triggering events may refer to events that occursuch that users may need to passively wait for the events to occur andthe distributed ledger system 20950 may need to monitor for theseevents.

In embodiments, smart contract actions 22086 may include, for example,monitoring events from a defined data source, verifying fulfillment ofobligations of one or more users and/or third parties according to oneor more conditions 22084 defined in the smart contract 21940, verifyingpayment and/or transfer of tokens, property, other goods, or services,between one or more users and/or third parties, transferring the digitalknowledge 21904 between parties or to one or more users, logging one ormore transactions in the distributed ledger 21908, performing one ormore operations with respect to the distributed ledger 21908, creatingone or more new blocks 22022 in the distributed ledger 21908, and thelike. In some embodiments, a smart contract 21940 may include an eventlistener 22080 that is configured to monitor one or more data sources(e.g., databases, data feeds, data lakes, public data sources, or thelike) for detecting events to determine whether one or more conditions22084 are met. For example, an event listener 22080 may listen to anapplication programming interface (API) that provides a connectionbetween the distributed ledger system 20950 and a printer, such that asmart contract may trigger an obligation of a user to make a paymentwhen a printing instruction set governed by the knowledge distributionset (such as a tokenized instruction set in a knowledge token 22038) isused to print an item using the instruction set. Thus, when a predefinedset of conditions 22084 is met, then a smart contract action 22086 maybe triggered. This may include triggering a payment process (such asinitiating an authorization of a payment on a credit card), closing outa contract (such as when a prepaid number of uses of a knowledge set hasbeen reached), determining a price (such as by initiating a reference tocurrent pricing data in a marketplace or exchange), reporting on anoutcome (such as reporting a workflow or event), or the like. Inresponse to being triggered, the smart contract may automaticallyexecute the smart contract action 22086. In some embodiments, the smartcontracts are Ethereum smart contracts and may be defined in accordancewith the Ethereum specification, which may be accessed athttps://github.com/ethereum, the contents of which are incorporated byreference. In other embodiments, the smart contract system 21968 mayinclude the event listener 22080.

In some embodiments, the ledger management system 21910 may define oneor more operations that may handle or process commitments of one or moreparties to the smart contract 21940 and/or terms thereof. When a set ofparties (e.g., knowledge providers 21906, knowledge recipients 21918,crowdsources 21936 and/or third parties) commit to the terms of a smartcontract 21940 to a term of a smart contract governing the transfer ofdigital knowledge 21904, the distributed ledger system 20950 (and/or thesmart contract 21940 itself) may handle or process commitments of theparties and/or identifiers of the parties to one or more portions (e.g.,terms) of the smart contracts 21940. In embodiments, upon a set ofparties committing to a smart contract 21940, the smart contract 21940and/or the distributed ledger system 20950 may link one or more of theparties to one or more of the triggering events defined in the smartcontract 21940, begin monitoring one or more data sources to determinewhether any conditions 22084 defined as trigger events are met, and/orautomatically perform operations/actions defined in the smart contract(e.g., in response to the occurrence of a triggering event). Forexample, a knowledge provider 21906 may upload a smart contract 21940(e.g., using knowledge provider device 21990) to the distributed ledger21908 and/or customize a smart contract 21940 using a smart contracttemplate in connection with uploading an instance of the digitalknowledge 21904. In embodiments, the knowledge provider 21906, aknowledge recipient 21918, or some other party may indicate (e.g., viathe distributed ledger system 20950, the distributed ledger 21908,and/or the smart contract 21940) terms of an agreement between theknowledge provider 21906 and the knowledge recipient 21918 upon anagreement being formed between the knowledge provider 21906 and theknowledge recipient 21918. In some embodiments, the smart contract 21940may include one or more rights, terms, and/or obligations provided bythe knowledge provider 21906 and/or a third party prior toidentification of and/or dealing with the knowledge recipient 21918. Theknowledge recipient 21918 may agree to be bound by rights, terms, and/orobligations defined via the smart contract 21940 upon agreeing toreceive the digital knowledge 21904 (e.g., using knowledge recipientdevice 21994). The knowledge recipient 21918 may be a user who iswilling to transact (e.g., buy, license, or otherwise make a deal withthe knowledge provider 21906) for the digital knowledge 21904. The smartcontract 21940 may commit or otherwise bind (or process commitments) theknowledge provider 21906, the knowledge recipient 21918, and/or otherparties to the agreement to terms and/or conditions 22084 of the smartcontract 21940 in response to receiving indication via the distributedledger system 20950 and/or the distributed ledger 21908.

In some embodiments, the distributed ledger system 20950 may include anaccount management system. In embodiments, the account management system21946 may facilitate creation and/or storage of user accounts related tousers of the distributed ledger system 20950, the distributed ledgersystem 20950, and/or the distributed ledger 21908. For example, theaccount management system 21946 may be configured to facilitateregistration of one or more of the knowledge providers 21906, theknowledge recipients 21918, the crowdsources 21936, and/or other thirdparties that may be associated with the distributed ledger system 20950,the distributed ledger system 20950, and/or the distributed ledger21908. In some embodiments, the account management system 21946 may beconfigured to, together with the ledger management system 21910,facilitate intake of data from registered users of the distributedledger 21908, such as name, address, company affiliation, financialaccount information (e.g., bank account numbers and/or routing numbers),digital identifiers (e.g., IP addresses, MAC addresses, and the like),and any other suitable information related to the registered users.

The account management system 21946 may update the user account of theregistered user with data taken in and related to the registered user.In some embodiments, the account management system may facilitategeneration and/or distribution of one or more of the permission keys22032 to one or more of the registered users. The permission keys 22032may provide the registered user with access to one or more instances ofthe digital knowledge 21904 and/or services associated with thedistributed ledger system 20950.

In some embodiments, the distributed ledger system 20950 may include auser interface system 21950 configured to present a user interface. Theuser interface may be configured to facilitate uploading of digitalknowledge 21904, generation and/or uploading of a smart contract 21940,and viewing of the digital knowledge 21904 and/or the smart contract21940 (and statuses thereof). The user interface may be a graphical userinterface. Information presented to users of the distributed ledgersystem 20950 via the user interface may include descriptions of one ormore instances of the digital knowledge 21904, ownership and/orlicensing information related to the one or more instances of thedigital knowledge 21904, information related to the user viewing theuser interface and/or other users of the distributed ledger system20950, price information related to one or more instances of the digitalknowledge 21904, statistics and/or metrics related to the distributedledger 21908 and/or contents thereof, such as node count, payouts forgeneration of additional nodes, and any other suitable information. Insome embodiments, users may view contents of their digital wallets viathe user interface, such as a balance of one or more types of currencytokens.

In some embodiments, the user interface may be configured to allow oneor more users to perform one or more of the operations related to thedigital knowledge 21904 and/or the distributed ledger 21908, such asbuying, selling, verifying, and/or reviewing the digital knowledge 21904and/or performing other operations related to the distributed ledger21908 discussed herein. For example, the knowledge provider 21906 mayselect a computer file (such as a 3D printer schematic file) to uploadto the distributed ledger 21908 via the user interface (e.g., usingknowledge provider device 21990). The user interface may present theknowledge provider 21906 with one or more options related to uploadingthe digital knowledge 21904, such as an ability to configure a smartcontract 21940 and related terms for wrapping and/or tokenizing thedigital knowledge 21904. Other options may include privacy options, suchas options pertaining to one or more users or classes of users who mayand/or may not view, buy, sell, license, rate, verify, review, orotherwise manage or interact with the digital knowledge 21904.

In embodiments, the distributed ledger system 20950 may include one ormore datastores 21958. In some embodiments, the distributed ledgersystem 20950 may include one or more datastores 21958 configured tostore data related to the digital knowledge 21904, the distributedledger 21908, the knowledge providers 21906, the knowledge recipients21918, the crowdsources 21936, the knowledge tokens 22038, the smartcontracts 21940, the account management system 21946, the marketplacesystem 21954, or any other suitable type of data. A datastore may storefolders, files, documents, databases, data lakes, structured data,unstructured data, or any other suitable data.

In some embodiments, the datastores 21958 may include a knowledgedatastore 22060 configured to store data. The knowledge datastore 22060may be in communication with the user interface system 21950. The userinterface system 21950 may be configured to populate the user interfacewith data stored in the knowledge datastore 22060. In some embodiments,data stored in the knowledge datastore 22060 may include knowledgerelated to the digital knowledge 21904 such as source, reviews, price,ownership, licensing, related knowledge providers 21906, relatedknowledge recipients 21918, serial numbers, related crowdsources 21936,or any other suitable information. For example, the knowledge datastore22060 may contain information related to a 3D printer schematic such asorigin, date of creation, names of one or more contributing individuals,groups, and/or companies, pricing, market trends for related schematics,serial numbers and/or part identifiers, and any other suitable type ofdata related to the 3D printer schematic.

In some embodiments, the datastores 21958 may include a client datastore22062 (e.g., may include user datastore), the client datastore 22062being configured to store data relating to users of the distributedledger system 20950. The client datastore 22062 may be in communicationwith the account management system 21946 and may be populated with useraccounts related to one or more of the user accounts, data contained inone or more of the user accounts, data related to the one or more useraccounts, and/or a combination thereof.

In some embodiments, the datastores 21958 may include a smart contractdatastore 22064. In embodiments, the smart contract datastore 22064 isconfigured to store data related to one or more of the smart contracts21940 and/or smart contract templates (from which smart contracts 21940may be parameterized and instantiated). In embodiments, the smartcontract datastore 22064 may be in communication with the ledgermanagement system 21910. Data stored in the smart contract datastore mayinclude, for example, smart contract templates, one or more smartcontracts 21940, data related to instances of the digital knowledge21904 related to one or more of the smart contracts 21940, data relatedto parties to one or more of the smart contracts 21940, and any othersuitable data. The smart contract datastore 22064 may be configured tostore completed smart contracts that have already been executed. Thesmart contract datastore 22064 may be configured to store smartcontracts that have not yet been uploaded to the distributed ledger21908.

FIG. 221 illustrates a method 22100 of performing high level processflow of a smart contract that distributes digital knowledge. Inembodiments, the smart contract may be a knowledge token that is storedon the distributed ledger and that is executed by one or more nodes thathost the distributed ledger. In some of these embodiments, the smartcontract may be executed on a virtual machine or in a container.

At 22110, the smart contract monitors one or more of the conditionsdefined in the smart contract. In some embodiments, an event listenerobtains data (either passively or actively) from one or more datasources defined in the smart contract 21940. As the event listenerobtains data from the one or more data sources, the smart contract maydetermine whether certain conditions are met, and if so, may perform anaction that is triggered by the met conditions.

At 22112, the smart contract verifies conditions for execution of one ormore terms of the smart contract, and at 22114, the smart contractinitiates execution of the one or more terms of the smart contract. Inembodiments, the smart contract may include an event listener thatdetermines whether a requisite amount of funds have been deposited tothe smart contract. Once a party has deposited the requisite amount offunds (e.g., a predefined amount of cryptocurrency or fiat currency),the smart contract may initiate the transfer of the digital knowledge tothe knowledge recipient (e.g., the party that deposited the requisiteamount of funds). In embodiments, this may include updating thedistributed ledger with a block that indicates the change in ownershipof the token to the knowledge recipient and providing any required keysto the knowledge recipient. Once the ownership of the knowledge tokenhas been changed, the knowledge recipient may access the digitalknowledge contained therein (and in accordance with any restrictionsdefined in the smart contract, such as using a particular type ofdevice).

Referring to FIG. 217 , in embodiments, the analytics system 20940 (withoptional reporting capabilities) may be configured into and/or inassociation with a gaming engine and smart contracts execution platform20900. The analytics system 20940 may provide a range of analysisservices to other components of the platform, such as analysis of dataprovided to an intelligence layer 21100 for use in artificialintelligence and the like. An intelligence layer 21100 may utilize ananalysis system 20940 to provide comparative analysis of a range ofintelligence functions, outcome ranking approaches, and the like. Theintelligence layer 21100 may further engage analytics and reportingsystem capabilities for analysis support of intelligence layer analysismodules 21112. The analytics and reporting system 20940 may facilitateanalysis for a risk analysis intelligence analysis module 21702. Otherintelligence analysis modules 21112 that may leverage at least theanalytics capabilities of the analytics and reporting system includemodules for security analysis 21712 (e.g., analyze security measures andremediation actions), ethics analysis 21716 (e.g., a structuredanalytical approach to ensure compliance with ethics factors), hazardanalysis 21720, quality analysis 21724, safety analysis 21722, FMEAanalysis 21718 (e.g., apply failure mode analysis algorithms), decisiontree analysis 21714 (e.g., provide analysis of multiple decisionoptions), risk analysis 21710 (e.g., perform analysis of variousscenarios for risk implications), and the like. In embodiments, ananalysis module management 21116 module may provide a set of interfaces,such as APIs and the like, through which the analytics and reportingsystem 20940 may communicate (e.g., provide analytics services) with oneor more intelligence analysis modules 21112.

In embodiments, the analytics system 20940 may further perform analysisfunctions for or as a part of a data processing system 20930 of theplatform. In an example, data analysis algorithms (optionally embodiedas software functions and the like) may be used by a data processingsystem 20930 to perform analysis of data being processed by the dataprocessing system 20940. The data processing system 20930 may utilizethe analytics system 20940 to handle various statistical analysisfunctions and the like. A reporting system (optionally a portion of ananalytics and reporting system 20940 of the gaming engine and smartcontracts execution platform 20900) may use capabilities of an analysissystem to determine one or more optimal reporting schedules and thelike. In an example, an analysis system 20940 may facilitate analyzing adegree to which information to be reported changes over time, therebyenabling a reporting system to direct computing resources of the dataprocessing system 20930 to process data in reporting areas in whichinformation is more regularly changing, such as by reducing reportingfrequency of infrequently changed information.

In embodiments, an analytics system may facilitate automated adaptationof an interface system 20980 of the platform, such as by analyzingefficiency of users accomplishing one or more outcomes within a userinterface portion of an interface system of the platform 20900.Similarly, for a computer-to-computer interface portion of the interfacesystem 20980, the analytics system 20940 may facilitate determining, forexample, an optimal range of a count of active input/output ports toachieve an interface goal, such as ensuring a level of service forresponse time to external computer system requests for access to theplatform. In embodiments, an analytics system of the platform may play arole in ensuring platform security in cooperation with a platformsecurity system 20960, such as by updating security statistics of theplatform (e.g., rates of attempted security breaches, ranking of malwareremediation approaches, and the like).

Referring to FIG. 218 , in embodiments, an analytics and reportingsystem 20940 may provide analytics and/or reporting services to adistributed ledger system 20950 of the platform 20900. A distributedledger system 20950 of the platform may utilize reporting services togenerate reports of transactions, transaction outcomes, and the like foruse by, for example, a blockchain smart contract data management service20952 and the like. Other exemplary uses of an analytics and reportingsystem 20940 by a distributed ledger system 20950 include providinganalysis of blockchain smart contract transaction services 20951, suchas escrow services to ensure, among other things, that transactionscenarios that may impact escrow management are thoroughly vetted. Infurther exemplary embodiments, transaction outcome analysis, such as fordetermining carbon offset credits required for carbon-neutraltransaction services may be provided by or in association with theanalytics and reporting system 20940. The analytics and reporting system20940 may also provide monitoring services to a distributed ledgersystem 20950, such as to monitor activity levels (e.g., via network dataanalysis) of nodes of a distributed ledger architecture and the like.

An analytics and reporting system 20940 of the platform may performanalysis of external sensor data that is accessed by the platformthrough an external data and services platform capability 21060 toverify, for example, smart contract execution. In an example, externalsensor data may record production information for a production line thatis called out in a smart contract as determining a daily allocation offinancial accruals per contract terms. The analysis and reporting systemmay perform analysis of the production line sensor data against contractterms, such as minimum quantities, yield, bonus quantities and the likefor a plurality of production lines and report a result of analysis intoan accrual data set. The reported data from the analysis may includeproduction line-specific accruals, deficits, and the like on a timedbasis, such as hourly, daily, per production shift, and the like.

Computing capabilities of an analytics and reporting system 20940 of theplatform may include platform-accessible web servers, data processingsystems, and quantum computing services, such as cloud-based quantumcomputing services and the like. In embodiments, the analytics system20940 may use conventional computing capabilities where doing soprovides an acceptable level of performance, accuracy, and/orreliability of analysis. The analytics system 20940 may also or insteadrely on quantum computing services when a desired level of accuracy,reliability, or the like is not being achieved through the use ofconventional computing services.

In embodiments, an analytics capability of a platform for integratinggaming engine capabilities with smart contract capabilities 20900, suchas in a common execution framework, may facilitate providing analysisfor smart contract services, such as for any of development, management,and execution of smart contracts. The analytics and reporting system20940 of the platform may facilitate providing analysis services to oneor more components and/or services of a smart contracts system 21040portion of the platform. The smart contracts system 21040 may provide asmart contracts configuration service to facilitate access to, forexample, smart contracts in a contracts library 21042 and the like.Configuring smart contracts, such as through a smart contractsconfiguration service 21044, may also include smart contracts analysis.In embodiments, smart contracts analysis may facilitate determining,such as in response to a request for smart contracts configuration, asuitability of a smart contract, such as a contract from the smartcontracts library, to satisfy one or more configuration parameters. Inan example, a smart contracts configuration service 21044 may beaccessed to configure one or more smart contracts for operating aninsurance provisioning service in a target jurisdiction, such as a statein the United States or a county in such a state, or even a city orlocal town in such a state. The analysis and reporting system 20940 ofthe platform may be engaged to analyze a plurality of smart insurancecontracts, such as in a smart contracts library 21042 for, among otherthings, compliance with insurance industry regulations in the targetjurisdiction. In another example, a smart contracts configurationservice request may include provisioning insurance contracts that meet aminimum quality rating, such as based on a national insurance providerrating service, a local business bureau rating service, and the like.The analysis and reporting system 20940 could be engaged in this exampleto analyze and report on the pertinent quality rating ofjurisdiction-specific smart insurance contracts (e.g., contract termsand the like) in, for example, the smart contracts library 21042. In yetanother example, the analysis and reporting module 20940 may be engagedto perform performance analysis of various smart contracts, such asbased on uses of the contracts for historical smart contractsconfiguration service requests. In this example, a base auctions smartcontract performance may be analyzed in comparison to a customized(e.g., third-party contributed, machine-learning enhanced, artificialintelligence-based) auctions smart contract performance to, among otherthings, determine if the base contract provides an acceptable level ofperformance to satisfy a corresponding requirement in a request forsmart contracts configuration that is being serviced by the smartcontracts configuration service 21044 of the smart contracts system21040. In summary, an analytics and reporting system 20940 may workcooperatively with a smart contract system 21040 for a range of analysisservices including, without limitation: analyze contract terms forsuitability; provide monitoring and notification services for contractcompliance during operation; analyze deliverables for verification ofcontracted terms; provide analysis of custom contracts against a set ofcontract performance parameters; provide monitoring and reportingservices for discovery of new and changes to existingexternal/public/customized contract segments.

In embodiments, the analytics and reporting system 20940 may work incooperation with a plurality of platform modules, such as the gamingengine system 20902 and the intelligence layer 21100 to, for example,identify and report fraudulent and/or anomalous activity during smartcontract development and/or execution. As an example, the analytics andreporting system 20940 may perform a trend analysis of a smart contractinstance. The analytics and reporting system 20940 may compare resultsof any portion (including temporally incomplete portions) of that trendanalysis with a corresponding reference trend, such as a generalized setof historical data associated with earlier instances of the same orsimilar smart contracts to detect, flag, and optionally report anomaliesand potentially problematic trends.

In embodiments, an analytics and reporting system 20940 of the platformmay facilitate providing analysis services to one or more componentsand/or services of a gaming engine system 20902 portion of the platform.The gaming engine system 20902 may provide a gaming engine configurationservice 21030 to facilitate access to, for example, gaming enginemodules in a module library 21032 and the like. Configuring gamingengines, such as through a gaming engine configuration service 21030,may also include gaming engine module analysis 21034. In embodiments,gaming engine module analysis 21034 may facilitate determining, such asin response to a request for gaming engine configuration, a suitabilityof a module, such as a module from the gaming engine module library21032, to satisfy one or more configuration parameters. In an example, agaming engine configuration service may be accessed to configure one ormore gaming engines for operating an insurance provisioning service in atarget jurisdiction, such as a state in the United States or a county insuch a state, or even a city or local town in such a state. The analysisand reporting system 20940 of the platform may be engaged to analyze aplurality of gaming engine insurance modules in a gaming engine modulelibrary 21032 for, among other things, compliance with insuranceindustry regulations in the target jurisdiction. In another example, agaming engine configuration service request may include provisioninginsurance services that meet a minimum quality rating, such as based ona national insurance provider rating service, a local business bureaurating service, and the like. The analysis and reporting system 20940could be engaged in this example to analyze and report on the pertinentquality rating of jurisdiction-specific insurance modules in the gamingengine module library 21032. In yet another example, the analysis andreporting system 20940 may be engaged to perform performance analysis ofvarious gaming engine modules, such as based on uses of the modules forhistorical gaming engine configuration service requests. In thisexample, a base auctions gaming engine module performance may beanalyzed in comparison to a customized (e.g., third-party contributed,machine-learning enhanced, artificial intelligence-based) auctionsgaming engine module performance to, among other things, determine ifthe base model provides an acceptable level of performance to satisfy acorresponding requirement in a request for gaming engine configurationthat is being serviced by the gaming engine configuration service 21030of the gaming engine system 20902.

In embodiments, the analytics and reporting 20940 system may provideanalysis and may optionally provide reporting services for variousgaming engine analysis operations, such as simulation of gaming enginesmart contract options. Through use of analysis of simulation outcomesfor a range of criteria, such as performance, governance compliance,reliability, ease of use, cost to implement, and the like, gaming enginesmart contract options may be analyzed and compared. Further byapplication of simulation outcome analysis, the analytics and reportingsystem may support autonomous and/or semi-autonomous smart contract termselection. The analytics and reporting system 20940 may coordinatesimulation outcome analysis with artificial intelligence services 21120of the intelligence layer 21100 to train the simulation system forimproving simulation confidence and compliance with, for example,real-world smart contract and gaming engine operations. In someembodiments, ML/AI supports automation, detection, decision support,and/or categorization/clustering.

In embodiments, the intelligence layer works with the gaming enginesystem and other platform elements and sub-elements and a range ofexternal data and services to facilitate operations in association withthe development and execution of gaming engine smart contracts.Artificial intelligence services include capabilities such as machinelearning, rules-based systems, RPA, digital twins, machine vision, NLP,neural networks, etc. These services can be combined in any way tofacilitate all types of analyses, decisions, recommendations, etc.associated with all aspects of development and execution of gamingengine smart contracts.

Areas for AI automation are broad. A partial list includes: automatedsmart contract code development, contract parsing, contract elementselection and assembly to produce a complete contract, contractexecution feedback and learning, selection of gaming engine tools forcontract verification, etc. Working with the data processing system andthe smart contract system, the intelligence layer can be configured toautomatically monitor smart contract execution and provide alerts andnotifications associated with contract terms, governance, fraud, etc.

The intelligence layer provides a range of AI and governancecapabilities and libraries that work with the smart contract system andexternal data and services to provide governance and compliance analysissupport. Examples include determination of fairness, regulatoryconformance, determination of whether contract terms were met, etc.

Working with the data processing system, the intelligence layer providesa range of AI capabilities to help with mechanisms related tocategorizing and grouping smart contract transactions, contract types,clients associated with certain contracts, and so forth. Possibleapplications include maintaining, updating, and organizing librariesbased on contract outcomes, using similar transaction data to parsecontract requests into an executable contract, etc. (see also smartcontract libraries).

The intelligence layer provides a digital twin capability that workswith the smart contract system. Digital twin services can be provided toPlatform users through the interface system as a digital twin userinterface, for example a GUI, to help visualize contract execution basedon a range of data inputs such as weather, users, economic conditions,etc. More generally, a digital twin library can be developed andmaintained for smart contract elements and assemblies for use with arange of analyses.

The intelligence layer provides digital twins that can bebi-directionally embedded or associated with various smart contractelements through an API or other means so that the digital twins canincorporate live marketplaces based on smart contracts for variousservices such as insurance, leasing, etc. In-twin marketplaces canprovide richer tools for making contract decisions and adjustments basedon real-time data. This capability may also introduce the possibilityfor the execution of phased contracts based on combined simulations andactual execution.

In embodiments, the analytics and reporting system 20940 may engage withdigital twin capabilities for performing simulation. In an example,digital twin-specific analytics capabilities may be provided for the useof digital twins in performing gaming engine smart contract simulations.Such analytics capabilities may be provided as an on-demand service bythe analytics and reporting system 20940 responsive to a request foranalytics capabilities by a digital twin associated with gaming enginesmart contract simulation. In an example of automated smart contractterm selection, a client may request a contract for a house paintingservice. In response, the platform may perform simulations of housepainting service smart contracts based on, among other things, dataassociated with such a request (e.g., a jurisdiction, a contact volume,a target cost and risk of ensuring contract compliance and execution,and the like). Through application of the analytics and reporting system20940 to analyze one or more outcomes of the simulations of housepainting service smart contract options available in, for example, alibrary of smart contracts 21042 and to report a result of the analysisof these options, the platform may provide a ranked selection ofcontract proposals, including complete contracts to the client.

In general, the analytics and reporting system 20940 may be engaged toperform analysis of a wide range of platform operations, fromdetermining alignment between business plans and business outcomes,demand for computing services, evaluating a range of client businessesengaging the platform, compliance with local regulations across a rangeof deployment jurisdictions, measures and metrics of new clientengagement timing (e.g., time from initial engagement to some form ofservice provisioning), and the like. This analysis of platformoperations may be performed on an as needed basis (e.g., based on athird party smart contract or other provider requests for co-marketingthe third-party services through the platform), on a periodic basis(e.g., each month, quarter, or the like), on a platform developmentschedule (e.g., ahead of a planned expansion or new release of a portionof the platform), based on a financial planning schedule (e.g., whendeveloping budget requirements for an upcoming term, such as a newfiscal year), and the like.

In embodiments, an analytics and reporting system 20940 of the platformmay provide analysis services, functions, algorithms, and the like thatsupports opportunity mining and/or discovery of gaming engine smartcontract capabilities, services, and functions. In an example, theanalytics and reporting system 20940 may work with, for example, thesmart contract system 21040 and optionally with one or more intelligencelayer services 21100 to proactively identify and develop smart contractconfigurations based on analysis of platform activity and informationaccessible to and optionally produced by the platform. One such analysismay include using the analytics and reporting system 20940 to identifyuse trends to facilitate prediction of smart contract configurations,services, terms, and the like.

In embodiments, analytics and reporting in a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework may be provided by an analytics and reporting system20940, such as the one described herein. An analytics and reportingsystem 20940 may facilitate analysis and reporting on, among otherthings, various aspects of platform operation such as system status,system statistics, operational capabilities along with other platformfunctions, such as key performance metrics, error conditions, riskconditions, and so forth. Output, such as those mentioned above andothers, of this system may be provided to the interface system fordisplay on a platform administrator dashboard (e.g., GUI). In anotherexample, output of the analytic and reporting system 20940 may be usedby the intelligence layer 21100 to help train machine learningalgorithms for, among other things, optimizing outcomes for variousaspects of the platform, including smart contract configuration, gamingengine recommendations, and integration of gaming engine features withsmart contract services.

The analytics and reporting system 20940 may be embodied as one or morestandalone computing systems in communicating relationships with othercomponents of the platform 20900. Such a standalone computing system maybe configured with local data storage that may provide access toanalytic algorithms, historical analysis data, and the like. Inembodiments, access to such algorithms, historical data, and the likemay be provided by one or more other portions of the platform, such asthrough networked connection to data storage facilities, via API calls,and the like.

In embodiments, the analytics and reporting system 20940 may beconfigured as a set of software programs that may be accessible incomputer storage facilities of the platform and may be accessible toprocessors and other computing facilities of the platform. Inembodiments, software program embodiments of the analytics and reportingsystem 20940 may be accessible as one or more sets of third-partyservices to which the platform may acquire access, such as through asubscription for analytic services, reporting services, and the like.

In embodiments, an analytics and reporting system 20940 of the platformmay provide ancillary services that may leverage one or more ofanalytics capabilities and reporting capabilities to enhance serviceprovisioning of the platform. Such enhanced service provisioning mayinclude, among other things, further automating of analytic functionsthereby extending a range of service offerings (e.g., smart contractoptions) in association with third parties. Example ancillary servicesof an analytics and reporting system 20940 may include databasesearching 20941, monitoring and notification 20942, marketing,advertising, research 20943, data stories support, and the like. In asimple example application of a marketing and/or advertising ancillaryservice, third party smart contract authors, providers, facilitators,and the like may gain access to platform client requests for gamingengine enhanced smart contracts. The analytics and reporting system20940 may provide contextual information based on analysis of, forexample, platform client requests, that may be used by a marketingand/or advertising ancillary service to determine sample criteria foraccepting smart contract proposals from third parties. This analysis mayinclude, for example, the types of smart contracts being requested byclients, the types of terms of smart contracts most likely to beselected by clients, the nature of business situations to which clientsrequest application of smart contracts, and the like. Through thisanalysis, it may, for example, be reported by the analytics andreporting system 20940 that third party smart contract providers whooffer contract term execution monitoring capabilities are preferred oversmart contract offerings that require a client to engage with additionalvendors for such services.

Marketing, advertising, search, and transaction systems are provided insome embodiments.

In embodiments, the analytics and reporting system 20940 incorporatesone or more of the ancillary services including any of marketing,advertising, and research 20943 that enables the platform to performtargeted analysis, such as analysis of contract execution data, targetedadvertising based on proposed or requested client services, activemarketing to platform users, and so forth. This type of activity mayinherently establish a market that can use the intelligence layer 21100and gaming engine system 20902 to perform targeted research.

In embodiments, an analytics and reporting system 20940 may facilitateoffering and/or access by platform clients to data stories capabilities.Data stories describe, illustrate, or otherwise provide detailedunderstanding regarding an area of interest, where available data isanalyzed and presented for a specific audience or client. For example, afinancial analysis, optionally performed by the analytics and reportingsystem 20940, for evaluating different risk scenarios for gaming engineintegration with business applications might employ a gaming engine forboth simulation and visualization for a chief financial officer (CFO) orother senior officers of a client business entity and the like. In thiscontext, data stories may be provided as a service within the smartcontract system that may be supported by the analysis and reportingsystem 20940. In embodiments, data stories may be included as anancillary service of the data analytics and reporting system 20940.

As noted above, the analytics and reporting system 20940 may provideancillary services. An ancillary service of the analytics and reportingsystem may include working with one or more of the intelligence layer21100, the data processing system 20930, and the user interface system20980, to provide various search capabilities for internal and externalclients. In examples, one or more processors of the analytics andreporting system 20940 (or executing routines of the analytics andreporting system) may provide database search capabilities 20941.Database search capabilities may include search query generationassistance, such as deriving search queries based on, for example,analysis of parameters associated with a search request. Parametersassociated with a search request may include search terms, searchtargets (e.g., private search databases, public/Internet databases, andthe like), and the like. However, parameters associated with a searchrequest may include other aspects, such as a target or maximum cost ofthe search, search terms constraints (e.g., no plurals, literalword/term occurrence, and the like), a frequency of searching (e.g., forongoing searches, such as to populate terms of a smart contract and thelike), turnaround time for a response that exhibits a confidence levelabove a search confidence threshold (e.g., to achieve a set of targetedresults), and the like. These and other search request parameters may beexplicit (e.g., specified by the request) and/or implicit (e.g., basedon prior search requests, keywords, search targets, and the like).

In embodiments, a searching ancillary service may leverage capabilitiesof other portions of the platform, such as the intelligence layer 21100for accessing intelligence services (e.g., artificial intelligence21120) to enrich search query generation, analysis of results, and thelike.

The analytics and reporting system 20940 may further offer monitoringand notification 20942 ancillary services. These ancillary services may,in example embodiments, be combined with one or more core functions ofthe platform to, for example, improve performance thereof. In anexample, a monitoring ancillary service 20942 may monitor for a criticalchange in statistics associated with operations of the platform, such asa decrease greater than 10% across units of time (e.g.,month-over-month, week-over-week, and the like) in requests for gamingengine and/or smart contract integration services. When critical changesare detected, a notification function may provide notification to aplatform owner, operator, and the like to facilitate taking remedialaction, such as attempting to mitigate an impact of the decrease inrequests on profitability of the platform. In embodiments, anotification ancillary service 20942 may attempt to notify platformusers, such as select clients of the platform, when services requestedof the platform, such as optimizing outcomes for a set of candidategaming engine smart contract configurations are completed or nearly so.Similarly, the notification ancillary service 20942 may work incooperation with the reporting services to deliver to targeted clientsand/or platform administrators results of operational analysis, such asa completion of a service request, a delay in completion of the servicerequest, and the like. In addition to monitoring platform operations,statistics, metrics, and the like (at least a portion of which may begenerated by the analytics capabilities of the analytics and reportingsystem 20940), a monitoring ancillary service 20942 of the analytics andreporting system may facilitate monitoring smart contract terms and/orvariables during, for example, operation of a smart contract portion ofa gaming engine/smart contract integration. A monitoring ancillaryservice 20942 may include monitoring smart contract term consumption,usage, user ratings, and the like. By monitoring these aspects of use ofthe platform, the analytics capabilities may be used to determinecontextual information about contract terms that might be useful inimproving platform performance. In an example, it may be determined, byanalysis of monitored contract term usage, that certain contract termsare more popular, that some contract terms are applied across a widerange (or narrow range) of types of contracts, and/or that contractsuccess may be impacted by some terms more than others. These are just afew of the potential outcomes and insights possible when combiningmonitoring with analytics capabilities.

In embodiments, monitoring ancillary services 20942 may also facilitatemonitoring platform component integrity and security. In an example, thegaming engine system 20902 may work with the intelligence layer 21100,data processing system 20930, and the like during one or more of thephases of gaming engine smart contract development and execution toensure, among other things, regulatory and other types of compliance.The analytics and reporting system 20940 includes monitoring andanalytical capabilities that enable fraud detection, reporting, andverification of remediation (e.g., by monitoring for additionaloccurrences of a remediated fraud condition). In examples, smartcontract elements may be monitored; based on the monitoring, certaintypes of fraudulent smart contract elements may be identified (e.g.,internally or externally). In embodiments, remedial action may be taken.Also, such elements may be listed in a fraud element library tofacilitate preventing future use, much like isolating a computer virus.In another example, the smart contract execution system may employ oneor more services of the intelligence layer 21100 (e.g., artificialintelligence, machine learning, and the like) together with theanalytics and reporting system 20940 to identify and monitor dataanomalies that could be the result of fraud.

Combinations of embodiments are contemplated in yet further embodiments.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework and having an interface for a user to atleast one of operate, maintain, update, improve, or integrate theplatform. In embodiments, provided herein is a platform for integratinga set of gaming engines and a set of smart contract services in a commonexecution framework and having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework and having a set of gaming engines forrendering a visualization associated with a smart contract. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework and having a set of gaming engines for generatingand/or presenting a marketplace-related data story. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution framework andhaving a cryptocurrency system for enabling the trading ofcryptocurrencies. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework and having a smart contract system forenabling the creation of smart contracts. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework and having asmart digital wallet for storing funds. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework and having averification system for verifying that a party has performed one or moreobligations of a smart contract. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework and having adistributed ledger for recording electronic transactions. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework and having an AI-driven system for automaticallyconfiguring a smart contract. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework and having a machinelearning and/or artificial intelligence system for detecting performanceof a contract. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework and having a machine learning and/orartificial intelligence system configured to provide governance andcompliance support. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework and having a machine learning and/orartificial intelligence system for categorizing transactions. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework and having intelligent agents for undertakingworkflows related to smart contract configuration. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution framework andhaving intelligent agents for negotiating contract terms. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework and having intelligent agents for getting otheragents. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework and having intelligent agents for recognizing atarget. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework and having simulation systems. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution framework andhaving a fraud detection system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework and having aconfiguration and management system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework and having a digitaltwin and a digital twin user interface for user interaction with thedigital twin. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework and having a marketing and/oradvertising system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework and having at least one of an augmentedreality interface, a virtual reality interface, or a mixed realityinterface for the digital twin system. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework and having an in-twinmarketplace system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework and having a system for integrating withat least one hardware or software system to provide at least one ofend-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform and havinga smart contract that renders a visual representation of at least one ofobjects or events associated with the smart contract. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving an interface for a user to at least one of operate, maintain,update, improve, or integrate the platform and having a set of gamingengines for rendering a visualization associated with a smart contract.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform and havinga set of gaming engines for generating and/or presenting amarketplace-related data story. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an interfacefor a user to at least one of operate, maintain, update, improve, orintegrate the platform and having a cryptocurrency system for enablingthe trading of cryptocurrencies. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an interfacefor a user to at least one of operate, maintain, update, improve, orintegrate the platform and having a smart contract system for enablingthe creation of smart contracts. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an interfacefor a user to at least one of operate, maintain, update, improve, orintegrate the platform and having a smart digital wallet for storingfunds. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform and havinga verification system for verifying that a party has performed one ormore obligations of a smart contract. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an interfacefor a user to at least one of operate, maintain, update, improve, orintegrate the platform and having a distributed ledger for recordingelectronic transactions. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having an interface for a userto at least one of operate, maintain, update, improve, or integrate theplatform and having an AI-driven system for automatically configuring asmart contract. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having an interface for a user to atleast one of operate, maintain, update, improve, or integrate theplatform and having a machine learning and/or artificial intelligencesystem for detecting performance of a contract. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having aninterface for a user to at least one of operate, maintain, update,improve, or integrate the platform and having a machine learning and/orartificial intelligence system configured to provide governance andcompliance support. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having an interface for a user to atleast one of operate, maintain, update, improve, or integrate theplatform and having a machine learning and/or artificial intelligencesystem for categorizing transactions. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an interfacefor a user to at least one of operate, maintain, update, improve, orintegrate the platform and having intelligent agents for undertakingworkflows related to smart contract configuration. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving an interface for a user to at least one of operate, maintain,update, improve, or integrate the platform and having intelligent agentsfor negotiating contract terms. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an interfacefor a user to at least one of operate, maintain, update, improve, orintegrate the platform and having intelligent agents for getting otheragents. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform and havingintelligent agents for recognizing a target. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having aninterface for a user to at least one of operate, maintain, update,improve, or integrate the platform and having simulation systems. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform and havinga fraud detection system. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having an interface for a userto at least one of operate, maintain, update, improve, or integrate theplatform and having a configuration and management system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform and havinga digital twin and a digital twin user interface for user interactionwith the digital twin. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having an interface for a user to atleast one of operate, maintain, update, improve, or integrate theplatform and having a marketing and/or advertising system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform and havingat least one of an augmented reality interface, a virtual realityinterface, or a mixed reality interface for the digital twin system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an interface for a user to at least one ofoperate, maintain, update, improve, or integrate the platform and havingan in-twin marketplace system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an interfacefor a user to at least one of operate, maintain, update, improve, orintegrate the platform and having a system for integrating with at leastone hardware or software system to provide at least one of end-user orautomated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart contract that renders avisual representation of at least one of objects or events associatedwith the smart contract and having a set of gaming engines for renderinga visualization associated with a smart contract. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a smart contract that renders a visual representation of at leastone of objects or events associated with the smart contract and having aset of gaming engines for generating and/or presenting amarketplace-related data story. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract that renders a visual representation of at least one of objectsor events associated with the smart contract and having a cryptocurrencysystem for enabling the trading of cryptocurrencies. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a smart contract that renders a visual representation of at leastone of objects or events associated with the smart contract and having asmart contract system for enabling the creation of smart contracts. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract and having a smart digital wallet for storing funds. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract and having a verification system for verifying that aparty has performed one or more obligations of a smart contract. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract and having a distributed ledger for recording electronictransactions. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart contract that renders avisual representation of at least one of objects or events associatedwith the smart contract and having an AI-driven system for automaticallyconfiguring a smart contract. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract that renders a visual representation of at least one of objectsor events associated with the smart contract and having a machinelearning and/or artificial intelligence system for detecting performanceof a contract. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart contract that renders avisual representation of at least one of objects or events associatedwith the smart contract and having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract and having a machine learning and/or artificialintelligence system for categorizing transactions. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a smart contract that renders a visual representation of at leastone of objects or events associated with the smart contract and havingintelligent agents for undertaking workflows related to smart contractconfiguration. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart contract that renders avisual representation of at least one of objects or events associatedwith the smart contract and having intelligent agents for negotiatingcontract terms. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart contract that renders avisual representation of at least one of objects or events associatedwith the smart contract and having intelligent agents for getting otheragents. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract and having intelligent agents for recognizing a target.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract and having simulation systems. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart contract that renders a visual representation of at least one ofobjects or events associated with the smart contract and having a frauddetection system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart contract that renders avisual representation of at least one of objects or events associatedwith the smart contract and having a configuration and managementsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a smart contract that renders a visualrepresentation of at least one of objects or events associated with thesmart contract and having a digital twin and a digital twin userinterface for user interaction with the digital twin. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a smart contract that renders a visual representation of at leastone of objects or events associated with the smart contract and having amarketing and/or advertising system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract that renders a visual representation of at least one of objectsor events associated with the smart contract and having at least one ofan augmented reality interface, a virtual reality interface, or a mixedreality interface for the digital twin system. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart contract that renders a visual representation of at least one ofobjects or events associated with the smart contract and having anin-twin marketplace system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract that renders a visual representation of at least one of objectsor events associated with the smart contract and having a system forintegrating with at least one hardware or software system to provide atleast one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for rendering avisualization associated with a smart contract. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having a setof gaming engines for rendering a visualization associated with a smartcontract and having a set of gaming engines for generating and/orpresenting a marketplace-related data story. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having a setof gaming engines for rendering a visualization associated with a smartcontract and having a cryptocurrency system for enabling the trading ofcryptocurrencies. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a set of gaming engines forrendering a visualization associated with a smart contract and having asmart contract system for enabling the creation of smart contracts. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for rendering avisualization associated with a smart contract and having a smartdigital wallet for storing funds. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for rendering a visualization associated with a smart contractand having a verification system for verifying that a party hasperformed one or more obligations of a smart contract. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a set of gaming engines for rendering a visualization associatedwith a smart contract and having a distributed ledger for recordingelectronic transactions. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a set of gaming enginesfor rendering a visualization associated with a smart contract andhaving an AI-driven system for automatically configuring a smartcontract. In embodiments, provided herein is a platform for integratinga set of gaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for rendering avisualization associated with a smart contract and having a machinelearning and/or artificial intelligence system for detecting performanceof a contract. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a set of gaming engines forrendering a visualization associated with a smart contract and having amachine learning and/or artificial intelligence system configured toprovide governance and compliance support. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having a setof gaming engines for rendering a visualization associated with a smartcontract and having a machine learning and/or artificial intelligencesystem for categorizing transactions. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for rendering a visualization associated with a smart contractand having intelligent agents for undertaking workflows related to smartcontract configuration. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a set of gaming enginesfor rendering a visualization associated with a smart contract andhaving intelligent agents for negotiating contract terms. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for rendering avisualization associated with a smart contract and having intelligentagents for getting other agents. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for rendering a visualization associated with a smart contractand having intelligent agents for recognizing a target. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a set of gaming engines for rendering a visualization associatedwith a smart contract and having simulation systems. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a set of gaming engines for rendering a visualization associatedwith a smart contract and having a fraud detection system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for rendering avisualization associated with a smart contract and having aconfiguration and management system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for rendering a visualization associated with a smart contractand having a digital twin and a digital twin user interface for userinteraction with the digital twin. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for rendering a visualization associated with a smart contractand having a marketing and/or advertising system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a set of gaming engines for rendering a visualization associatedwith a smart contract and having at least one of an augmented realityinterface, a virtual reality interface, or a mixed reality interface forthe digital twin system. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a set of gaming enginesfor rendering a visualization associated with a smart contract andhaving an in-twin marketplace system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for rendering a visualization associated with a smart contractand having a system for integrating with at least one hardware orsoftware system to provide at least one of end-user or automatedservices.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for generating and/orpresenting a marketplace-related data story. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having a setof gaming engines for generating and/or presenting a marketplace-relateddata story and having a cryptocurrency system for enabling the tradingof cryptocurrencies. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a set of gaming engines forgenerating and/or presenting a marketplace-related data story and havinga smart contract system for enabling the creation of smart contracts. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for generating and/orpresenting a marketplace-related data story and having a smart digitalwallet for storing funds. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a set of gaming enginesfor generating and/or presenting a marketplace-related data story andhaving a verification system for verifying that a party has performedone or more obligations of a smart contract. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having a setof gaming engines for generating and/or presenting a marketplace-relateddata story and having a distributed ledger for recording electronictransactions. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a set of gaming engines forgenerating and/or presenting a marketplace-related data story and havingan AI-driven system for automatically configuring a smart contract. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for generating and/orpresenting a marketplace-related data story and having a machinelearning and/or artificial intelligence system for detecting performanceof a contract. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a set of gaming engines forgenerating and/or presenting a marketplace-related data story and havinga machine learning and/or artificial intelligence system configured toprovide governance and compliance support. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having a setof gaming engines for generating and/or presenting a marketplace-relateddata story and having a machine learning and/or artificial intelligencesystem for categorizing transactions. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for generating and/or presenting a marketplace-related datastory and having intelligent agents for undertaking workflows related tosmart contract configuration. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for generating and/or presenting a marketplace-related datastory and having intelligent agents for negotiating contract terms. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for generating and/orpresenting a marketplace-related data story and having intelligentagents for getting other agents. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for generating and/or presenting a marketplace-related datastory and having intelligent agents for recognizing a target. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for generating and/orpresenting a marketplace-related data story and having simulationsystems. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for generating and/orpresenting a marketplace-related data story and having a fraud detectionsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a set of gaming engines for generating and/orpresenting a marketplace-related data story and having a configurationand management system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a set of gaming engines forgenerating and/or presenting a marketplace-related data story and havinga digital twin and a digital twin user interface for user interactionwith the digital twin. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a set of gaming engines forgenerating and/or presenting a marketplace-related data story and havinga marketing and/or advertising system. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for generating and/or presenting a marketplace-related datastory and having at least one of an augmented reality interface, avirtual reality interface, or a mixed reality interface for the digitaltwin system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a set of gaming engines forgenerating and/or presenting a marketplace-related data story and havingan in-twin marketplace system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a set of gamingengines for generating and/or presenting a marketplace-related datastory and having a system for integrating with at least one hardware orsoftware system to provide at least one of end-user or automatedservices.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having acryptocurrency system for enabling the trading of cryptocurrencies andhaving a smart contract system for enabling the creation of smartcontracts. In embodiments, provided herein is a platform for integratinga set of gaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having a smart digital wallet forstoring funds. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a cryptocurrency system forenabling the trading of cryptocurrencies and having a verificationsystem for verifying that a party has performed one or more obligationsof a smart contract. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a cryptocurrency system forenabling the trading of cryptocurrencies and having a distributed ledgerfor recording electronic transactions. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having acryptocurrency system for enabling the trading of cryptocurrencies andhaving an AI-driven system for automatically configuring a smartcontract. In embodiments, provided herein is a platform for integratinga set of gaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having a machine learning and/orartificial intelligence system for detecting performance of a contract.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having a machine learning and/orartificial intelligence system configured to provide governance andcompliance support. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a cryptocurrency system forenabling the trading of cryptocurrencies and having a machine learningand/or artificial intelligence system for categorizing transactions. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having intelligent agents forundertaking workflows related to smart contract configuration. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having intelligent agents fornegotiating contract terms. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having acryptocurrency system for enabling the trading of cryptocurrencies andhaving intelligent agents for getting other agents. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a cryptocurrency system for enabling the trading ofcryptocurrencies and having intelligent agents for recognizing a target.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having simulation systems. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having a fraud detection system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having a configuration and managementsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having a digital twin and a digital twinuser interface for user interaction with the digital twin. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having a marketing and/or advertisingsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a cryptocurrency system for enabling thetrading of cryptocurrencies and having at least one of an augmentedreality interface, a virtual reality interface, or a mixed realityinterface for the digital twin system. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having acryptocurrency system for enabling the trading of cryptocurrencies andhaving an in-twin marketplace system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having acryptocurrency system for enabling the trading of cryptocurrencies andhaving a system for integrating with at least one hardware or softwaresystem to provide at least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart contract system for enabling thecreation of smart contracts. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract system for enabling the creation of smart contracts and havinga smart digital wallet for storing funds. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart contract system for enabling the creation of smart contracts andhaving a verification system for verifying that a party has performedone or more obligations of a smart contract. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart contract system for enabling the creation of smart contracts andhaving a distributed ledger for recording electronic transactions. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart contract system for enabling thecreation of smart contracts and having an AI-driven system forautomatically configuring a smart contract. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart contract system for enabling the creation of smart contracts andhaving a machine learning and/or artificial intelligence system fordetecting performance of a contract. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract system for enabling the creation of smart contracts and havinga machine learning and/or artificial intelligence system configured toprovide governance and compliance support. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart contract system for enabling the creation of smart contracts andhaving a machine learning and/or artificial intelligence system forcategorizing transactions. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a smart contract systemfor enabling the creation of smart contracts and having intelligentagents for undertaking workflows related to smart contractconfiguration. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart contract system forenabling the creation of smart contracts and having intelligent agentsfor negotiating contract terms. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract system for enabling the creation of smart contracts and havingintelligent agents for getting other agents. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart contract system for enabling the creation of smart contracts andhaving intelligent agents for recognizing a target. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a smart contract system for enabling the creation of smartcontracts and having simulation systems. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract system for enabling the creation of smart contracts and havinga fraud detection system. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a smart contract systemfor enabling the creation of smart contracts and having a configurationand management system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart contract system forenabling the creation of smart contracts and having a digital twin and adigital twin user interface for user interaction with the digital twin.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart contract system for enabling thecreation of smart contracts and having a marketing and/or advertisingsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a smart contract system for enabling thecreation of smart contracts and having at least one of an augmentedreality interface, a virtual reality interface, or a mixed realityinterface for the digital twin system. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract system for enabling the creation of smart contracts and havingan in-twin marketplace system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smartcontract system for enabling the creation of smart contracts and havinga system for integrating with at least one hardware or software systemto provide at least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart digital wallet for storing funds. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart digital wallet for storing funds andhaving a verification system for verifying that a party has performedone or more obligations of a smart contract. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart digital wallet for storing funds and having a distributed ledgerfor recording electronic transactions. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smart digitalwallet for storing funds and having an AI-driven system forautomatically configuring a smart contract. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having asmart digital wallet for storing funds and having a machine learningand/or artificial intelligence system for detecting performance of acontract. In embodiments, provided herein is a platform for integratinga set of gaming engines and a set of smart contract services in a commonexecution framework having a smart digital wallet for storing funds andhaving a machine learning and/or artificial intelligence systemconfigured to provide governance and compliance support. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a smart digital wallet for storing funds and having a machinelearning and/or artificial intelligence system for categorizingtransactions. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart digital wallet forstoring funds and having intelligent agents for undertaking workflowsrelated to smart contract configuration. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smart digitalwallet for storing funds and having intelligent agents for negotiatingcontract terms. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a smart digital wallet forstoring funds and having intelligent agents for getting other agents. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart digital wallet for storing funds andhaving intelligent agents for recognizing a target. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a smart digital wallet for storing funds and having simulationsystems. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a smart digital wallet for storing funds andhaving a fraud detection system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smart digitalwallet for storing funds and having a configuration and managementsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a smart digital wallet for storing funds andhaving a digital twin and a digital twin user interface for userinteraction with the digital twin. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smart digitalwallet for storing funds and having a marketing and/or advertisingsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a smart digital wallet for storing funds andhaving at least one of an augmented reality interface, a virtual realityinterface, or a mixed reality interface for the digital twin system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a smart digital wallet for storing funds andhaving an in-twin marketplace system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a smart digitalwallet for storing funds and having a system for integrating with atleast one hardware or software system to provide at least one ofend-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving a distributed ledger for recording electronic transactions. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving an AI-driven system for automatically configuring a smartcontract. In embodiments, provided herein is a platform for integratinga set of gaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving a machine learning and/or artificial intelligence system fordetecting performance of a contract. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a verificationsystem for verifying that a party has performed one or more obligationsof a smart contract and having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving a machine learning and/or artificial intelligence system forcategorizing transactions. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a verification systemfor verifying that a party has performed one or more obligations of asmart contract and having intelligent agents for undertaking workflowsrelated to smart contract configuration. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a verificationsystem for verifying that a party has performed one or more obligationsof a smart contract and having intelligent agents for negotiatingcontract terms. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a verification system forverifying that a party has performed one or more obligations of a smartcontract and having intelligent agents for getting other agents. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving intelligent agents for recognizing a target. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a verification system for verifying that a party has performedone or more obligations of a smart contract and having simulationsystems. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving a fraud detection system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a verificationsystem for verifying that a party has performed one or more obligationsof a smart contract and having a configuration and management system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving a digital twin and a digital twin user interface for userinteraction with the digital twin. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a verificationsystem for verifying that a party has performed one or more obligationsof a smart contract and having a marketing and/or advertising system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving at least one of an augmented reality interface, a virtual realityinterface, or a mixed reality interface for the digital twin system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a verification system for verifying that aparty has performed one or more obligations of a smart contract andhaving an in-twin marketplace system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a verificationsystem for verifying that a party has performed one or more obligationsof a smart contract and having a system for integrating with at leastone hardware or software system to provide at least one of end-user orautomated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a distributed ledger for recording electronictransactions. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a distributed ledger forrecording electronic transactions and having an AI-driven system forautomatically configuring a smart contract. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having adistributed ledger for recording electronic transactions and having amachine learning and/or artificial intelligence system for detectingperformance of a contract. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a distributed ledger forrecording electronic transactions and having a machine learning and/orartificial intelligence system configured to provide governance andcompliance support. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a distributed ledger forrecording electronic transactions and having a machine learning and/orartificial intelligence system for categorizing transactions. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a distributed ledger for recording electronictransactions and having intelligent agents for undertaking workflowsrelated to smart contract configuration. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a distributedledger for recording electronic transactions and having intelligentagents for negotiating contract terms. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a distributedledger for recording electronic transactions and having intelligentagents for getting other agents. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a distributedledger for recording electronic transactions and having intelligentagents for recognizing a target. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a distributedledger for recording electronic transactions and having simulationsystems. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a distributed ledger for recording electronictransactions and having a fraud detection system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a distributed ledger for recording electronic transactions andhaving a configuration and management system. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having adistributed ledger for recording electronic transactions and having adigital twin and a digital twin user interface for user interaction withthe digital twin. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a distributed ledger forrecording electronic transactions and having a marketing and/oradvertising system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a distributed ledger forrecording electronic transactions and having at least one of anaugmented reality interface, a virtual reality interface, or a mixedreality interface for the digital twin system. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having adistributed ledger for recording electronic transactions and having anin-twin marketplace system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a distributedledger for recording electronic transactions and having a system forintegrating with at least one hardware or software system to provide atleast one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an AI-driven system for automaticallyconfiguring a smart contract. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an AI-drivensystem for automatically configuring a smart contract and having amachine learning and/or artificial intelligence system for detectingperformance of a contract. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having an AI-driven system forautomatically configuring a smart contract and having a machine learningand/or artificial intelligence system configured to provide governanceand compliance support. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having an AI-driven system forautomatically configuring a smart contract and having a machine learningand/or artificial intelligence system for categorizing transactions. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an AI-driven system for automaticallyconfiguring a smart contract and having intelligent agents forundertaking workflows related to smart contract configuration. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an AI-driven system for automaticallyconfiguring a smart contract and having intelligent agents fornegotiating contract terms. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an AI-drivensystem for automatically configuring a smart contract and havingintelligent agents for getting other agents. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having anAI-driven system for automatically configuring a smart contract andhaving intelligent agents for recognizing a target. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving an AI-driven system for automatically configuring a smartcontract and having simulation systems. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an AI-drivensystem for automatically configuring a smart contract and having a frauddetection system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having an AI-driven system forautomatically configuring a smart contract and having a configurationand management system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having an AI-driven system forautomatically configuring a smart contract and having a digital twin anda digital twin user interface for user interaction with the digitaltwin. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having an AI-driven system for automaticallyconfiguring a smart contract and having a marketing and/or advertisingsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having an AI-driven system for automaticallyconfiguring a smart contract and having at least one of an augmentedreality interface, a virtual reality interface, or a mixed realityinterface for the digital twin system. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an AI-drivensystem for automatically configuring a smart contract and having anin-twin marketplace system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having an AI-drivensystem for automatically configuring a smart contract and having asystem for integrating with at least one hardware or software system toprovide at least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for detecting performance of a contract. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for detecting performance of a contract and having amachine learning and/or artificial intelligence system configured toprovide governance and compliance support. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having amachine learning and/or artificial intelligence system for detectingperformance of a contract and having a machine learning and/orartificial intelligence system for categorizing transactions. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for detecting performance of a contract and havingintelligent agents for undertaking workflows related to smart contractconfiguration. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a machine learning and/orartificial intelligence system for detecting performance of a contractand having intelligent agents for negotiating contract terms. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for detecting performance of a contract and havingintelligent agents for getting other agents. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having amachine learning and/or artificial intelligence system for detectingperformance of a contract and having intelligent agents for recognizinga target. In embodiments, provided herein is a platform for integratinga set of gaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for detecting performance of a contract and havingsimulation systems. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a machine learning and/orartificial intelligence system for detecting performance of a contractand having a fraud detection system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a machinelearning and/or artificial intelligence system for detecting performanceof a contract and having a configuration and management system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for detecting performance of a contract and having adigital twin and a digital twin user interface for user interaction withthe digital twin. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a machine learning and/orartificial intelligence system for detecting performance of a contractand having a marketing and/or advertising system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a machine learning and/or artificial intelligence system fordetecting performance of a contract and having at least one of anaugmented reality interface, a virtual reality interface, or a mixedreality interface for the digital twin system. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having amachine learning and/or artificial intelligence system for detectingperformance of a contract and having an in-twin marketplace system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for detecting performance of a contract and having asystem for integrating with at least one hardware or software system toprovide at least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport and having a machine learning and/or artificial intelligencesystem for categorizing transactions. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a machinelearning and/or artificial intelligence system configured to providegovernance and compliance support and having intelligent agents forundertaking workflows related to smart contract configuration. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport and having intelligent agents for negotiating contract terms. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport and having intelligent agents for getting other agents. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport and having intelligent agents for recognizing a target. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport and having simulation systems. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a machinelearning and/or artificial intelligence system configured to providegovernance and compliance support and having a fraud detection system.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport and having a configuration and management system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport and having a digital twin and a digital twin user interface foruser interaction with the digital twin. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a machinelearning and/or artificial intelligence system configured to providegovernance and compliance support and having a marketing and/oradvertising system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a machine learning and/orartificial intelligence system configured to provide governance andcompliance support and having at least one of an augmented realityinterface, a virtual reality interface, or a mixed reality interface forthe digital twin system. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having a machine learningand/or artificial intelligence system configured to provide governanceand compliance support and having an in-twin marketplace system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system configured to provide governance and compliancesupport and having a system for integrating with at least one hardwareor software system to provide at least one of end-user or automatedservices.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for categorizing transactions. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a machine learning and/or artificial intelligence system forcategorizing transactions and having intelligent agents for undertakingworkflows related to smart contract configuration. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a machine learning and/or artificial intelligence system forcategorizing transactions and having intelligent agents for negotiatingcontract terms. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a machine learning and/orartificial intelligence system for categorizing transactions and havingintelligent agents for getting other agents. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having amachine learning and/or artificial intelligence system for categorizingtransactions and having intelligent agents for recognizing a target. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for categorizing transactions and having simulationsystems. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for categorizing transactions and having a frauddetection system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a machine learning and/orartificial intelligence system for categorizing transactions and havinga configuration and management system. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a machinelearning and/or artificial intelligence system for categorizingtransactions and having a digital twin and a digital twin user interfacefor user interaction with the digital twin. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having amachine learning and/or artificial intelligence system for categorizingtransactions and having a marketing and/or advertising system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for categorizing transactions and having at leastone of an augmented reality interface, a virtual reality interface, or amixed reality interface for the digital twin system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a machine learning and/or artificial intelligence system forcategorizing transactions and having an in-twin marketplace system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a machine learning and/or artificialintelligence system for categorizing transactions and having a systemfor integrating with at least one hardware or software system to provideat least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for undertaking workflowsrelated to smart contract configuration. In embodiments, provided hereinis a platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having intelligentagents for undertaking workflows related to smart contract configurationand having intelligent agents for negotiating contract terms. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for undertaking workflowsrelated to smart contract configuration and having intelligent agentsfor getting other agents. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having intelligent agents forundertaking workflows related to smart contract configuration and havingintelligent agents for recognizing a target. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework havingintelligent agents for undertaking workflows related to smart contractconfiguration and having simulation systems. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework havingintelligent agents for undertaking workflows related to smart contractconfiguration and having a fraud detection system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving intelligent agents for undertaking workflows related to smartcontract configuration and having a configuration and management system.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for undertaking workflowsrelated to smart contract configuration and having a digital twin and adigital twin user interface for user interaction with the digital twin.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for undertaking workflowsrelated to smart contract configuration and having a marketing and/oradvertising system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having intelligent agents forundertaking workflows related to smart contract configuration and havingat least one of an augmented reality interface, a virtual realityinterface, or a mixed reality interface for the digital twin system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for undertaking workflowsrelated to smart contract configuration and having an in-twinmarketplace system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having intelligent agents forundertaking workflows related to smart contract configuration and havinga system for integrating with at least one hardware or software systemto provide at least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for negotiating contractterms. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for negotiating contractterms and having intelligent agents for getting other agents. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for negotiating contractterms and having intelligent agents for recognizing a target. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for negotiating contractterms and having simulation systems. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having intelligentagents for negotiating contract terms and having a fraud detectionsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for negotiating contractterms and having a configuration and management system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving intelligent agents for negotiating contract terms and having adigital twin and a digital twin user interface for user interaction withthe digital twin. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having intelligent agents fornegotiating contract terms and having a marketing and/or advertisingsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for negotiating contractterms and having at least one of an augmented reality interface, avirtual reality interface, or a mixed reality interface for the digitaltwin system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having intelligent agents fornegotiating contract terms and having an in-twin marketplace system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for negotiating contractterms and having a system for integrating with at least one hardware orsoftware system to provide at least one of end-user or automatedservices.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for getting other agents.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for getting other agentsand having intelligent agents for recognizing a target. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving intelligent agents for getting other agents and having simulationsystems. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for getting other agentsand having a fraud detection system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having intelligentagents for getting other agents and having a configuration andmanagement system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having intelligent agents for gettingother agents and having a digital twin and a digital twin user interfacefor user interaction with the digital twin. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework havingintelligent agents for getting other agents and having a marketingand/or advertising system. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having intelligent agents forgetting other agents and having at least one of an augmented realityinterface, a virtual reality interface, or a mixed reality interface forthe digital twin system. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having intelligent agents forgetting other agents and having an in-twin marketplace system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for getting other agentsand having a system for integrating with at least one hardware orsoftware system to provide at least one of end-user or automatedservices.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for recognizing a target.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for recognizing a targetand having simulation systems. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having intelligentagents for recognizing a target and having a fraud detection system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for recognizing a targetand having a configuration and management system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving intelligent agents for recognizing a target and having a digitaltwin and a digital twin user interface for user interaction with thedigital twin. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having intelligent agents forrecognizing a target and having a marketing and/or advertising system.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for recognizing a targetand having at least one of an augmented reality interface, a virtualreality interface, or a mixed reality interface for the digital twinsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having intelligent agents for recognizing a targetand having an in-twin marketplace system. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework havingintelligent agents for recognizing a target and having a system forintegrating with at least one hardware or software system to provide atleast one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having simulation systems. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework havingsimulation systems and having a fraud detection system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving simulation systems and having a configuration and managementsystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having simulation systems and having a digital twinand a digital twin user interface for user interaction with the digitaltwin. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having simulation systems and having a marketingand/or advertising system. In embodiments, provided herein is a platformfor integrating a set of gaming engines and a set of smart contractservices in a common execution framework having simulation systems andhaving at least one of an augmented reality interface, a virtual realityinterface, or a mixed reality interface for the digital twin system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having simulation systems and having an in-twinmarketplace system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having simulation systems and having asystem for integrating with at least one hardware or software system toprovide at least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a fraud detection system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a fraud detection system and having a configuration andmanagement system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a fraud detection system andhaving a digital twin and a digital twin user interface for userinteraction with the digital twin. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a frauddetection system and having a marketing and/or advertising system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a fraud detection system and having at leastone of an augmented reality interface, a virtual reality interface, or amixed reality interface for the digital twin system. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a fraud detection system and having an in-twin marketplacesystem. In embodiments, provided herein is a platform for integrating aset of gaming engines and a set of smart contract services in a commonexecution framework having a fraud detection system and having a systemfor integrating with at least one hardware or software system to provideat least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a configuration and management system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a configuration and management system andhaving a digital twin and a digital twin user interface for userinteraction with the digital twin. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a configurationand management system and having a marketing and/or advertising system.In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a configuration and management system andhaving at least one of an augmented reality interface, a virtual realityinterface, or a mixed reality interface for the digital twin system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a configuration and management system andhaving an in-twin marketplace system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a configurationand management system and having a system for integrating with at leastone hardware or software system to provide at least one of end-user orautomated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a digital twin and a digital twin userinterface for user interaction with the digital twin. In embodiments,provided herein is a platform for integrating a set of gaming enginesand a set of smart contract services in a common execution frameworkhaving a digital twin and a digital twin user interface for userinteraction with the digital twin and having a marketing and/oradvertising system. In embodiments, provided herein is a platform forintegrating a set of gaming engines and a set of smart contract servicesin a common execution framework having a digital twin and a digital twinuser interface for user interaction with the digital twin and having atleast one of an augmented reality interface, a virtual realityinterface, or a mixed reality interface for the digital twin system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a digital twin and a digital twin userinterface for user interaction with the digital twin and having anin-twin marketplace system. In embodiments, provided herein is aplatform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a digital twinand a digital twin user interface for user interaction with the digitaltwin and having a system for integrating with at least one hardware orsoftware system to provide at least one of end-user or automatedservices.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a marketing and/or advertising system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a marketing and/or advertising system andhaving at least one of an augmented reality interface, a virtual realityinterface, or a mixed reality interface for the digital twin system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a marketing and/or advertising system andhaving an in-twin marketplace system. In embodiments, provided herein isa platform for integrating a set of gaming engines and a set of smartcontract services in a common execution framework having a marketingand/or advertising system and having a system for integrating with atleast one hardware or software system to provide at least one ofend-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a digital twin system having at least one ofan augmented reality interface, a virtual reality interface, or a mixedreality interface for the digital twin system. In embodiments, providedherein is a platform for integrating a set of gaming engines and a setof smart contract services in a common execution framework having adigital twin system having at least one of an augmented realityinterface, a virtual reality interface, or a mixed reality interface forthe digital twin system and having an in-twin marketplace system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a digital twin system having at least one ofan augmented reality interface, a virtual reality interface, or a mixedreality interface for the digital twin system and having a system forintegrating with at least one hardware or software system to provide atleast one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an in-twin marketplace system. Inembodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having an in-twin marketplace system and having asystem for integrating with at least one hardware or software system toprovide at least one of end-user or automated services.

In embodiments, provided herein is a platform for integrating a set ofgaming engines and a set of smart contract services in a commonexecution framework having a system for integrating with at least onehardware or software system to provide at least one of end-user orautomated services.

Additive Manufacturing Embodiments

“Additive manufacturing” refers to a collection of versatile fabricationtechniques for rapid prototyping and/or manufacturing of parts thatallow 3D digital models (CAD designs) to be converted to threedimensional objects by depositing multiple thin layers of material, suchas according to a series of two-dimensional, cross-sectional depositionmaps.

Accordingly, the term “additive manufacturing platform” used hereinencompasses a platform that prints, builds, or otherwise produces 3Dparts and/or products at least in part using an additive manufacturingtechnique. The additive manufacturing platform may encompasstechnologies like 3D printing, vapor deposition, polymer (or othermaterial) coating, epitaxial and/or crystalline growth approaches, andothers, alone or in combination with other technologies, such assubtractive or assembly technologies, and enables manufacturing of athree-dimensional product from a design via a process of formingsuccessive layers of the product, with optional interim or subsequentsteps to arrive at a finished component or system. The design may be inthe form of a data source like an electronic 3D model created with acomputer aided design package or via 3D scanner. The 3D printing orother additive process then involves forming a first material-layer andthen adding successive material layers wherein each new material-layeris added on a pre-formed material-layer, until the entire designedthree-dimensional product is completed. The additive manufacturingplatform may be a stand-alone unit, a sub-unit of a larger system orproduction line, and/or may include other non-additive manufacturingfeatures, such as subtractive-manufacturing features, pick-and-placefeatures, coating features, finishing features (such as etching,lithography, painting, polishing and the like), two-dimensional printingfeatures, and the like. Further, the platform may includethree-dimensional additive manufacturing machines configured for rapidprototyping, three-dimensional printing, two-dimensional printing,freeform fabrication, solid freeform fabrication, and stereolithography;subtractive manufacturing machines including computer numericalcontrolled fabrication machines; injection molding machines; and thelike.

FIG. 222 is a diagrammatic view illustrating an example environment ofan autonomous additive manufacturing platform 22210 according to someembodiments of the present disclosure. The platform operates within amanufacturing node 22200, which in turn is a part of a larger network oftransaction enablement platform entities. The manufacturing node 22200includes an additive manufacturing unit 22202, such as a 3D printer forprinting with metal materials, biocompatible materials, bioactivematerials, biological materials, or other more conventional additivemanufacturing materials, or other additive manufacturing type describedherein, in the documents incorporated herein by reference, or asunderstood in the art. The manufacturing node 22200 may include, amongother elements, a pre-processing system 22204, a post-processing system22206, and a material handling system 22208. The autonomous additivemanufacturing platform 22210 helps in automating and optimizing thedigital production workflow leading to better outcomes at all stages ofoperation. A data processing and intelligence component 22218 of theautonomous additive manufacturing platform 22210 runs artificialintelligence systems, such as involving machine learning or otheralgorithms, neural networks, expert systems, models, and others, toprocess the input data and calculate an optimal set of processparameters for printing or other additive manufacturing. Process controlcomponent 22220 of the autonomous additive manufacturing platform 22210then adjusts one or more process parameters in real time and theadditive manufacturing unit 22202 uses these process parameters tocomplete the additive manufacturing process. In embodiments, finishingsystems 22221 at the manufacturing node 22210, such as subtractivesystems, assembly systems, additional processing systems, and the likemay undertake further processing, optionally in iterative sequences withadditive stages, resulting in a finished item (e.g., a part, component,or finished good). In embodiments, the resulting product is thenoptionally packaged at a packaging system 22222 and may be shipped,using a shipping system 22224 and one or more transaction enablementplatform entities 22226, right up to an end customer. In otherembodiments, the additive manufacturing platform 22210 and/or a set ofadditive manufacturing units 22202 may comprise portable or otherwisemobile units, such as handheld units, units equipped with robotic orother autonomous mobility, and/or units positioned in or on vehicles,including general purpose vehicles and special purpose vehicles. In suchcases, actions from design through delivery may occur in parallel withmobility of the units 22202 and in coordination, by the additivemanufacturing platform 22210, with the location and mobility of othertransaction enablement platform entities 22226. In one of many possibleexamples, a set of autonomously mobile 3D printing units may becoordinated to points of service work, such as a set of home or businesslocations, where they may be configured to print tools, parts, or otheritems to support the service work, such as repairs or replacements. Inembodiments, additive manufacturing, including design generation, designreview, preprocessing, and printing steps, may commence while the unit22202 is in transit to the point of service. In another example, amobile autonomous additive manufacturing unit 22202 (either autonomous,semi-autonomous, or with an operator) and packaging unit may completefinal steps of manufacturing in transit, such as by adding customizationelements (e.g., a final coating of a selected color, a customer-specificdesign element, or the like) in transit and optionally completing finalpackaging in transit. In embodiments, one or more components of theadditive manufacturing platform 22210 may be disposed in or integratedwith a smart container or a smart package, as described elsewhere hereinand in the documents incorporated by reference herein. In embodiments, aset of additive manufacturing units 22202 may be integrated into or witha set of robotic systems, such as mobile and/or autonomous roboticsystems. For example, the additive manufacturing unit 22202 may becontained within the housing or body of a robotic system, such as amulti-purpose/general purpose robotic system, such as one that simulateshuman or other animal species capabilities. Alternatively, oradditionally, the additive manufacturing unit 22202 may be configured todeliver additive layering from a nozzle that is disposed on an operatingend of a robotic arm or other assembly. In embodiments, multipleadditive manufacturing units 22202, or multiple nozzles, printheads orother working elements may be integrated with a single mobile,autonomous, and/or or multi-purpose robotic system, such as where oneadditive manufacturing unit 22202 is housed and prints/layers within thebody of the robotic system (such as in a chamber, such as vacuumchamber, pressurized chamber, heated chamber, or the like) and anotheradditive manufacturing unit 22202 prints/layers or otherwise operatesupon an external site, such as a target location of a machine, product,or the like, such as by a nozzle, printhead, or the like that isdisposed on an arm or similar element of the robot. In embodiments,multiple printing/layering elements are served by a common materialsource, such as of thermoplastic material. In embodiments, multiplematerial sources are available for internal and externalprinting/layering elements. In embodiments, an internal printing elementoperates within a chamber using materials that require control over theprinting environment or operates on high-value production elements, suchas parts that are intended for long-term use, such as metal manufacturedparts. In embodiments, the external working unit uses materials or doesjobs that require other materials and/or have other purposes, such asproduction of disposable tools, grips, supports, fasteners and the likein support of a job, such as a repair or replacement job, among manyothers. In embodiments, the external printing/layering unit is combinedwith a robotic arc welding unit, such as to provide, in series orparallel, a set of printing/layering steps and a series of arc weldingsteps to undertake a job on an external site, workpiece, or the like. Inembodiments, an assembly may be provided to encapsulate and/or shield anexternal working unit, such as a temporary chamber, balloon, tent, orother volume that isolates the area where the nozzle, printhead, or thelike will print, layer, or the like, optionally also encapsulating orshielding a workpiece or target location for printing/layering withinthe same shielded/isolated space as the additive manufacturing element.In embodiments, the encapsulated/shielded area may be sealed to allowpressurization, depressurization, vacuum creation, introduction ofmaterials for deposition, and the like. In embodiments, theencapsulation/shielding may use an additively manufactured element orcombination thereof with another element. In embodiments, an AI system22312 may automate one or more of the design, configuration, scheduling,coordination and/or execution of a set of robotic jobs, and a set ofadditive manufacturing jobs, such that the capabilities of an integratedmobile robotic and additive manufacturing unit are coordinated acrossthe various jobs in time (e.g., where an interior 3D printer or otheradditive manufacturing unit 22202 prints a tool, workpiece, part or thelike for a later job while the robotic unit performs a current job)and/or wherein jobs are coordinated across a fleet or workforce ofrobotic units, additive manufacturing units, and integrated combinationsthereof (such as where units are matched to jobs according to locations,robotic capabilities, additive manufacturing capabilities, and otherfactors).

In embodiments, material handling systems 22208 provide storage,movement, control, and handling of materials through the process ofmanufacturing and distribution. For example, the material handlingsystems 22208 may feed, orient, load/unload, or otherwise manipulatemetal materials, biocompatible materials, bioactive materials,biological materials, or other more conventional additive manufacturingmaterials in the manufacturing space. In embodiments, the materialhandling systems 22208 may be semi or fully automated and may includeone or more robotic units for material handling.

In embodiments, the material handling systems 22208 may include orintegrate with, optionally in the same housing, unit or system, amaterial capture and processing system 22227 for capturing material(such as recapturing unused material from jobs and/or capturingavailable material from a work site, such as from used, broken, ordefective items) and rendering the material suitable to use as a sourcematerial, such as by: (a) automatically analyzing an item to determineits compatibility for use as source material (e.g., by identifying it asa given type of metal, alloy, polymer or plastic, such as by machinevision, chemical testing, image-based testing, weighing the item, or thelike); (b) cleaning, filtering, disassembling, or otherwisepre-processing the item or material, such as to remove non-conformingmaterial; (c) rendering a solid item or material into a thermoplasticstate, such as by controlled heating, such as according to amaterial-specific heating profile; (d) filtering or otherwise treatingthe material, such as to remove defects; (e) storing the item in anappropriate vessel or form factor for later use, with appropriatereporting of capacity and availability, such as to a broader system formanaging jobs, including cooling and/or otherwise processing thematerial into a wire, powder, mesh, rod, filament, or the like until theneed for a job arises; (f) delivering the item for additivemanufacturing operation; and/or (g) reporting on measures of recaptureand savings, including material cost savings, savings on recyclingcosts, and/or time savings. For example, in embodiments, a broken partmay be melted down onsite and reprinted. For example, in embodiments, amaterial that would otherwise be disposed of or recycled may be rendereduseful on site, without the need for reverse logistics. In embodiments,a common heating source is used, with alternate points of heating atdifferent temperatures, to render recaptured material into athermoplastic state and for preparing material for additivemanufacturing operations.

The manufacturing node 22200 may also connect to other nodes like amanufacturing node 22228 through connectivity facilities so as toconstitute a distributed manufacturing network 22230. Also, thedifferent systems within the manufacturing node 22200 including theadditive manufacturing unit 22202, the pre-processing system 22204, thepost-processing system 22206, the material handling system 22208, theautonomous additive manufacturing platform 22210, the user interface22212, the data sources 22214, and the design and simulation system22216 as well as the different parts and products being printed may bereferred to as distributed manufacturing network entities.

In embodiments, connectivity facilities include various connectivityfacilities described throughout this disclosure and the documentsincorporated by reference herein, including network connections(including various configurations, types, and protocols for fixed andwireless connections), Internet of Things devices, edge devices,routers, switches, access points, repeaters, mesh networking systems,interfaces, ports, application programming interfaces (APIs), brokers,services, connectors, wired or wireless communication links,human-accessible interfaces, software interfaces, micro-services, SaaSinterfaces, PaaS interfaces, IaaS interfaces, cloud capabilities, or thelike by which data or information may be exchanged between systems orsub-systems of the autonomous additive manufacturing platform 22210, aswell as with other systems, such as distributed manufacturing networkentities or external systems, such as cloud-based or on-premisesenterprise systems (e.g., accounting systems, resource managementsystems, CRM systems, and many others). In embodiments, connectivityfacilities use, include, or are integrated with artificial intelligenceor autonomous capabilities as described herein and/or in the documentsincorporated herein by reference, such as enabling self-organization orself-configuration of connectivity, data storage, computation, dataprocessing, packet routing, data filtering, quality-of-service, errorcorrection, packet security, session management, and the like. Inembodiments, the additive manufacturing unit 22202 may incorporate awireless mesh network node, such as an RF repeater, optionally usingsoftware-defined bandpass filtering, such that a set of such additivemanufacturing units 22202 may operate as a coordinated mesh on a definednetwork infrastructure (including physical and/or virtual networkresources). In embodiments, the additive manufacturing unit 22202 mayinclude network coding system for controlling the utilization of a datapath between the additive manufacturing unit 22202 and other additivemanufacturing units 22202 and/or to control the utilization of the datapath between the additive manufacturing unit 22202 and various edge,cloud, on-premises, telecommunications network, and other informationtechnology systems.

The additive manufacturing unit 22202 may be any suitable type ofprinter that executes any suitable type of 3D printing process or anyother type of unit that executes another additive manufacturing process.Various different types of additive manufacturing units 22202 and 3Dprinting processes are discussed below for purposes of example. Thedisclosure, however, is not limited to the 3D printing processesdescribed below.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute Fused Deposition Modeling (FDM)™ process (also known as, forexample, Fused Filament Fabrication™). The process of FDM may involve asoftware process which may process an input file, such as an STL(stereolithography) file. An object may be produced by extruding smallbeads of, for example, thermoplastic material to form layers as thematerial hardens immediately after extrusion from a nozzle. Extrusion isthe 3D printing technique where the material, such as a polymer, metal(including alloys), or the like, is pushed in fluid form through a tubeand into a moving nozzle which extrudes the material to a targetlocation where the material subsequently hardens in place. By accuratelymoving the extruder either continuously or starting and stopping atextremely fast speeds, the design is built layer by layer. The sourcematerial is typically supplied and stored in solid form, such as in afilament or wire that is wound in a coil and then unwound to supplymaterial to a heating element to render the material into athermoplastic state and an extrusion nozzle which can control the flowof the material between an “off” state and a maximal flow state. Aworm-drive, or any other suitable drive system, may be provided to pushthe filament into the nozzle at a controlled rate. The nozzle is heatedto melt the material. The thermoplastic materials are heated past theirstate transition temperature (from solid to fluid) and are thendeposited by an extrusion head. The nozzle can be moved in bothhorizontal and vertical directions, such as by a numerically controlledmechanism. In embodiments, the nozzle may follow a tool-path that iscontrolled by a computer-aided manufacturing (CAM) software package, andthe object is fabricated layer-by-layer, such as from the bottom up.

In embodiments, the additive manufacturing unit 22202 may includemultiple source materials and multiple extrusion nozzles (and supportingcomponents for the same, such as for movement and positioning), such asto allow (a) rapid switching between source materials, such asfacilitated by a valve set, such as a high-pressure valve set, and/or(b) simultaneous extrusion by multiple nozzles, such as to enablesimultaneous layering at different points of work on an item. Inembodiments, the additive manufacturing unit 22202 enables voxelatedsoft matter printing and/or metal printing via multi-material,multi-nozzle printing, with high-speed switching between materials,e.g., at speeds of 50 times per second or faster.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute an electron beam freeform fabrication (EBFFF) process. TheEBFFF process may utilize electron beam welding technology to createmetallic parts. In embodiments, with the EBFFF method, metallic preformscan be manufactured from computer-generated 3D drawings or models. Thedeposition path and process parameters may be generated frompost-processing of a virtual 3D model and executed by a real-timecomputer control. The deposition takes place in a vacuum environment. Awire may be directed toward the molten pool and melted by a focusedelectronic beam. Different parts of the object to be fabricated arebuilt up layer by layer by moving the electronic beam and wire sourceacross a surface of underlying material referred to as a substrate. Thedeposit solidifies immediately after the electron beam has passed.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute a direct metal laser sintering process (DMLS). The DMLSprocess may involve a laser as a power source to sinter powderedmaterial such as a metal at points in space defined by a 3D model, thusbinding the material together to create a solid structure. The DMLSprocess may involve the use of a 3D CAD model whereby a file, such as an.stl file, is created and sent to the software of the additivemanufacturing unit 22202. The DMLS-based 3D printer may use ahigh-powered fiber optic laser. The metal powder is fused into a solidpart by melting it locally using the focused laser beam. Object partsare built up additively layer by layer.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute a selective laser melting (SLM) process. The SLM process uses3D CAD data as a digital information source and energy in the form of ahigh-power laser beam to create 3D metal parts by fusing fine metallicpowders together. The process involves slicing of the 3D CAD file datainto layers to create a 2D image of each layer. Thin layers of atomizedfine metal powder are evenly distributed using a coating mechanism ontoa substrate plate that is fastened to an indexing table that moves inthe vertical (Z) axis. This takes place inside a chamber containing atightly controlled atmosphere of inert gas such as argon. Once eachlayer has been distributed, each 2D slice of the geometry is fused byselectively applying the laser energy to the powder surface by directingthe focused laser beam using two high frequency scanning mirrors in theX- and Y-axes. The laser energy permits full melting of the particles toform solid metal. The process is repeated layer after layer until thepart is complete. In embodiments, the SLM process may be a multi-scannerand/or multi-laser SLM process, such as enabling simultaneous actionacross multiple scans and/or multiple target points of laser meltingwork.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute a selective heat sintering process. The process may involve athermal printhead to apply heat to layers of powdered source material torender it to a thermoplastic state. When a layer is finished, the powderbed of source material moves down, and an automated roller adds a newlayer of material, which is sintered to form the next cross-section ofthe object. Power bed printing may refer to a technique where one ormore powders, typically a metal powder, are connected via variousmethods such as lasers or heat in order to rapidly produce the endproduct. Typically, it is done by either having an area filled withpowder and only connecting the design areas of the powder while layer bylayer removing the rest or by adding powder layer-by-layer whilesimultaneously connecting it. Similar to light polymerization, powderbed printing is significantly faster than other types of 3D printing. Inembodiments, the additive manufacturing unit 22202 may employ multiplepowder bed/roller subsystems, thereby enabling simultaneous work ondifferent target points of work and/or multi-material powder bedapplications that allow switching between materials.

In embodiments, the additive manufacturing unit 22202, of various typesdescribed herein, may combine materials to produce an output comprisinga composite of materials, such as to combine favorable properties (e.g.,mechanical properties) of two materials to provide benefits that surpassthose of a single material. In embodiments, composite materials producedin or by the additive manufacturing units 22202 may comprisefunctionally graded materials (FGMs), such as where two materials arejoined with a graded interface that avoids a distinct boundary betweenthe materials. This may distribute thermal and/or mechanical stressesthat result from different material properties over a largervolume/space, thereby mitigating issues like cracking and breaking thatoccur with non-graded composite materials.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute a selective laser sintering process. The process of selectivelaser sintering (SLS) involves a laser used to melt a flame-retardantplastic powder, which then solidifies to form the printed layer. Inembodiments, the additive manufacturing unit 22202 may be configured toexecute a plaster-based 3D printing process. In embodiments, theadditive manufacturing unit 22202 may be configured to execute alaminated object manufacturing process. In this process, layers ofadhesive-coated paper, plastic, or metal laminates may be successivelyglued together and cut to shape with a knife or laser cutter. After theobject is fabricated by the additive manufacturing unit 22202,additional modifications may be done by machining or drilling afterprinting. In embodiments, the selective laser sintering (SLS) involvesmultiple lasers, thereby allowing for switching and/or simultaneous workon different target locations and/or different material types.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute stereo-lithography (SLA) processes. The process may employ aresin, such as from a vat of liquid ultraviolet curable photopolymermaterial, and an ultraviolet laser to build layers one at a time. Foreach layer, the laser beam traces a cross-section of the part pattern onthe surface of the liquid resin. Exposure to the ultraviolet laser lightcures and solidifies the pattern traced on the resin and joins it to thelayer below. In embodiments, the SLA process may involve multiple UVlasers, allowing for switching and/or simultaneous work on differenttarget locations and/or different material types.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute digital light processing (DLP) methods. Digital lightprocessing uses a projector to project an image of a cross-section of anobject into a vat of photopolymer (light reactive plastic). The lightselectively hardens only the area specified in that image. A printedlayer is then repositioned to leave room for unhardened photopolymer tofill the newly created space between the print and the projector.Repeating this process builds up the object one layer at a time. Inembodiments, multiple DLP sources deliver light to different locations,allowing for switching and/or simultaneous work on different targetlocations within the light reactive plastic material.

In embodiments, the additive manufacturing unit 22202 may be configuredto execute light polymerization methods. In this process, drops of aliquid plastic are exposed to a laser beam of ultraviolet light. Duringthis exposure, light converts the liquid into a solid. Lightpolymerization may employ a technique where a rising or falling layer oflight-sensitive polymer is subjected to the type of light which causesit to harden in changing areas over time as it rises or falls and/or atechnique where a moving (e.g., laser) light source is targeted todifferent locations where liquid polymer/plastic material is positioned.This causes these areas of the polymer to harden, and once the desiredshape is created, the remaining liquid polymer that did not harden isremoved, leaving the finished product. Light polymerization is usefulbecause of how fast the final product completes, with some types workingup to a hundred times faster, or more, than other 3D printing methodsfor some designs.

In embodiments, the additive manufacturing unit 22202 may involve theuse of an inkjet type printhead to deliver a liquid or colloidal bindermaterial to layers of a powdered build material. The printing techniquemay involve applying a layer of a powdered build material to a surface,such as using a roller. After the build material is applied to thesurface, the printhead delivers the liquid binder to predetermined areasof the layer of material. The binder infiltrates the material and reactswith the powder, causing the layer to solidify in the printed areas by,for example, activating an adhesive in the powder. After the firstcross-sectional portion is formed, the steps are repeated, andsuccessive cross-sectional portions are fabricated until the finalproduct is formed.

In embodiments, the methods performed by the additive manufacturing unit22202 may involve deposition of successive layers of a build material ona rotary build table and deposition of a liquid in a predeterminedpattern on each successive layer of the build material to form a 3Dobject.

In embodiments, the additive manufacturing unit 22202 may incorporatemultiple types of additive manufacturing capabilities among thosedescribed herein or understood by those with skill in the art, therebyforming a hybrid additive manufacturing unit. In embodiments, hybridadditive manufacturing units may further integrate other manufacturingcapabilities, such as subtractive techniques, assembly systems, handlingsystems, finishing systems, and the like. In embodiments, a hybridadditive manufacturing unit may integrate injection delivery of acolloidal binder material with a liquid polymerization technique.

In embodiments, the platform 22210 may provide 3D printed products thatconform to a body part/anatomy of the user including wearables likeeyewear, footwear, earwear, and headgear. Conformance may, inembodiments, be based on a scan of a body part or anatomical feature,such as a laser or other structured light scan, a MRI, EEG, computedtomography, ultrasound or other imaging scan, or the like. A 3D topologyfor the anatomical feature may be used as an input source for generationby a CAD system or other design system (which may be linked to orintegrated into an additive manufacturing platform) of a design foradditive manufacturing. The design may be configured to produce ananatomy-compatible item that conforms well to anatomy (such as ahearable unit that fits the inner ear, headgear that fits the head, abrace that fits a joint, or the like) and/or an item that is intended toreplace a part of the anatomy, such as a prosthetic.

In embodiments, the platform 22210 has the capability to self-start andself-power.

In embodiments, the platform 22210 has a built-in recycling capabilitywherein scrap parts may be automatically returned to the productionprocess and support materials and excess powders may be returned to theproduction process.

FIG. 223 is a schematic illustrating an example implementation of theautonomous additive manufacturing platform for automating and optimizingthe digital production workflow for additive manufacturing (e.g., metalmanufacturing) according to some embodiments of the present disclosure.

The autonomous additive manufacturing platform 22210 includes a datacollection and management system 22302, a data storage system 22304, anda data processing system 22306.

The data collection and management system 22302 collects and organizesdata collected from various data sources including real time datacollected from a set of sensors. Some examples of sensors providing dataas input to the data collection and management system 22302 include apower and energy sensor, mass sensor, location sensor, temperaturesensor, humidity sensor, pressure sensor, viscosity sensor, flow sensor,chemical/gas sensor, strain gauge to measure, image capture/camera,video capture, thermal imaging, hyperspectral imaging, sound sensor, andair quality sensor.

The data storage system 22304 may store a wide range of data types usingvarious storage media, data architecture, and formats including but notlimited to: entity or asset data (such as part profile, product profile,printer profile), state data (such as indicating a state, conditionstatus, or other indicator with respect to any asset, entity,application, components or elements of the platform 22210), user data(including identity data, role data, task data, workflow data, healthdata, performance data, quality data and many other types), event data(such as with respect to any of a wide range of events, includingoperational data, transactional data, workflow data, maintenance data,and many other types of data that includes or relates to events thatoccur within the platform 22210 or with respect to one or moreapplications, including process events, financial events, transactionevents, output events, input events, state-change events, operatingevents, workflow events, repair events, maintenance events, serviceevents, damage events, replacement events, refueling events, rechargingevents, shipping events, supply chain events, and many others); claimsdata (such as data relating to product liability, general liability,injury, and other liability claims and claims data relating tocontracts, such as supply contract performance claims, product deliveryrequirements, warranty claims, indemnification claims, deliveryrequirements, timing requirements, milestones, key performanceindicators, and others); accounting data (such as data relating tocompletion of contract requirements, satisfaction of bonds, payment ofduties and tariffs, and others); and risk management data (such asrelating to parts or products supplied, amounts, pricing, delivery,sources, routes, customs information, and many others), among many otherdata types associated with the platform 22210.

In embodiments, the data storage system 22304 may store data in adistributed ledger, digital thread, or the like, such as for maintaininga serial or other record of an entity or asset over time, including apart or products or any other asset or entity described herein.

The data processing system 22306 includes an artificial intelligencesystem 22312, such as a machine learning system 22310. The machinelearning system 22310 may define a machine learning model 22313 forperforming analytics, simulation, decision making, and predictiveanalytics related to data processing, data analysis, simulationcreation, and/or simulation analysis of one or more of assets orentities of the distributed manufacturing network 22230 of FIG. 222 . Inembodiments, the platform 22210 may include a set of artificialintelligence systems 22312 (including any of the types described hereinor in the documents incorporated herein by reference) that areconfigured (a) to operate on a set of inputs and/or a set ofoptimization factors to automatically select a suitable type of additivemanufacturing for a design/job; (b) to automatically discover a set ofavailable additive manufacturing units 22202 (optionally includingsingle-type units and/or hybrid type units), (c) to automatically selecta set of units 22202 to perform an additive manufacturing job; (d) toautomatically schedule a set of additive manufacturing units 22202 toperform a set of additive manufacturing jobs; (e) to automaticallyconfigure a selected set of additive manufacturing units 22202 toundertake a set of additive manufacturing jobs using a set of designsprovided by the set of artificial intelligence system; and/or (f) toautomatically configure logistics and delivery of a set of outputs froma set of additive manufacturing units. In embodiments, the set of inputsmay include locations and types of available additive manufacturingunits 22202, current job schedules for additive manufacturing units,cost factors (such as material costs, energy costs, costs of ITresources, costs of labor, pricing for additive manufacturing services,and others), design inputs (such as functional requirements regardingstrength, flexibility, resilience, temperature tolerance, straintolerance, resistance to wear, water resistance, stress tolerance,weight bearing, tensile strength, load bearing, and many others), aswell as compatibility factors (including shape compatibility,biocompatibility, chemical compatibility, environmental compatibility,and others). Optimization factors may include aesthetic factors,compatibility factors (as noted above), economic factors (such asmarginal cost, total cost, profitability, price, brand impact, andothers), timing factors (such as for coordination with workflows andactivities, including various ongoing manufacturing, service,maintenance, marketing, delivery and/or logistics processes),prioritization factors, and many others. In embodiments, the artificialintelligence system of the platform 22210 is trained based on a trainingset of data that includes expert interactions with a set of additivemanufacturing projects that involve various types of additivemanufacturing options. In embodiments, the AI system is trained based onoutcome factors, such as product quality and/or product defect outcomes,economic outcomes, on-time completion outcomes, and the like, such asinvolving deep learning, supervised learning, and/or semi-supervisedlearning. In embodiments, the AI system is distributed between theadditive manufacturing units 22202 and a host system, such as acloud-based system. In embodiments, the AI system is integrated into theadditive manufacturing unit 22202. In embodiments, the AI system isdistributed across a set of additive manufacturing units 22202, such asa mesh or network of additive manufacturing unit 22202 nodes, such thatthe above capabilities are coordinated across the units, such as byself-configuration of the units 22202 in coordination with other units,such as a fleet of additive manufacturing units 22202 owned by anenterprise and/or co-operated and/or shared by a set of users (such asin an “additive manufacturing as a service” system). As one exampleamong many possible examples, the AI system of the platform 22210 maytake a set of design requirements, such as functional requirements,generate a set of designs that satisfy the functional requirements,determine the optimal combination of additive manufacturing types toproduce each set of designs, find and compare available additivemanufacturing units for each combination (such as using economic factorsand other factors), and select, configure, and schedule units toundertake the design. For example, among many possibilities across awide range of product categories, the AI system may take functionalrequirements for a customized wearable device for a latex-allergicindividual user that meets a design requirement of using biocompatible,waterproof materials, while being capable of withstanding impacts andbending, in a color that matches the customers exact preference from alarge palette of colors. The AI system may automatically generate aninstruction set for producing the wearable device using acombination/hybrid of light polymerization (operating on a non-latexpolymer) for components of the wearable that will touch the user and aDMLS process for interior metal/alloy components. The AI system may thenfind available units, such as different units or an integrated/hybridunit, schedule the units to undertake jobs (e.g., to fit a targeteddelivery time), configure the units, send the jobs, and scheduledelivery. Thus, the AI system may automatically manage the design,generation, and delivery, through use of a set of additive manufacturingunits, of a highly customized product based on customer specific designrequirements, including health requirements, physical configurationrequirements, economic factors, and preferences, among many others.

In embodiments, the machine learning model 22313 is an algorithm and/orstatistical model that performs specific tasks without using explicitinstructions, relying instead on patterns and inference. The machinelearning model 22313 may build one or more mathematical models based ontraining data to make predictions and/or decisions without beingexplicitly programmed to perform the specific tasks. The machinelearning model 22313 may receive inputs of sensor data or other data astraining data, including event data and state data related to one ormore of the entities or assets, or other inputs noted above orthroughout this disclosure. The sensor data input to the machinelearning model 22313 may be used to train the machine learning model22313 to perform the analytics, simulation, decision making, and/orpredictive analytics relating to the data processing, data analysis,simulation creation, and/or simulation analysis of the one or more ofthe distributed manufacturing network entities or assets. The machinelearning model 22313 may also use input data from a user or users of theautonomous additive manufacturing platform 22210. In embodiments, themachine learning model 22313 may use the input data and sensor data todetermine an optimal set of process parameters for 3D printing of a partby the additive manufacturing unit 22202. The machine learning model22313 may include an artificial neural network, a decision tree, alogistic regression model, a stochastic gradient descent model, a fuzzyclassifier, a support vector machine, a Bayesian network, a hierarchicalclustering algorithm, a k-means algorithm, a genetic algorithm, anyother suitable form of machine learning model, or a combination thereof.The machine learning model 22313 may be configured to learn throughsupervised learning, unsupervised learning, reinforcement learning,self-learning, feature learning, sparse dictionary learning, anomalydetection, association rules, a combination thereof, or any othersuitable algorithm for learning.

In embodiments, the artificial intelligence system 22312 may define adigital twin system 22316 to create a digital replica or digital twin ofone or more of the distributed manufacturing network entities. Thedigital twin of the one or more of the distributed manufacturing networkentities may use substantially real-time sensor data to provide forsubstantially real-time virtual representation of the distributedmanufacturing network entities and for simulation of one or morepossible future states of the one or more distributed manufacturingnetwork entities. The digital twin exists simultaneously with the one ormore distributed manufacturing network entities being replicated(physical twin) and may be updated continuously based on sensor data,test and inspection results, conducted maintenance, modifications etc.to reflect the current condition or parameter values of the one or moredistributed manufacturing network entities. The digital twin providesone or more simulations of both physical elements and characteristics ofthe one or more distributed manufacturing network entities beingreplicated and the dynamics thereof, in embodiments, throughout thelifecycle of the one or more distributed manufacturing network entitiesbeing replicated. The digital twin may provide a hypothetical simulationof the one or more distributed manufacturing network entities, forexample, during a design phase before the one or more entities aremanufactured or fabricated or during or after construction orfabrication of the one or more entities by allowing for hypotheticalextrapolation of sensor data to simulate a state of the one or moredistributed manufacturing network entities, such as during high stress,after a period of time has passed during which component wear may be anissue, during maximum throughput operation, after one or morehypothetical or planned improvements have been made to the one or moredistributed manufacturing network entities, or any other suitablehypothetical situation. In embodiments, the machine learning model 22313may automatically predict hypothetical situations for simulation withthe digital twin, such as by predicting possible improvements to the oneor more distributed manufacturing network entities, predicting when oneor more components of the one or more distributed manufacturing networkentities may fail, and/or suggesting possible improvements to the one ormore distributed manufacturing network entities, such as changes toparameters, arrangements, components, or any other suitable change tothe distributed manufacturing network entities.

The digital twin allows for simulation of the one or more distributedmanufacturing network entities during both design and operation phasesof the one or more distributed manufacturing network entities, as wellas simulation of hypothetical operation conditions and configurations ofthe one or more distributed manufacturing network entities. The digitaltwin allows for analysis and simulation of the one or more distributedmanufacturing network entities by facilitating observation andmeasurement of nearly any type of metric, including temperature,pressure, wear, light, humidity, deformation, expansion, contraction,deflection, bending, stress, strain, load-bearing, shrinkage, in, on,and around each of the one or more distributed manufacturing networkentities. The insights gained from analysis and simulation using digitaltwins may be passed onto the design or manufacturing processes forimprovement of these processes.

In embodiments, the machine learning model 22313 may process the sensordata, including the event data and the state data, to define simulationdata for use by the digital twin system 22314. The machine learningmodel 22313 may, for example, receive state data and event data relatedto a particular distributed manufacturing network entity and perform aseries of operations on the state data and the event data to format thestate data and the event data into a format suitable for use by thedigital twin system 22314 in creation of a digital replica of thedistributed manufacturing network entity. For example, one or moredistributed manufacturing network entities may include a product beingmanufactured by the additive manufacturing unit 22202. The machinelearning model may collect data from one or more sensors positioned on,near, in, and around the product. The machine learning model may performoperations on the sensor data to process the sensor data into simulationdata and output the simulation data to the digital twin system 22314.The digital twin system 22314 may use the simulation data to create oneor more product twins 22315, the simulation including, for example,metrics including temperature, wear, speed, rotation, and vibration ofthe product and parts thereof. The simulation may be a substantiallyreal-time simulation, allowing for a user of the platform 22210 to viewthe simulation of the product, metrics related thereto, and metricsrelated to parts thereof, in substantially real time. The simulation maybe a predictive or hypothetical situation, allowing for a user of theplatform 22210 to view a predictive or hypothetical simulation of theproduct, metrics related thereto, and metrics related to componentsthereof.

In embodiments, the machine learning model 22313 and the digital twinsystem 22314 may process sensor data and create a digital twin of a setof distributed manufacturing network entities to facilitate design,real-time simulation, predictive simulation, and/or hypotheticalsimulation of a related group of distributed manufacturing networkentities.

In embodiments, a control system 22316 in the data processing system22306 may adjust process parameters of the 3D printing process inreal-time based on the simulations.

In embodiments, a distributed manufacturing network entity, such as theadditive manufacturing unit 22202 or the platform 22210, may, optionallyautomatically, generate a set of digital twins of a set of manufactureditems, such as products, components, parts, or the like. In embodiment,the digital twin of a manufactured item generated by the additivemanufacturing unit 22202 or the platform 22210 may include, link to, beenriched by, and/or integrate with, among other things: (a) aninstruction set according to which an item was additively manufactured,such as including shape information, material layering information,functional information, operational parameter information (such asdescribed elsewhere herein), and the like; (b) a training data set basedupon data which an artificial intelligence system was trained inconnection with the design or manufacturing of the item; (c) a sensordata set, such as containing time series sensor data (such as imagingdata from various imaging systems) indicating exact conditions ofmanufacturing of the item, such as linking a series of images of layersof the item as it was generated with data indicating, in case withrespect to the item, the environment in which it was manufactured, theequipment or tools used, the materials used, and/or the like, the sensordata set may relate to temperatures, pressures, fluid flow rates, heatflux data, volume data, topological data, radiation data (e.g.,intensity of lasers, visible light, infrared light, UV, x-rays, magneticfields, electrical fields and the like), chemical information (e.g.,presence of reactants, catalysts, and the like), biological data (e.g.,presence and states of biomaterials, pathogens, and other factors), andothers; (d) a testing data set, such as indicating outcomes of testingbefore, during, or after manufacturing, such as equipment testing,material testing, stress testing, visual inspection (including bymachine vision), strain testing, torsion testing, load testing, impacttesting, operational testing, and the like; (e) manufacturinginformation relating to similar items, such as outcomes ofmanufacturing, usage, or the like; and others. In embodiments, theadditive manufacturing unit 22202 may automatically create the digitaltwin upon receiving an instruction to manufacture an item andsubsequently enrich and/or modify the digital twin during manufacturingand/or after manufacturing. In embodiments, the additive manufacturingunit 22202 may automatically embed the above-referenced data for thedigital twin of the item in or on the item (such as by writing to a datastructure that is embedded in or disposed on the item, such as chip), ona tag for the item, on a container or package, or the like.

FIG. 224 is a block diagram illustrating the information flow in theautonomous additive manufacturing platform 22210 for optimization ofdifferent operational parameters of the additive manufacturing processaccording to some embodiments of the present disclosure. In embodiments,the parameters may be associated with a 3D printed part, a 3D printedproduct, a 3D printing process, or a 3D printing machine. Some examplesfor parameters include: extrusion temperature, rate of materialdeposition, tool path, voltage settings of heating apparatus, exposurepattern, layer height, printing surface temperature, layerheight/thickness, build speed, build material flow rate, partorientation, air gap, shape and volume information for holes, spaces,voids, lumens, gaps, conduits and the like, support structure settings,ambient conditions including temperature, humidity and pressure, rawmaterial conditions including temperature and viscosity, part conditionsincluding temperature, stress concentrations including compressive,tensile, shear, bending and torsional stresses, and the like. Again, theparameters are typically specific to a given additive manufacturingtechnique, material, geometry and application, or particular hybrid orcombination thereof.

Referring to FIG. 224 , at 22400, input data for the printing of aproduct is received at the autonomous additive manufacturing platform22210. The input data may be received at a user interface of platform22210 and can include details like 3D printing technique, geometry andkey features of the product, and printing material etc. In embodiments,the input data may just include the required properties (like strength,stiffness, yield, elasticity, elongation, electrical conductivity,thermal conductivity etc.) or areas of application (aerospace, dental,automotive, jewelry, etc.) of the product, and the platform 22210 maydetermine details like 3D printing technique or material to be used forprinting. This may occur automatically (such as by artificialintelligence), or with human interaction and/or supervision, such aswhere a set of recommended details are suggested by AI and confirmedand/or modified by a human user.

At 22402, an instruction set for additive manufacturing, such as aprofile, such as a 3D print profile, is determined based on the inputreceived at 22400, as well as simulation received from the machinelearning system 22310 and the digital twin system 22314. The profileincludes parameters for additive manufacturing of the product, such asusing the 3D printer.

At 22404, sensor data (including but not limited to ambient, product ormaterial temperatures; compressive, shear, tensile, bending andtorsional stresses; oxygen, carbon dioxide level, and ozone levels;humidity; vibration; sound signature and visual indicators) from theadditive manufacturing (e.g., 3D printing) process is collected. Thedata collection and management system 22302 helps collect the sensordata through an array of sensors and other data collecting technologieslike IoT devices, machine vision systems, and the like. The collecteddata may be analyzed at the edge devices or sent to one or more datapools within the data storage system 22304, such as for laterconsumption by local or remote intelligence. The use ofcloud-connectable edge devices, such as within computing infrastructurethat is proximal to the additive manufacturing unit(s) 22202 (such as ina local area network of a building, campus, or other premises where theadditive manufacturing unit(s) 22202 are located and/or in a connectedvehicle that transports the additive manufacturing unit(s) 22202) and/orthat is integrated with or into the additive manufacturing unit 22202,such as where the additive manufacturing unit 22202 has onboard edgecomputational and/or connectivity resources, such as 5G (or othercellular), Wifi, Bluetooth, fixed networking resources, or the like),offers opportunities to provide rapid, real time or near real timeprocessing responsiveness while benefiting from the expansive computingand data storage capabilities provided by highly scalable cloudcomputing resources, such as servers and the like.

In embodiments, data may also be stored in a blockchain, such as onewhere storage is distributed across multiple manufacturing nodes as wellas other data storage devices or systems. In embodiments, this may takethe form of a distributed ledger that may capture transactions, events,or the like, such as financial events involving additive manufacturing,smart contract-related events, operational events (such as scheduling orcompletion of jobs), and others. The data may also be multiplexed orotherwise condensed using sensor fusion and relayed over a network andfed into the machine learning system employing one or more machinelearning models.

At 22406, the parameters may be dynamically adjusted as needed based onthe analysis of sensor data. As the 3D printing is complete, the datarelated to the outcome of the 3D printing process is collected at 22408.The outcome data may be collected through a user interface wherein auser provides information regarding the success or failure of the 3Dprint. The data is then provided as feedback to the machine learningsystem 22310, which uses the feedback to train or improve the initialmachine learning model (such as improvements by adjusting weights,rules, parameters, or the like, based on the feedback). In embodiments,the feedback is utilized to analyze trends over multiple 3D printsperformed by one or more users across multiple additive manufacturingunits 22202 and manufacturing nodes 22200.

In embodiments, the autonomous additive manufacturing platform 22210provides optimization and process control across the entire lifecycle ofmanufacturing using machine learning, from product conception and designthrough manufacturing and distribution to service and maintenance.

In embodiments, the autonomous additive manufacturing platform 22210provides for generative design and topology optimization to determine atleast one product design suitable for fabrication.

In embodiments, the autonomous additive manufacturing platform 22210provides for optimization of a build preparation process.

In embodiments, the autonomous additive manufacturing platform 22210optimizes part orientation process for superior production results.

In embodiments, the autonomous additive manufacturing platform 22210automatically determines and recommends support structures to minimizematerial costs, print time, post processing, and risk of damage to the3D printed part (on support removal).

In embodiments, the autonomous additive manufacturing platform 22210provides for optimizing toolpath generation. For example, in a 3Dprinter, a toolpath may comprise the trajectory of the nozzle and/orprint head. In embodiments, toolpath generation enables a manufacturingprocess to fill the boundary and interior areas of each sliced layer.Various types of toolpath strategies and algorithms, such as zigzag,contour, spiral, and partition patterns, are possible withconsiderations on the build time, cost, geometrical quality, warpage,shrinkage, strength, and stiffness of a manufacturing model. Inembodiments, an artificial intelligence system may be trained onoutcomes, such as described above, to provide a recommended toolpathand/or to entirely automate toolpath generation.

In embodiments, the autonomous additive manufacturing platform 22210provides for optimized dynamic 2D, 2.5D, and 3D nesting to maximize thenumber of printed parts while minimizing the raw material waste. Inembodiments, nesting is optimized such that the nesting algorithmevaluates individual part priority to ensure high priority parts arehandled accordingly, such as with scheduling priority, priority inquality, priority in ease-of-use, priority of positioning, or the like.In embodiments, nesting is optimized such that the nesting algorithmminimizes the travel time for the cutting tool. In embodiments, nestingis optimized such that the nesting algorithm integrates with supportstructure optimization.

In embodiments, the autonomous additive manufacturing platform 22210provides for optimization of post processing processes.

In embodiments, the autonomous additive manufacturing platform 22210provides for an automated powder removal system utilizing a digital twinwherein the digital twin calculates the optimal movement of the powderremoval system while de-powdering.

In embodiments, the autonomous additive manufacturing platform 22210provides for an automated, hands-free support structure removal.

In embodiments, the autonomous additive manufacturing platform 22210provides for automated surface finishing.

In embodiments, the autonomous additive manufacturing platform 22210provides for automated part metrology for use with integrated qualityand process control systems.

In embodiments, manufacturing methods described herein may use materialadditives during processing that impart various characteristics infinished parts. Examples in plastic injection molding include glassfiber for added strength, and electrically conductive and shieldingfibers for tailored electrical properties. For some applications,orientation of added fibers or other materials may affect theperformance of finished parts. For example, in a glass fiberreinforcement application, long fiber orientation may dictate minimumand maximum deformation orientations under stress. Fiber orientationduring manufacturing may be only partially controlled through molddesign, injection nozzle location and pressure, and other processcontrols.

3D printed parts may also be manufactured using material additives;however, most 3D printing methods can only produce materials withlimited ability to optimize additive characteristics such as fiberorientation to help optimize finished part performance. For example, 3Dprinters may use nozzles that extrude various plastic materials, butinherent flow characteristics of a fixed nozzle, and limitations of the3D printing process in general, limit options for finished part materialengineering. Such use of 3D printing nozzles offer the ability tocontrol orientation of additive materials as they are laid down for partproduction. This development provides the opportunity to finely tailormaterial performance, for example, localized orientations for structuralenhancement or homogeneous random orientation for electrical shieldingperformance. In examples, this capability may be provided by a 3Dprinting nozzle that uses actuated flexible elements to change the shapeof the nozzle during material application, resulting in predictablefiber orientations. This may be used in conjunction with other printingprocess parameters such as nozzle orientation, flow rate and pressure,and the like to further refine material characteristics. Use caseexamples include, but are not limited to: one or more engineeringcharacteristics that may vary across a single part to provide targetedperformance, for example, varying stiffness; optimized use of materialsbased on enhanced process control, for example, using less material toproduce a part with the same functional performance; and providingcontrol of multiple additives to impart combined capabilities, forexample, orientation of structural long fibers for structuralperformance, combined with randomized conductive additives for aspecified electrical performance.

Neural Networks for Artificial Intelligence Systems

Some embodiments of the present disclosure, including ones involvingartificial intelligence, machine learning, automation (including roboticprocess automation, remote control, autonomous operation, automatedconfiguration, and the like), expert systems, self-organization,adaptive intelligent systems for prediction, classification,optimization, and the like, may benefit from the use of a neuralnetwork, such as a neural network trained for pattern recognition, forclassification of one or more parameters, characteristics, or phenomena,for support of autonomous control, and other purposes.

Neural networks (or artificial neural networks) are a family ofstatistical learning models inspired by biological neural networks andare used to estimate or approximate functions that may depend on a largenumber of inputs and are generally unknown. Neural networks represent asystem of interconnected “neurons” which send messages to each other.The connections have numeric weights that can be tuned based onexperience, making neural nets adaptive to inputs and capable oflearning.

References to artificial intelligence, neural networks, or neural netthroughout this disclosure should be understood to encompass a widerange of different types of machine learning systems, neural networks,such as feed forward neural networks, convolutional neural networks(CNN), recurrent neural networks (RNN), long short-term memory (LSTM)neural networks, gated recurrent unit (GRU) neural networks,self-organizing map (SOM) neural networks (e.g., Kohonen self-organizingneural networks), autoencoder (AE) neural networks, encoder-decoderneural networks, modular neural networks, or variations, hybrids orcombinations of the foregoing, or combinations with reinforcementlearning (RL) systems or other expert systems, such as rule-basedsystems, and model-based systems (including ones based on physicalmodels, statistical models, flow-based models, biological models,biomimetic models, and the like).

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes topredict one or more outputs. Functions may involve weights, features,feature vectors, and the like. Neurons may include perceptrons, neuronsthat mimic biological functions (such as the human senses of touch,vision, taste, hearing, and smell), and the like. Neural networks canemploy multiple layers of operations including one or more hidden layerssituated between an input layer and an output layer. The output of eachlayer can be used as input to another layer, e.g., the next hidden layeror the output layer. The output of a particular neuron can be a weightedsum of the inputs to the neuron, adjusted with a bias and multiplied byan activation function, e.g., a rectified linear unit (ReLU) or asigmoid function.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training a neural network can involve providinginputs to the untrained neural network to generate predicted outputs,comparing the predicted outputs to expected outputs, and updating thealgorithm's weights and biases to account for the difference between thepredicted outputs and the expected outputs. Specifically, a costfunction can be used to calculate a difference between the predictedoutputs and the expected outputs. By computing the derivative of thecost function with respect to the weights and biases of the network, theweights and biases can be iteratively adjusted over multiple cycles tominimize the cost function. Training may be complete when the predictedoutputs satisfy a convergence condition, e.g., a small magnitude ofcalculated cost as determined by the cost function.

Training may include presenting the neural network with one or moretraining data sets that represent values (including the many typesdescribed throughout this disclosure), as well as one or more indicatorsof an outcome, such as an outcome of a process, an outcome of acalculation, an outcome of an event, an outcome of an activity, or thelike. Training may include training in optimization, such as training aneural network to optimize one or more systems based on one or moreoptimization approaches, such as Bayesian approaches, parametric Bayesclassifier approaches, k-nearest-neighbor classifier approaches,iterative approaches, interpolation approaches, Pareto optimizationapproaches, algorithmic approaches, and the like. Feedback may beprovided in a process of variation and selection, such as with a geneticalgorithm that evolves one or more solutions based on feedback through aseries of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more environments andtransmitted to the cloud platform over one or more networks, includingusing network coding to provide efficient transmission. In the cloudplatform, optionally using massively parallel computational capability,a plurality of different neural networks of various types (includingmodular forms, structure-adaptive forms, hybrids, and the like) may beused to undertake prediction, classification, and control functions andprovide other outputs as described in connection with expert systemsdisclosed throughout this disclosure. The different neural networks maybe structured to compete with each other (optionally including use ofevolutionary algorithms, genetic algorithms, or the like), such that anappropriate type of neural network, with appropriate input sets,weights, node types and functions, and the like, may be selected, suchas by an expert system, for a specific task involved in a given context,workflow, environment process system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like a source of data about an individual, through a seriesof neurons or nodes, to an output. Data may move from the input nodes tothe output nodes, optionally passing through one or more hidden nodes,without loops. In embodiments, feed forward neural networks may beconstructed with various types of units, such as binary McCulloch-Pittsneurons, the simplest of which is a perceptron.

Example Embodiment for Part Classification and Defect ClassificationUsing Neural Networks

In embodiments, artificial intelligence and machine learning systems inthe data processing system of the autonomous additive manufacturingplatform may enable automatic classification and clustering of 3Dprinted parts and products. In embodiments, artificial intelligence andmachine learning systems in the data processing system of the autonomousadditive manufacturing platform may enable automatic classification andclustering of malicious defects in the additive manufacturing process.

The automated part and defect classification methods and systems of thepresent disclosure may be implemented using image sensors and/or machinevision systems. The machine vision systems may monitor the additivemanufacturing process in real time, such as by capturing and analyzingimages of the part or other item being printed. Automated imageprocessing of the captured images may then be used to monitor any of avariety of part properties, e.g., dimensions (overall dimensions, ordimensions of specific features), feature angles, feature areas, surfacefinish (e.g., degree of light reflectivity, number of pits, and/orscratches per unit area), and the like. The machine vision systems alsotrack the process to detect any defects or errors in the printed part inreal time while successive layers of materials are being deposited bythe 3D printer.

Defects may be identified, e.g., by removing noise from the inspectiondata and subtracting a reference data set (e.g., a reference image of adefect-free part in the case that machine vision tools are beingutilized for inspection), and classified using an unsupervised machinelearning algorithm such as cluster analysis or an artificial neuralnetwork, to classify individual objects as either meeting or failing tomeet a specified set of decision criteria (e.g., a decision boundary) inthe feature space in which defects are being monitored. For example, apartially printed part may be compared with a render of the partial partand, in case the partial part differs beyond a selected threshold fromthe render, the part may be classified as defective.

In embodiments, in-process, the defect classification data may be usedby the machine learning algorithm to determine a set or sequence ofprocess control parameter adjustments that will implement a correctiveaction, e.g., to adjust a layer dimension or thickness, so as to correcta defect when first detected. In some embodiments, in-process automateddefect classification may be used by the machine learning algorithm tosend a warning or error signal to an operator, or optionally, toautomatically abort the deposition process.

In embodiments, the machine vision system uses a variable focus liquidlens-based camera for image capture and defect detection. Inembodiments, the machine vision system uses infrared or visiblewavelength cameras.

FIG. 225A is a diagrammatic view illustrating an example implementationof a data processing system using a neural network to provide real-time,adaptive control of an additive manufacturing process including partdefect classification and feedback according to some embodiments of thepresent disclosure.

Neural networks generally comprise an interconnected group of nodesorganized into multiple layers of nodes. For example, the neural networkarchitecture may comprise at least an input layer, one or more hiddenlayers, and an output layer with each layer comprising a plurality ofnodes or neurons that respond to different combinations of inputs fromthe previous layers. The input for the input layer is received directlyfrom sensors and image or part defect data, whereas the hidden layersuse output of nodes in previous layers as their input. The connectionsbetween the neurons have numeric weights that determine how muchrelative effect an input has on the output value of the node inquestion. The neural network may comprise any total number of layers andany number of hidden layers, where the hidden layers function astrainable feature extractors that allow mapping of a set of input datato a preferred output value or set of output values. The output layerprovides a determination of a predicted future build state, and theneural network model is trained to predict future build state based oncurrent build state and a set of actions.

Referring to FIG. 4 , the input layer comprises one or more real-timestreams of process/product property data that provide an indication ofthe current state of the manufacturing process and/or the product beingfabricated. Examples of suitable input data streams include, but are notlimited to, process simulation data, process monitoring orcharacterization data, in-process inspection data, post-build inspectiondata, or any combination thereof, as well as a list of process controlparameters that may be adjusted to implement next step actions toachieve a target (or future) fabrication state.

This data is fed to a neural network 22500, which in many cases has beenpreviously trained using one or more training data sets comprisingprocess simulation data, process monitoring or characterization data,in-process inspection data, post-build inspection data, or anycombination thereof, from previous fabrication runs of the same ordifferent types of parts.

The input layer may include a plurality of input nodes 22502, 22504,22506, 22508 and 22510 that may provide input data (e.g., sensor data,image data, defect data, audio data, etc.) to the neural network 22200.The input data may be from different sources and may include librarydata x1, simulation data x2, user input data x3, training data x4, andoutcome data x5.

The input nodes 22502, 22504, 22506 22508 and 22510 may pass on theinformation to the next layer, and no computation may be performed bythe input nodes. Hidden layer(s) may include a plurality of nodes, suchas nodes 22512, 22514, and 22516, that may process the information fromthe input layer based on the weights of the connections between theinput layer and the hidden layer and transfer information to the outputlayer. The output layer may include an output node 22518, whichprocesses information based on the weights of the connections betweenthe hidden layer and the output layer and is responsible for computingand transferring information from the network to the outside world, suchas optimizing a process parameter, classifying certain parts or defects,or predicting a condition or an action.

In embodiments, the neural network 22500 may include two or more hiddenlayers and may be referred to as a deep neural network. In embodiments,the layers may be constructed so that the first layer detects a set ofprimitive patterns in the input (e.g. image) data, the second layerdetects patterns of patterns, and the third layer detects patterns ofthose patterns.

In embodiments, a node in the neural network 22500 may have connectionsto all nodes in the immediately preceding layer and the immediate nextlayer. Thus, the layers may be referred to as fully connected layers.

In embodiments, a node in the neural network 22500 may have connectionsto only some of the nodes in the immediately preceding layer and theimmediate next layer. Thus, the layers may be referred to as sparselyconnected layers.

Each neuron in the neural network consists of a weighted combination(e.g., linear combination) of its inputs, and the computation on eachneural network layer may be described as a multiplication of an inputmatrix and a weight matrix. A bias matrix may then be added to theresulting product matrix to account for the threshold of each neuron inthe next level. Further, an activation function may be applied to eachresultant value, and the resulting values may be placed in the matrixfor the next layer. Thus, the output from a node i in the neural networkmay be represented as:

y _(i) =f(Σx _(i) w _(i) +b _(i))

where f is the activation function, Σx_(i)w_(i) is the weighted sum ofinput matrix, and b_(i) is the bias matrix.

The activation function determines the activity level or excitationlevel generated in the node as a result of an input signal of aparticular size. The purpose of the activation function is to introducenon-linearity into the output of a neural network node because mostreal-world functions are non-linear, and it is desirable that theneurons can learn these non-linear representations. Several activationfunctions may be used in an artificial neural network. One exampleactivation function is the sigmoid function σ(x), which is a continuousS-shaped monotonically increasing function that asymptoticallyapproaches fixed values as the input approaches plus or minus infinity.The sigmoid function σ(x) takes a real-valued input and transforms itinto a value between 0 and 1:

σ(x)=1/(1+exp(−x)).

Another example activation function is the tan h function, which takes areal-valued input and transforms it into a value within the range of[−1, 1]:

tan h(x)=2σ(2x)−1

A third example activation function is the rectified linear unit (ReLU)function. The ReLU function takes a real-valued input and thresholds itabove zero (i.e., replacing negative values with zero):

f(x)=max(0,x).

In the example shown in FIG. 4 , nodes 22502, 22504, 22506, 22508, and22510 in the input layer may take external inputs x1, x2, x3, x4, andx5, which may be numerical values depending upon the input dataset. Itwill be understood that even though only five inputs are shown in FIG. 4, in various implementations, a node may include tens, hundreds,thousands, or more inputs. As discussed above, no computation isperformed on input layer, and thus the outputs from nodes 22502, 22504,22506, 22508, and 22510 of input layer are x1, x2, x3, x4, and x5respectively, which are fed into the hidden layer. The output of node22512 in the hidden layer may depend on the outputs from input layer(x1, x2, x3, x4, and x5) and weights associated with connections (w1,w2, w3, w4, and w5). Thus, the output from node 22512 may be computedas:

Y ₄₁₂ =f(x1w1+x2w2+x3w3+x4w4+x5w5+b ₄₁₂)

The outputs from the nodes 22514 and 22516 in the hidden layer may alsobe computed in a similar manner and then be fed to the node 22518 in theoutput layer. Node 22518 in the output layer may perform similarcomputations (using weights v1, v2, and v3 associated with theconnections) as the nodes 22512, 22514, and 22516 in the hidden layers.

Y ₄₁₈ =f(y ₄₁₂ v1+y ₄₁₄ v2+y ₄₁₆ v3+b ₄₁₈)

Where Y₄₁₈ is the output of the neural network 22500.

Training

As mentioned, the connections between nodes in the neural network haveassociated weights. Weights determine how much relative effect an inputvalue has on the output value of the node in question. Before thenetwork is trained, random values are selected for each of the weights.The weights are adjusted during the training process, and thisadjustment of weights to determine the best set of weights that maximizethe accuracy of the neural network is referred to as training. For everyinput in a training dataset, the output of the artificial neural networkmay be observed and compared with the expected output, and the errorbetween the expected output and the observed output may be propagatedback to the previous layer. The weights may be adjusted accordinglybased on the error. This process is repeated until the output error isbelow a predetermined threshold.

In embodiments, backpropagation (e.g., backward propagation of errors)is utilized with an optimization method, such as gradient descent, toadjust weights and update the neural network characteristics.Backpropagation may be a supervised training scheme that learns fromlabeled training data and errors at the nodes by changing parameters ofthe neural network to reduce the errors. For example, a result offorward propagation (e.g., output activation value(s)) determined usingtraining input data is compared against a corresponding known referenceoutput data to calculate a loss function gradient. The gradient may bethen utilized in an optimization method to determine new updated weightsin an attempt to minimize a loss function. For example, to measureerror, the mean square error is determined using the equation:

E=(target−output)²

To determine the gradient for a weight “w,” a partial derivative of theerror with respect to the weight may be determined, where:

gradient=∂E/∂w

The calculation of the partial derivative of the errors with respect tothe weights may flow backwards through the node levels of the neuralnetwork. Then, a portion (e.g., ratio, percentage, etc.) of the gradientis subtracted from the weight to determine the updated weight. Theportion may be specified as a learning rate “a.” Thus, an exampleequation of determining the updated weight is given by the formula:

w new=w old−α∂E/∂w

The learning rate must be selected such that it is not too small (e.g.,a rate that is too small may lead to a slow convergence to the desiredweights) and not too large (e.g., a rate that is too large may cause theweights to not converge to the desired weights). After the weightadjustment, the network should perform better than before for the sameinput because the weights have now been adjusted to minimize the errors.

In some embodiments, a neural network model may be used directly todetermine adjustments to process control parameters using training orlearning of a neural network model. Initially, the model is allowed tochoose randomly from a range of values for each input process controlparameter or action. If the sequence of process control parameteradjustments or actions leads to a flaw or defect, it is scored asleading to an undesirable (or negative) outcome. Repetition of theprocess using different sets of randomly chosen values for each processcontrol parameter or action leads to reinforcement of those sequencesthat leads to desirable (or positive) outcomes. Ultimately, the neuralnetwork model “learns” what adjustments to make to a set or sequence ofdeposition process control parameters or actions in order to achieve thetarget outcome, i.e., a defect-free printed part.

In embodiments, methods and systems described herein may use aconvolutional neural network (referred to in some cases as a CNN, aConvNet, a shift invariant neural network, or a space invariant neuralnetwork), wherein the units are connected in a pattern similar to thevisual cortex of the human brain.

FIG. 225B is a diagrammatic view illustrating an example implementationof a data processing system using a convolutional neural network (CNN)to provide automatic classification and clustering of parts and defectsin an additive manufacturing process according to some embodiments ofthe present disclosure.

A convolutional neural network (CNN) is a specialized neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for classification, objectrecognition, and machine vision applications, but they also may be usedfor other types of pattern recognition such as speech and languageprocessing.

A convolutional neural network learns highly non-linear mappings byinterconnecting layers of artificial neurons arranged in many differentlayers with activation functions that make the layers dependent. Itincludes one or more convolutional layers, interspersed with one or moresub-sampling layers and non-linear layers, which are typically followedby one or more fully connected layers.

Referring to FIG. 4 , a CNN includes an input layer with an input image22520 to be classified by the CNN, a hidden layer which in turn includesone or more convolutional layers, interspersed with one or moreactivation or non-linear layers (e.g., ReLU) and pooling or sub-samplinglayers, and an output layer typically including one or more fullyconnected layers. The input image 22520 may be represented by a matrixof pixels and may have multiple channels. For example, a colored imagemay have a red, a green, and a blue channel each representing red,green, and blue (RGB) components of the input image. Each channel may berepresented by a 2-D matrix of pixels having pixel values in the rangeof, for example, 0 to 22355. A gray-scale image, on the other hand, mayhave only one channel. The following section describes processing of asingle image channel. It will be understood that multiple channels maybe processed in a similar manner.

As shown, the input image 22520 may be processed by the hidden layer,which includes sets of convolutional and activation layers 22522 and22526, each followed by pooling layers 22524 and 22528.

The convolutional layers of the convolutional neural network serve asfeature extractors capable of learning and decomposing the input imageinto hierarchical features. The convolution layers may performconvolution operations on the input image where a filter (also referredas a kernel or feature detector) may slide over the input image at acertain step size (referred to as the stride). For every position (orstep), element-wise multiplications between the filter matrix and theoverlapped matrix in the input image may be calculated and summed to geta final value that represents a single element of an output matrixconstituting a feature map. The feature map refers to image data thatrepresents various features of the input image data and may have smallerdimensions as compared to the input image. The activation or non-linearlayers use different non-linear trigger functions to signal distinctidentification of likely features on each hidden layer. Non-linearlayers use a variety of specific functions to implement the non-lineartriggering, including the rectified linear units (ReLUs), hyperbolictangent, absolute of hyperbolic tangent, and sigmoid functions. In oneimplementation, a ReLU activation implements the function y=max(x, 0)and keeps the input and output sizes of a layer the same. The advantageof using ReLU is that the convolutional neural network is trained manytimes faster. ReLU is a non-continuous, non-saturating activationfunction that is linear with respect to the input if the input valuesare larger than zero and zero otherwise.

As shown in FIG. 225B, the first convolution and activation layer 22522may perform convolutions on the input image 22520 using multiple filtersfollowed by a non-linearity operation (e.g., ReLU) to generate multipleoutput matrices (or feature maps) 22530. The number of filters used maybe referred to as the depth of the convolution layer. Thus, the firstconvolution and activation layer 22522 in the example of FIG. 4 has adepth of three and generates three feature maps using three filters.Feature maps 22530 may then be passed to the first pooling layer 22524that may sub-sample or down-sample the feature maps using a poolingfunction to generate an output matrix 22532. The pooling functionreplaces the feature map with a summary statistic to reduce the spatialdimensions of the extracted feature map thereby reducing the number ofparameters and computations in the network. Thus, the pooling layerreduces the dimensionality of the feature maps while retaining the mostimportant information. The pooling function can also be used tointroduce translation invariance into the neural network, such thatsmall translations to the input do not change the pooled outputs.Different pooling functions may be used in the pooling layer, includingmax pooling, average pooling, and 12-norm pooling.

The output matrix 22532 may then be processed by a second convolutionand an activation layer 22526 to perform convolutions and non-linearactivation operations (e.g., ReLU) as described above to generatefeature maps 22534. In the example shown in FIG. 4 , a secondconvolution and the activation layer 22526 may have a depth of five. Thefeature maps 22534 may then be passed to a pooling layer 22528, wherethe feature maps 22534 may be subsampled or down-sampled to generate anoutput matrix 22536.

The output matrix 22536 generated by the pooling layer 22528 is thenprocessed by one or more fully connected layer 22538 that forms a partof the output layer. The fully connected layer 22538 has a fullconnection with all the feature maps of the output matrix 22536 of thepooling layer 22528. In embodiments, the fully connected layer 22538 maytake the output matrix 22536 generated by the pooling layer 22528 as theinput in vector form and perform high-level determination to output afeature vector containing information of the structures in the inputimage 22520. In embodiments, the fully connected layer 22538 mayclassify the object in input image 22520 into one of several categories,such as using a softmax function. In embodiments, the softmax functionmay be used as the activation function in the output layer and may takea vector of real-valued scores and map it to a vector of values betweenzero and one that sum to one. In embodiments, other classifiers, such asa support vector machine (SVM) classifier, may be used.

In embodiments, one or more normalization layers may be added to the CNNto normalize the output of the convolution filters. The normalizationlayer may provide whitening or lateral inhibition, avoid vanishing orexploding gradients, stabilize training, and enable learning with higherrates and faster convergence. In embodiments, the normalization layersare added after the convolution layer, but before the activation layer.

A CNN may thus be seen as multiple sets of convolution, activation,pooling, normalization, and fully connected layers stacked together tolearn, enhance, and extract implicit features and patterns in the inputimage 22302. A layer, as used herein, can refer to one or morecomponents that operate with similar function by mathematical or otherfunctional means to process received inputs to generate/derive outputsfor a next layer with one or more other components for furtherprocessing within the CNN.

The initial layers of the CNN, e.g., convolution layers, may extract lowlevel features such as edges and/or gradients from the input image22520. Subsequent layers may extract or detect progressively morecomplex features and patterns such as presence of curvatures andtextures in image data and so on. The output of each layer may serve asan input of a succeeding layer in the CNN to learn hierarchical featurerepresentations from data in the input image 22520. This allowsconvolutional neural networks to efficiently learn increasingly complexand abstract visual concepts.

Although only two convolution layers are shown in the example, thepresent disclosure is not limited to the example architecture, and theCNN architecture may comprise any number of layers in total, and anynumber of layers for convolution, activation and pooling. Inembodiments, a convolutional neural network may be deployed with a largenumber of neurons (e.g., 100,000, 500,000 or more), with multiple (e.g.,10, 50, 100 or more) layers, and with thousands of parameters.

For example, there have been many variations and improvements over thebasic CNN model described above. Some examples include Alexnet,GoogLeNet, VGGNet (that stacks many layers containing narrowconvolutional layers followed by max pooling layers), Residual networkor ResNet (that uses residual blocks and skip connections to learnresidual mapping), DenseNet (that connects each layer of CNN to everyother layer in a feed-forward fashion), Squeeze and excitation networks(that incorporate global context into features), and AmobeaNet (thatuses evolutionary algorithms to search and find optimal architecture forimage recognition).

Training of Convolutional Neural Network

The training process of a convolutional neural network may be similar tothe training process discussed in FIG. 225A with respect to the neuralnetwork 22500.

First, all parameters and weights (including the weights in the filtersand weights for the fully connected layer) may be assigned, such asrandomly assigned. Then, during training, a set of training image orimages (in the case of CNNs used for object recognition) in which theobjects have been detected and classified is provided as the input tothe CNN, which performs the forward propagation steps. In other words,the CNN applies convolution, non-linear activation, and pooling layersto each training image to determine the classification vectors (i.e., todetect and classify each training image). These classification vectorsare compared with the predetermined classification vectors. The error(e.g., the squared sum of differences, log loss, softmax log loss)between the classification vectors of the CNN and the predeterminedclassification vectors is determined. This error is then employed toupdate the weights and parameters of the CNN in a backpropagationprocess which may use gradient descent and may include one or moreiterations. The training process is repeated for each training image inthe training set.

The training process and inference process described above may beperformed on hardware, software, or a combination of hardware andsoftware. However, training a convolutional neural network or using thetrained CNN for inference generally requires a significant amount ofcomputation power to perform, for example, the matrix multiplications orconvolutions. Thus, specialized hardware circuits, such as graphicprocessing units (GPUs), tensor processing units (TPUs), neural networkprocessing units (NPUs), FPGAs, ASICs, or other highly parallelprocessing circuits may be used for training and/or inference. Trainingand inference may be performed on a cloud, on a data center, or on adevice.

In embodiments, one or more models building on the basic framework ofconvolutional neural networks may be employed. For example, an objectdetection model may be used that extends the functionality of CNN basedimage classification models by not only classifying parts or defects,but also determining their locations in an image in terms of boundingboxes. Similarly, Region-based CNN (R-CNN) models may be used to extractregions of interest (ROI), where each ROI is a rectangle that mayrepresent the boundary of a part in image.

In embodiments, Capsule Networks may be employed to use fewer labeledtraining examples to achieve similar classification performance of CNNs.

In embodiments, transformer-based, encoder-decoder architectures usingattention mechanisms may be used in conjunction with or in place ofconvolutional neural networks.

FIG. 226 is a schematic view illustrating a system for learning on datafrom the platform 22210 to train the artificial learning system to usedigital twins for classification, predictions, and decision-makingaccording to some embodiments of the present disclosure.

Referring to FIG. 226 , the digital twins 22314 in the autonomousadditive platform may include product twins 22315, part twins 22604,printer twin 22606, user twin 22608, manufacturing node twin 22610,packager twin 22612, and the like that allow for modeling, simulation,prediction, decision-making, and classification. The digital twins 22314may be populated with relevant data, for example, the product twins22602 may be populated with data related to corresponding productincluding dimension data, material data, feature data, thermal data,price data, and the like.

In embodiments, a digital twin may be generated from other digitaltwins. For example, the product twin 22315 may be generated using one ormore part twins 22604. In another example, the part twins 22604 may begenerated using the product twins 22315. In embodiments, a digital twinmay be embedded in another digital twin. For example, the part digitaltwin 22604 may be embedded in the product digital twin 22315 which maybe embedded in the manufacturing node digital twin 22610.

In embodiments, a simulation management system 22614 may set up,provision, configure, and otherwise manage interactions and simulationsbetween and among digital twins 22314.

In embodiments, the artificial intelligence system 22312 is configuredto execute simulations in a simulation management system 22614 using thepart twins 22602 and/or other digital twins available to the digitaltwin system 22314. For example, the artificial intelligent system 22312may adjust one or more features of the printer twin 22606 as a set ofpart twins 22604 are printed by the 3D printer. In embodiments, theartificial intelligent system 22312 may, for each set of features,execute a simulation based on the set of features and may collect thesimulation outcome data resulting from the simulation. For example, inexecuting a simulation on the set of part twins 22604 being manufacturedin the printer twin 22606, the artificial intelligent system 22312 canvary the properties of the printer twin 22606 and can executesimulations that generate outcomes. During the simulation, theartificial intelligent system 22312 may vary the ambient temperature,pressure, humidity, lighting, and/or any other properties of the printertwin 22606. In this example, an outcome can be a condition of the parttwin 22604 after being subjected to a high temperature. The outcomesfrom simulations can be used to train the machine learning models 22313.In embodiments, the machine learning system 22310 may receive trainingdata, outcome data, simulation data, and/or any other data from otherdata sources 22214. In embodiments, the machine learning system 22310may train/reinforce the machine learning models 22313 using the receiveddata to improve the models.

In embodiments, the machine-learning system 22310 trains one or moremodels that are utilized by the artificial intelligence system 22312 tomake classifications, predictions, recommendations, and/or to generateor facilitate decisions or instructions relating to the product and thepart, such as decisions or instructions governing design, configuration,material selection, shape selection, manufacturing type, job scheduling,and many others.

In example embodiments, the artificial intelligence system 22312 trainsa part failure prediction model. A failure prediction model may be amodel that receives part related data and outputs one or morepredictions or answers regarding the probability of part failure. Thetraining data can be gathered from multiple sources including partspecifications, environmental data, sensor data, machine vision data,and outcome data. Some examples of questions that the prediction modelmay answer are: when will the machine fail, what type of failure it willbe, what is the probability that a failure will occur within the next Xhours, what is the remaining useful life of the part, and the like. Theartificial intelligence system 22312 may train one or more predictionmodels to answer different questions. For example, a classificationmodel may be trained to predict failure within a given time window,while a regression model may be trained to predict the remaining usefullife of the machine. In embodiments, training may be done based onfeedback received by the system, which is also referred to as“reinforcement learning.” The artificial intelligence system 22312 mayreceive a set of circumstances that led to a prediction (e.g.,attributes of part, attributes of a model, and the like) and an outcomerelated to the part and may update the model according to the feedback.

In embodiments, the artificial intelligence system 22312 may use aclustering algorithm to identify the failure pattern hidden in thefailure data to train a model for detecting uncharacteristic oranomalous behavior. The failure data across multiple parts and theirhistorical records may be clustered to understand how different patternscorrelate to certain wear-down behavior. For example, if the failurehappens early in the print, the failure may be due to uneven printsurface. If the failure occurs later on in the print, it is likely thatthe part became detached from the printing surface and the cause offailure is poor bed adhesion and/or warping. All of the informationgathered can be used as feedback for the model. Over time, variousfailure modes will become associated with corresponding parameters. Forexample, poor bed adhesion is likely caused by incorrect temperaturesettings or printing orientation. Any failure to meet dimensionaltolerances is likely caused by incorrect acceleration, speed, or layerheight. The artificial intelligence system 22310 can determine thedegree of correlation between each input and each failure mode.

In embodiments, the artificial intelligence system 22312 may beconfigured to monitor cutting tools, filters, and machine lasers toinitiate maintenance or replacement as needed, including platform-widemaintenance management and as part of computerized maintenancemanagement systems (MMS). In embodiments, additive manufacturingentities of a transaction enablement platform may be prepared,configured, and/or deployed to support replacement of parts. Forexample, in connection with a service visit to a home or business, anadditive manufacturing unit may be designated to support the servicevisit, such as a mobile additive manufacturing unit and/or a unitlocated in sufficiently close proximity to the service visit tofacilitate rapid delivery of items produced by the additivemanufacturing unit. Based on the nature of the service visit (e.g., thetype of equipment to be serviced, the nature of component parts andmaterials in the equipment, identified problems, and the like), theadditive manufacturing unit may be equipped with appropriate materials,such as a combination of metal printing materials and other printingmaterials, that are suitable to print a range of possible replacementparts, specialized tools, or other elements to support the servicevisit. In embodiments, the platform may take inputs from or related tothe service visit, such as inputs indicating the item being serviced(e.g., technical specifications, CAD designs, and the like); inputsindicating diagnosed issues (such as a need to replace an entiresub-assembly, a need to repair a crack or other damage, or the like);and inputs captured by cameras, microphones, data collectors, sensors,and other information sources associated with the service visit. Forexample, a service technician may capture a set of photos that show adamaged part. In embodiments, the platform may process the inputs, suchas using an artificial intelligence system (such as a robotic processautomation system trained on a training set of expert service visitdata), to determine a recommended action, which in embodiments, mayinvolve replacement of a part and/or repair of a part. The platform may,in some such embodiments, automatically determine (such as using anartificial intelligence system, such as robotic process automationtrained on an expert data set) whether a replacement part is readilyavailable and/or whether an additive manufacturing system should producethe replacement part, such as to reduce delay, to save costs, or thelike. Similarly, the platform may, in some embodiments, using similarsystems, automatically determine that an element should be additivelymanufactured to facilitate repair, such as where a complementarycomponent may be generated to replace a worn or absent element. Inembodiments, automatic determination may occur using a machine visionsystem that captures a set of photo images from the service visit,compares them to reference designs for applicable parts, and produces aninstruction set for additively manufacturing a complementary elementthat can be added (such as by being adhered with a specified adhesive)to a defective element in order to render the part in compliance withthe reference design. In any such embodiment that recommends orconfigures instructions for additive manufacturing, the platform maydiscover available units, configure instructions, initiate additivemanufacturing, and provide updates to the service technician, such asupdates as to when an element will be ready to use. In embodiments, theplatform, such as through a trained AI agent, may automaticallyconfigure and schedule a set of jobs across a set of additivemanufacturing units with awareness of the status of other relevantentities involved in service and other workflows, such as the overallplanned duration of a service job (e.g., to allow de-prioritization ofadditive manufacturing jobs that will produce outputs that won't be usedimmediately), what other work is being done (e.g., to allow forappropriate sequencing of additive manufacturing outputs that align withoverall workflows), the priority of the service job (e.g., whether itrelates to a mission critical item of operating equipment, versus anon-critical accessory item), the cost of downtime, or other factors. Inembodiments, optimization of workflows across a set of additivemanufacturing entities may occur by having an artificial intelligencesystem undertake a set of simulations, such as simulations involvingalternative scheduling sequences, design configurations, alternativeoutput types, and the like. In embodiments, simulations may includesequences involving additive manufacturing and other manufacturingentities (such as subtractive manufacturing entities that cut, drill, orthe like and/or finishing entities that polish, cure, or the like),including handoffs between sets of different manufacturing entity types,such as where handoffs are handled by robotic handling systems. Inembodiments, a set of digital twins may represent attributes andcapabilities of the various manufacturing systems, various handlingsystems (robotic systems, arms, conveyors, and the like, as well ashuman workforce), and/or the surrounding environment (such as a vehicle,a manufacturing facility, a campus, or even a larger scale entity, suchas a city).

In embodiments, the artificial intelligence system 22312 may beconfigured to manage the real time dynamics affecting inventory levelsfor smart inventory and materials management. This may include, forexample, forecasting inventory levels based on a set of demand factorsand/or supply factors of various types described herein and configuringschedules for additive manufacturing units 22202 to produce items forlocations where shortages are anticipated.

In embodiments, the artificial intelligence system 22312 may beconfigured to build, maintain, and provide a library of parts withpreconfigured parameters, that may be searchable by materials,properties, part type, part class, industry, compliance, etc. This mayinclude, for example, a set of search algorithms that discover parts byreferencing published materials, including website materials, productspecifications, or the like; a set of algorithms that query APIs orother interfaces of parts providers, such as to query databases forparts information; and/or a set of data collection systems that captureimages, sensor data, test data, or the like of or about parts.

In embodiments, the artificial intelligence system 22312 may beconfigured to analyze usage patterns associated with one or more usersand learning user preferences with respect to outputs, timing,materials, colors, shapes, orientations, and/or print strategies. Forexample, the system 22312 may develop a profile, such as by the additivemanufacturing unit 22202, by location, by user, by organization, byrole, or the like, that indicates what materials were used formanufacturing, what processes were used for manufacturing, what shapeswere produced, what finishing steps were undertaken, what colors wereused, what functions were enabled, and the like. The profile may be usedto determine, infer, or suggest preferences of users, organizations, orthe like. For example, an organization's preferred brand colors may berecognized, such that conforming materials and coatings are recommendedand/or preconfigured in development of additive manufacturing steps.

In embodiments, the artificial intelligence system 22312 may beconfigured to perform real time calibration for one or more 3D printers.This may include training on a training data set of calibrationinteractions of expert users. Calibration may be job-specific, such asby training the artificial intelligence system 22312 to calibrate theadditive manufacturing unit 22202 to operate with a specific material,which may include material from a specific bin or lot of the samegeneral type of material.

In embodiments, the artificial intelligence system 22312 may beconfigured to minimize the material waste production during the additivemanufacturing process. This may include configuring production tominimize material that needs to be removed in finishing steps,configuring production to produce outputs where unused material iseasily removed for reuse, and/or configuring production to favorreusable/recyclable materials.

In embodiments, the artificial intelligence system 22312 may beconfigured to detect cyber security risks and threats to the platform22210.

In embodiments, the artificial intelligence system 22312 may beconfigured to assess regulatory compliance. For example, in embodiments,the artificial intelligence system 22312 may be configured to search alibrary or other source of approved or certified product designs, suchas ones that are UL or CE certified, FDA approved, OSHA-approved, or thelike, and compare a design configuration to the same to confirm that anoutput of additive manufacturing will result in a compliant/approvedform of product. In embodiments, the artificial intelligence system22312 may work with a digital twin system, a simulation system, or thelike to simulate performance of a resulting output and may compare thesimulated performance to regulatory or other requirements, such as onesapplying to the ability to withstand forces, chemical effects,biological effects, radiation, or the like. For example, where a productcomponent, such as a housing, is intended to provide shielding fromradiation, the artificial intelligence system 22312 may operate on orwithin a digital twin that includes a radiation propagation physicsmodel to automatically assess whether product materials, thicknesses,and shapes will provide shielding sufficient to meet regulatory and/ordesign requirements.

In embodiments, the artificial intelligence system 22312 may beconfigured to optimize power consumption for the platform 22210. Thismay include training the artificial intelligence system 22312 on atraining set of operational data that includes (a) measuring powerconsumed by various available activities; (b) training the artificialintelligence system 22312 to undertake scheduling of additivemanufacturing jobs according to a predictive model of energy pricing;and/or (c) having the artificial intelligence system undertake a largebody of simulations to select a preferred sequence of operations thatproduces a favorable power consumption pattern.

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins for predicting part shrinkage or expansion.This may include having the artificial intelligence system 22312 use aset of physical models that include thermal coefficients of expansionfor elements, alloys, compounds, mixtures, and/or combinations,including, in embodiments, graded layers of material where there is nota clear boundary between materials. In embodiments, the artificialintelligence system 22312 may be trained based on observed shrinkingand/or expansion during manufacturing and/or use.

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins for predicting part warpage. This may includehaving the artificial intelligence system 22312 use a set of physicalmodels that include thermal coefficients of expansion for elements,alloys, compounds, mixtures, and/or combinations, including, inembodiments, graded layers of material where there is not a clearboundary between materials. In embodiments, the artificial intelligencesystem 22312 may be trained based on observed warpage duringmanufacturing and/or use.

In embodiments, the models trained by the machine learning system 22310may be utilized by the artificial intelligence system 22312 to executesimulations on part twins for calculating necessary changes to the 3Dprinted process to compensate for part shrinkage, expansion, and/orwarpage.

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins for testing the compatibility of additivelymanufactured parts. In embodiments, the compatibility may be tested withone or more other parts in an assembly. In embodiments, thecompatibility may be tested with an operating environment. Inembodiments, the compatibility may be tested with a 3D printer.Compatibility may include shape compatibility (e.g., key-in-lock;housing-around-interior; peg-in-hole; male-with-female,support-with-supported, or other types of interface/interconnectcompatibility); environmental compatibility (e.g., compatibility ofmaterials with anticipated environment of use, such as chemical factors,physical factors, radiation factors, biological factors, temperatures,pressures, and the like); functional compatibility (e.g., ability towithstand loads, stresses, torsion, or the like), and others.

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins for predicting deformations or failure in anadditively manufactured item.

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins for optimizing the build process to minimizethe occurrence of deformations.

In embodiments, the models trained by the machine learning system 22310may be utilized by the artificial intelligence system 22312 to executesimulations on product twins for predicting the price of a product. Inembodiments, prediction of a price may include: (a) prediction based onmarket prices of similar items (and/or forecasts of such prices); (b)prediction based on predicted demand; (c) prediction based on committeddemand; (d) prediction based on smart contract terms and conditions;and/or (e) prediction based on cost, including materials, energy costs,shipping, and labor, among others (which may include a range ofprofit/markup amounts to arrive at a price from a base cost). Inembodiments, price prediction may include wholesale pricing, retailpricing, volume pricing, location-based pricing, and the like.

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins, product twins, and printer twins forgenerating additive manufacturing quotes.

In embodiments, the models trained by the machine learning system 22310may be utilized by the artificial intelligence system 22312 to executesimulations on part twins, product twins, and printer twins forgenerating recommendations related to printing to a user of theplatform. In embodiments, the recommendations may relate to a choice ofa material for printing. In embodiments, the recommendations may relateto a choice of an additive manufacturing technique. In embodiments,recommendations may relate to timing of manufacturing.

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins, product twins, and printer twins forpredicting delivery times for additive manufacturing jobs. Simulationsmay include ones that vary at a level of priority to determine apredicted delivery time under different priority levels (such as toindicate tradeoffs between latency and price/cost).

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins, product twins, printer twins, manufacturingnode twins, or others for predicting cost over-runs in the manufacturingprocess.

In embodiments, the models trained by machine learning system 22310 maybe utilized by the artificial intelligence system 22312 to executesimulations on part twins, product twins, printer twins, andmanufacturing node twins for optimizing the production sequencing ofparts based on quoted price, delivery, sale margin, order size, orsimilar characteristics. In embodiments, optimization may includeoptimization based on public data, such as market data, website data,manufacturer-provided data (such as by APIs), and/or terms andconditions of a set of smart contracts that relate to suchcharacteristics.

In embodiments, the models trained by the machine learning system 22310may be utilized by the artificial intelligence system 22312 to executesimulations on part twins, product twins, and printer twins foroptimizing the cycle time for manufacturing. In embodiments, theoptimizing of cycle time includes time for post-processing (which canvary dramatically per part specifications and additive manufacturingtechnology).

In embodiments, an instruction set for additive manufacturing may beautomatically generated from a text description, such as using a blendof natural language-based artificial intelligence and other artificialintelligence for handling and/or generating images and/or spatialrepresentations, such as using the DALL-E language model from OpenAI™ orother transformer language model (a combination of text-based andimage-based models) further combined with a model for transforming animage into a 3D model and/or a model for transforming an image or 3Dmodel into an additive manufacturing instruction set. The hybridtransformer artificial intelligence system may, for example, be trainedto generate a set of parameters that represent a set of semantic objects(such as a pair of glasses and a cat), generate an output design (suchas glasses that have catlike attributes, such as whiskers or cats-eyelenses), and convert the output design into an additive manufacturinginstruction set. In such embodiments, a user may, for example, enter atext string for a desired output and be provided with a range of 3Dmodels representing options. The user may select the preferred optionand initiate an additive manufacturing job to produce the item. Inembodiments, the platform may track interests, attributes, searchresults, profiles, news topics, or other factors to generate a set ofinput text strings to produce a set of objects that are recommended foradditive manufacturing for a user. In embodiments, recommendations arebased on similarity to other users, such as based on clusteringtechniques. In embodiments, recommendations are based on collaborativefiltering.

In embodiments, the digital twins 22314 are configured to communicatewith a user via multiple communication channels such as speech, text,gestures, and the like. For example, the digital twin may receivequeries from a user about the distributed manufacturing networkentities, generate responses for the queries, and communicate suchresponses to the user. Additionally, digital twins may communicate withone another to learn from and identify similar operating patterns andissues in other distributed manufacturing network entities, as well assteps taken to resolve those issues. For example, the digital twins oftwo manufacturing nodes or those of a part, a printer, and amanufacturing node may communicate with one another for resolving oranswering a customer request.

FIGS. 227A-C are schematics illustrating an example implementation of anautonomous additive manufacturing platform including various componentsalong with other entities of a distributed manufacturing networkaccording to some embodiments of the present disclosure.

The autonomous additive manufacturing platform 22210 may collect datafrom one or more entities including users, programs, and the datasources 22214. A data acquisition system 22702 in user interface 22212may include a set of interfaces like a chat interface 22704, a smartvoice interface 22706, and a file upload interface 22708 to collect datafrom one or more users of the platform. Additionally, one or moresensors 22710 including camera and machine vision system, acoustic/soundsensors (e.g., with microphones, including optionally multiplemicrophones in an array), power and energy sensor, mass sensor, locationsensor, temperature sensor, humidity sensor, pressure sensor, viscositysensor, flow sensor, chemical/gas sensor, strain gauge, thermal imaging,hyperspectral imaging, sound sensor, air quality sensor, and the likemay provide data to platform 22210. The data sources 22214 may alsoinclude programs, the feedback sources 22712 providing outcome data fromthe machine learning system 22310 and a data library 22714.

In embodiments, a data visualization 22715 in the user interface 22212may provide a set of dashboards, interfaces, and integrations for a userof the platform 22210 to visualize information related to thedistributed manufacturing network 22230 or one or more entities in thenetwork 22230. For example, a dashboard may provide visualizationsincluding information related to digital threads for distributedmanufacturing network entities like a 3D printed part or a product.Another dashboard may provide visualizations including information aboutreal time visibility of status of a manufacturing order. An alternatedashboard may provide visualizations including information related tobatch traceability to identify parts from the same batch. A dashboardmay provide visualization of demand factors, including predicted demand,inventory levels, and the like. A search interface may be provided toresolve queries from one or more users based on part, machine,production date, or location. In embodiments, a virtual reality (VR)system may be integrated with the data visualization 22715 and modellingsystem 22720, thereby enabling a user to build 3D models in VR. Inembodiments, the virtual reality system may be integrated with ascanning system 22717, such as allowing a user to build models thatconsist of scanned data (such as point clouds) and/or combinations ofmodel-based VR and scans (and/or other augmentations or overlays, suchas in augmented reality and/or mixed reality models). This may alsoinclude a wider set of user interactions for developing part designswithout in-depth expertise including using augmented reality (AR) andmixed reality (MR).

In embodiments, the user interface 22212 may include a single clickpre-processing process triggering pre-set configurations for partorientation, support determination, toolpath generation, and/or nesting.

In embodiments, the user interface 22212 may include a single clickpost-processing process triggering pre-set configurations forde-powdering, support removal, and surface finishing.

A user of the platform may also use the simulation system 22216 to buildCAD and STL files capturing the design of the part or product to beprinted. A set of design tools 22716 and design libraries 22718 mayallow a user to build models in modelling system 22720 and runsimulations in simulation environment 22722. In embodiments, the designof the part or product may be captured in various file formats includingbut not limited to, IGES files, SolidWorks files, Catia files, ProEfiles, 3D Studio files, STEP files, and Rhino files. In embodiments, thedesign may be captured in the form of digital images, such as in PNGfiles, JPEG files, GIF files, and/or PDF files, as well as scanned dataformats, such as point clouds produced by laser scanning and outputsfrom ultrasound, MRI, x-Ray, electron beam, radar, IR, and otherscanning systems.

The data storage system 22304 may store data in a distributed ledger22724, a digital thread 22726, or the like, such as for maintaining arecord of event data 22728 and a state data 22730 for an entity or assetof the distributed manufacturing network 22230 over time, including apart or products or any other asset or entity described herein.

In embodiments, the digital thread 22726 constitutes information relatedto the complete lifecycle of an item produced by additive manufacturing,such as a part, from design, modeling, production, validation, use, andmaintenance through disposal.

In embodiments, the digital thread 22726 constitutes information relatedto one or more additive manufacturing machines or tools includingpost-processing tools such as CNC equipment, robotics support,product/part marking, metrology equipment, and the like across multiplemanufacturing facilities/locations.

In embodiments, the digital thread 22726 constitutes information relatedto the complete lifecycle of a product from design, modeling,production, validation, use, and maintenance through disposal,optionally including aggregated, linked, or integrated information frommultiple constituents into a full product digital thread.

The data processing system 22306 processes the data collected by datacollection and the management system 22302 to optimize and adjust theprocess parameters in real time through the artificial intelligencesystem 22312 (including the machine learning system 22310), the digitaltwin system 22314, and the control system 22316 as described in detailin FIGS. 223, 224, 225, and 226 or elsewhere herein or in the documentsincorporated herein by reference.

The manufacturing workflow management applications 22308 may manage thevarious workflows, events and applications related to production orprinting and transaction enablement platform management. In embodiments,a matching system 22732 may help with matching a set of customer orderswith a set of additive manufacturing units 22202 or manufacturing nodes.Orders may include firm orders, contingent orders (e.g., based on pricecontingency, timing contingency, or other factors), aggregated orders,custom orders, volume orders, time-based orders, and others. Inembodiments, orders may be expressed in smart contracts, such asoperating on a set of blockchains. The matching may be based on factorslike additive manufacturing capabilities, locations of the customer andthe manufacturing nodes, available capacity at each node, materialavailability, pricing (including materials, energy, labor, andopportunity costs of other available uses for capacity), and timelinerequirements. In embodiments, different parts of a product may bematched with different manufacturing nodes, and the product may beassembled at one of the nodes or elsewhere in a transaction enablementplatform before being finally delivered to the customer.

In embodiments, the additive manufacturing platform may be configured tomaintain an inventory of parts available to large airplane or sea-goingsystems in which multiple redundancies are mandated by custom and/orregulation. In embodiments, example systems include double, triple, ormore redundancies over primary operation systems. In these examples,certain systems may benefit from ready-to-be made products filling infor the third, fourth, etc. redundancy when, previously, a fullinventory to adequately supply the entire third, fourth, etc. redundancywas required. It will be appreciated in light of the disclosure that notall systems will be applicable, in that some critical systems may onlypermit such parts as further layers of redundancies to the alreadymandated supplies. While in flight, the desire to minimize weight andenergy consumption may limit the desire for the creation of certainparts, and the ability to generate parts on longer endurance flights toattend to the needs of the cabin may be one motivation to provide someinflight functionality. For example, locking components that may failmidflight, such as latches, hinges, seatbelts, and the like, can bereplaced or temporarily locked closed to improve in-cabin safety.Components that may have come loose may also be shimmed or temporarilylodged in place by a custom printed part to wedge or hold parts in placethroughout the flight. Examples include holding avionic components in adashboard, overhead, or other cockpit controls, holding hospitalityitems in the galley, holding seats on seat rails, and the like.

In an example, the additive manufacturing platform can be used to createadditional inventory to outfit the airplane for items constructibleinflight that are required on the minimum equipment list to fly and havethose parts replaced before the airplane lands and returns to the gatefor service, thus at least contributing to a repair that otherwise wouldnot require an early landing, but may prevent the next dispatch of theairplane to its next desired use.

In sea-going embodiments, the additive manufacturing platform may beused to create additional inventory to outfit the sea-going vessel withitems constructible during the voyage that are required on the mandatedminimum equipment list to embark (or the like) and have those partsreplaced before the vessel moors and reloads, thus at least contributingto a repair that otherwise would not require a detour and coming ashoreearly, but may prevent the next timely dispatch of the vessel to itsnext desired use.

In embodiments, the additive manufacturing platform may be configured tocoordinate with land-based additive manufacturing assets to coordinateconstruction of parts and coordinated portions of greater assemblies sodowntime in port or in the hanger can be minimized. In this example,entities providing just in time maintenance inventories can extend theirreach and depth by augmenting their one or more offerings orcoordinating their one or more offerings within port or in hangersystems that can be coordinating with one or more in-situ systems activeduring voyage and/or flight.

In embodiments, the matching system 22732 helps with matching anadditive manufacturing task with an engineer where the matching may bebased on factors like task complexity, engineer experience, andexpertise. In embodiments, the matching system 22732 helps with matchingan additive manufacturing task with the location and/or availability ofa finishing worker where the matching may be based on factors like taskcomplexity, worker experience, and expertise. In embodiments, thematching system 22732 helps with matching an additive manufacturing taskwith a set of additive manufacturing units 22202.

In embodiments, a scoring system 22734 helps with scoring and ratingvarious entities in the distributed manufacturing network 22230, such asbased on their performance, quality, timeliness, condition, status, orthe like. In embodiments, the scoring system 22734 helps with rating amanufacturing node based on a customer satisfaction score, such as formeeting customer requirements. In embodiments, the scoring system 22734helps with rating an engineer or other worker based on thecondition/performance in completing an additive manufacturing task,including time required, quality of output, energy used, and otherfactors. In embodiments, the scoring system 22734 helps with rating theadditive manufacturing unit 22202 based on the condition or performancein completing an additive manufacturing task, including process metrics,output metrics, product quality measures, economic measures (such asROI, yield, profit, and the like), customer satisfaction measures,environmental quality measures, and the like.

In embodiments, an order tracking system 22736 helps with tracking aproduct order through its movement in the distributed manufacturingnetwork 22230 till it is finally delivered to the customer. The ordertracking system 22736 may receive state data from various entities ofthe distributed manufacturing network 22230 on real-time or a nearreal-time basis. For example, a 3D printer may provide updates onproduction stage data or a shipping system may provide updates onproduct location. This information may then be tracked, such as by auser or customer identity, on real time or near real-time basis throughthe order tracking system 22736. A workflow manager 22738 manages thecomplete 3D printing production workflow for the distributedmanufacturing network 22230 including various events, activities, andtransactions related to one or more entities of the network 22230.

In embodiments, an alerts and notifications system 22740 providesalerts, notifications, or reports about one or more events to a user orcustomer of the network 22230. For example, the alerts and notificationssystem 22740 may receive data related to certain production parametersor errors based on monitoring of the production workflow, based on whichalerts and notifications may be generated. Such alerts, notifications,or reports may then be transmitted to a computing device (e.g., acomputer, tablet computer, smart phone, telephone, mobile phone, PDA,TV, gaming console, and the like) of a user or customer via email, textmessage, instant message, phone call, and/or other communication (e.g.,using the Internet or other data or messaging network).

In embodiments, the error notifications may provide options for a use ofthe platform 22210 related to continuing or stopping production ormaking adjustments to the design or production settings.

In another example, a user or customer of the distributed manufacturingnetwork may be provided with custom reports, including live status andanalytics based on real-time and historical data of the distributedmanufacturing network 22230. In embodiments, the custom report mayinclude data and analytics related to demand, production capacity,material usage, workflow inefficiencies, output type, output parameters,materials used, cost, ROI, and the like across one or more manufacturingnodes in the network.

In embodiments, the payment gateway 22742 manages the entire billing,payment, and invoicing process for a customer ordering a product usingthe distributed manufacturing network 22230. This may include recordingevents or transactions on an account or ledger, such as a distributedledger, such as a blockchain-based ledger. Payments may be allocatedaccording to a set of rules, such as embodied in a smart contract, suchas to allocate payments across payees; for example, printing from acopyright-protected or other proprietary instruction set may trigger aroyalty payment to the intellectual property owner, manager, or thelike.

It will be apparent that these applications provided by the platform22210 are only presented by way of example and should not be construedas limiting the scope, and many other applications may be provided tomanage one or more aspects of the distributed manufacturing network22230.

In embodiments, an authentication application may be provided toauthenticate the identity of users of the platform through one or moreauthentication mechanisms including a simple username/passwordmechanism, biometric mechanism, or cryptographic key exchange mechanism.Similarly, an authorization application may define the roles and accessprivileges of users of the platform such that users with different rolesare provided different access privileges. For example, an“administrator” or “host” privilege may allow a user of the platform tomake changes to platform configuration, add and remove programs, accessany files, and manage other users on the platform; an “engineer”privilege may allow a user of the platform to operate the platform; anda “service” privilege may allow a user of the platform to access asubset of administrator privileges to perform maintenance and repairactivities.

Some other example applications provided by the platform 22210 forproduction management include part marking, slicing tool selection,alerts and notifications for feedstock supply, printing queuemanagement, printer floor management, job scheduling (including acrossmultiple units), finish work management, packaging management,preparation for logistics, and the like. Some example applicationsprovided by the platform 22210 for production reporting include orderfailure reporting, management information system alerts, remote qualityassurance, certification, indexing, and the like. Some exampleapplications provided by the platform 22210 for production analysisinclude order matching, production failure analysis, warrantymanagement, and so on.

In embodiments, the platform 22210 is integrated with one or more thirdparty systems of various types described herein and in the documentsincorporated by reference herein, such as an Enterprise ResourcePlanning (ERP) system 22744, a Manufacturing Execution system (MES)22746, a Product Lifecycle Management (PLM) system 22748, a maintenancemanagement system (MMS) 22750, a Quality Management system (QMS) 22752,a certification system 22754, a compliance system 22756, a Robot/Cobotsystem 22758, an SCCG system 22760, and the like. In embodiments, theplatform is integrated into a transaction enablement platform controltower system, such as for managing a set of transaction enablementplatform entities.

In embodiments, an API system facilitates the transfer of data betweenthe platform 22210 and one or more third party systems. The API systemmay consist of a set of APIs for transfer of instruction sets, forpassing alerts, notifications, and the like, for transmitting eventstreams (such as workflow-related events), for passing sensor data (suchas process sensing from manufacturing, environmental sensing andothers), for handling user data, for processing payments, forintegrating with smart contracts, blockchains, and other systems, forpassing data with AI systems, for passing data with 3D rendering andother modeling systems, and many others.

In embodiments, the Enterprise resource planning (ERP) system 22744helps streamline and integrate business processes across finance, sales,marketing, service, engineering, product management, accounting,procurement, distribution, resources, project management, riskmanagement, and compliance, among other functions, both within amanufacturing node and across multiple manufacturing nodes in thedistributed manufacturing network 22230. ERP System 22744 may tietogether various production and transaction enablement platformprocesses in the distributed manufacturing network 22230 and enable theflow of data between them.

In embodiments, the manufacturing execution system (MES) 22746 connectsand monitors machines, processes, equipment, tooling, and materials tostreamline manufacturing operations both within a manufacturing node andacross multiple manufacturing nodes in the distributed manufacturingnetwork 22230.

In embodiments, an additive manufacturing platform, such as thatassociated with a transaction enablement platform or other network, maybe designed, prepared, configured, and/or deployed to support thedesign, development, manufacture, and distribution of parts andmaintenance materials (e.g., oil, gas, other chemicals) for vehiclesused to distribute products that may include trucks, trains, airplanes,boats, drones, etc.; parts and maintenance materials for machines (e.g.,robots) used in packaging products; parts and maintenance materials fortools and machines (e.g., robots) used in moving packaged products fromwarehouse to vehicles; arts repair on existing parts (and, while inservice); missing parts from a product that is otherwise ready to go; orsome other part or component for the design, development, manufacture,and distribution of parts and maintenance materials.

In embodiments, an additive manufacturing platform, as described herein,may be designed, prepared, configured, and/or deployed to support themonitoring of packaging materials (e.g., boxes, crates, wrap material,and the like) and need to generate more “as needed.” The additivemanufacturing platform may address a “recall” situation by adding orrevising a product in-warehouse, monitoring for problems with vehicles,machines, tools, and other equipment being used, replacing needed partsor materials “as needed,” creating tools on-demand as needed by workersor robots in warehouse/distribution network, and the like.

In embodiments, an additive manufacturing platform, as described herein,may be designed, prepared, configured, and/or deployed to supportprocessing manufacturing inputs, such as using an artificialintelligence system (e.g., a robotic process automation system trainedon a training set of expert service visit data) to determine arecommended action, which, in embodiments, may involve replacement of apart and/or repair of a part or some other activity. In embodiments, theadditive manufacturing platform may automatically determine that anelement should be additively manufactured to facilitate repair, such aswhere a complementary component may be generated to replace a worn orabsent element. In example embodiments, some techniques and/ortechnologies that may be utilized with the warehouse/distribution centermay include, but are not limited to: providing and/or including multiplesource materials to generate in real time (i.e., on the fly) differenttools, parts, and/or packaging; using AI to optimize product design,manufacturing process configuration (including packaging materialgeneration process), job scheduling, prioritization, and/or logistics(efficiency of warehouse processes for replacing parts, materials, andthe likes without disrupting other general processes involved inwarehouse/distribution center); enriching AI with input/source/trainingset data relevant to design factors, economic factors, quality factors,etc. involved in particular example embodiments (e.g., using sensors andmonitoring of data to adjust manufacturing processes of parts materialsneeded for machines and/or packaging materials); coupling inputs,process data, and outputs with digital twins for running simulations ofindividual processes or a combination of processes to anticipatematerial needs for being able to produce or manufacture tools, parts,packaging, and/or fix machines with materials in real time (as needed);networking additive manufacturing nodes in meshes and/or fleets forcoordinated operation within a warehouse/distribution network in anefficiency manner with respect to producing tools, parts, packaging,and/or other materials used to fix machines in real time; using robotsthat are able to attach to machines and then print directly onto aproduct, print tool, print parts for machines used inwarehouse/distribution network, print packaging, and/or print materialsused to fix machines in real time; using hybrids/pairs of differenttypes of 3D print additive manufacturing including any and all of theitems listed within warehouse/distribution center network processes forfixing products, producing tools, producing parts, producing packaging,and/or producing other materials to fix machines in real time (asneeded).

In embodiments, the Product Lifecycle management (PLM) system 22748helps manage the part or product across the entire lifecycle, fromconception and design through manufacturing and distribution to customeruse and service. The PLM system 22748 may contain accurate, real-timeproduct information across the lifecycle and transaction enablementplatform. This helps with developing and managing the product in amanner that is responsive to feedback from one or more distributedmanufacturing network entities, such as customers using the product,distributors, logistics providers, regulators, safety professionals,service professionals, salespeople, product managers, designers,resellers, and many others. This may also enable an accelerated proof ofconcept and rapid customization of the product in the productdevelopment stage. Also, this may help with predicting product demandand prices, improving customer engagement, performing product testingwhile in customer use, and providing pre-emptive warranty management.

In embodiments, the maintenance management system (MMS) 22750 monitors aset of 3D printers, cutting tools, filters, machine lasers, and othermachines, manages spare parts, maintains records, and uses artificialintelligence and machine learning models to efficiently self-diagnosemaintenance requirements and generate work orders. In embodiments, theMMS 22750 monitors a set of other machines, equipment, products,fixtures, or other assets, maintains records, and manages maintenanceoperations for that set of items, including coordinating additivemanufacturing workflows (such as to produce spare parts, tools,workpieces, accessories, replacement elements, and the like) with othermaintenance workflows. In embodiments, this occurs with automation, suchas robotic process automation, such as where an RPA agent is trainedupon a set of expert interactions to undertake, or to support,operations performed by maintenance workers.

In embodiments, the Quality Management system (QMS) 22752 determineswhether a printed part has been produced correctly by comparing realtime sensor data with expected feedback data wherein the expectedfeedback data is generated from at least one of historical data, testdata, and machine learning. In embodiments, the QMS 22752 also generateswarranty certification including the duration of part warranty and scopeof coverage upon determining completion of testing and qualityassurance.

In embodiments, the QMS 22752 includes automated part metrology andutilizes a vision system with variable focus optical system andartificial intelligence-based pattern recognition for automated partmetrology. In embodiments, the vision system may include a conformablevariable focus liquid lens assembly and a processing system thatdynamically learns on a training data of outcomes, parameters, and datacollected from the conformable variable focus liquid lens assembly totrain an artificial intelligence system to recognize an object. Theconformable variable focus liquid lens assembly may constantly adjustbased on environment factors and on feedback from the processing systemto generate training data that is deeper in context and that correspondsto the physical light that the image represents. By training the visionsystem to recognize objects using variable optical parameters throughthe liquid lens assembly, the processing system may learn about the mostoptimum optical setting to detect an object. The vastly more dynamicinput to the vision system may result in creating a richer context andproviding superior object recognition.

In embodiments, the certification system 22754 is configured to generateworkflow and process control documentation to obtain certificates ofconformance from one or more Manufacturing Certification Authorities orStandards Authorities. In embodiments, the one or more ManufacturingCertification Authorities or Standards Authorities include InternationalOrganization for Standardization (ISO), European Certification (CEmarking) bodies, Underwriters Laboratories (UL), Society of AutomotiveEngineers (SAE), Federal Aviation Administration (FAA), TUV SUD, DNV GL,AS9100, IAQG 9100, American Society of Testing and Materials (ASTM),NIST (research, measurement science and standards), Fraunhofer Institute(research), and Sandia National Labs (research).

In embodiments, the compliance system 22756 is configured to performcompliance checks on 3D printed parts. In embodiments, compliancechecking occurs by or with support from robotic process automation, suchas where a compliance model or algorithm is trained by qualified expertsin certification/compliance with a specific requirement on a trainingset of compliance review data or the like. In embodiments, a set ofdomain-specific or topic-specific models may be trained, such as one foreach compliance domain or topic, such as for compliance withenvironmental standards, material standards, structural standards,chemical standards, safety standards, electrical standards, fire-relatedstandards, and many others.

In embodiments, robot/cobot system 22758 may include an autonomousrobotic system or arm unit integrated with a set of additivemanufacturing units 22202. For example, the additive manufacturing unit22202 may be contained within the housing or body of a robotic system,such as a multi-purpose/general purpose robotic system, such as one thatsimulates human or other animal species capabilities. Alternatively, oradditionally, the additive manufacturing unit 22202 may be configured todeliver additive layering from a nozzle that is disposed on an operatingend of a robotic arm or other assembly.

In embodiments, the autonomous additive manufacturing platform 22210 maycreate and manage profiles of different distributed manufacturingnetwork entities. For example, profiles may include, without limitation:a part or component profile with accompanying part data structures thatmay store part-related information and component-related information,including name, number, class, type, material(s), size, shape, function,performance specifications, and the like; a batch profile withaccompanying batch data structures for storing batch-related informationincluding batch number, batch date, bin number, batch type, locationinformation (such as origin), batch inspection data, and the like; amachine profile with accompanying machine data structures for storingmachine related information including identifier, name, class, functionetc.; a manufacturing node profile with accompanying manufacturing nodedata structure for storing information related to manufacturing nodeincluding identifier, location, order history, production capacity, andprevious product designs; a packager profile with accompanying datastructures for storing packaging related information; a user profilewith accompanying user data structures for storing user relatedinformation; and a behavioral profile with accompanying data structuresfor storing behavioral information, among many others. Some examples ofusers of the platform 22210 may include a designer looking to generate adesign for fabrication; an engineer looking to print and manufacture apart; a CFO looking to optimize price for production; or a customerlooking to get a product printed. Users may include role-based users,such as described in connection with other use cases referenced hereinand in the documents incorporated herein by reference, such as varioususers described in connection with digital twins, such as executive andother role-based digital twins, consumers of automatically generateddata stories, and many others.

The metal additive manufacturing platform 22210 described herein mayhelp in automating and optimizing a very wide range of manufacturing andtransaction enablement platform functions.

Process and Material Selection

The selection and use of one or more processes or materials for additivemanufacturing may be automated and optimized. The platform 22210 maytake as input the product requirements in terms of part properties,price, performance characteristics etc. and automatically determine theprocesses or material for building the part. The artificial intelligencesystem 22312 may consume model information comprising physical,chemical, and/or biological models of material behavior, includingstructural, stress, strain, wear, load bearing, response tocontamination, chemical interaction with other materials, interactionwith biological elements (antibacterial, antiviral, toxicity), etc. Theartificial intelligence system 22312 may then automate and optimizeprocess and material selection, including based on expert feedbackand/or feedback from trials/outcomes.

Referring now to FIGS. 223, 224, and 228 an example embodiment forautomating process and material selection is described.

A part design comprising model information and product requirements ispresented to the design and simulation 22216 where it is evaluated formanufacturing compatibility with at least one type of the additivemanufacturing unit 22202 in the manufacturing node 22200. The design andsimulation 22216 may be assisted by the artificial intelligence 22312,the simulation management 22614, the printer twin 22606 (which, inembodiments, may be a twin of any type of additive manufacturing unit),and the process and material selection twin 23502 for performing theoptimization. An example analysis includes the use of the printer twin22606 in the digital twins 22314 to simulate and compare part designdimensions and accuracy with available 3D printer working envelopes andspecifications.

After a part design is validated to be compatible with one or more ofthe additive manufacturing units 22202 in the manufacturing node 22200,part data for manufacturing may be optimized for export at the designand simulation 22216. For example, an optimized STL file may be producedfrom a finely meshed 3D CAD surface model to meet part accuracyrequirements and then be exported to the autonomous additivemanufacturing platform 22210.

The autonomous additive manufacturing platform 22210 may include aprocess and material selection system 23504. Using optimized part datafrom the design and simulation 22216, external information, includingpricing and market related information from sources such as thetransaction enablement platform entities 22226, and help from theartificial intelligence system 22312, the process and material selectionsystem 23502 performs analysis to select one or more of the additivemanufacturing units 22202 for part manufacturing. In one example, theprocess and material selection system 23502 may analyze availability andcost of printer feedstock materials to select the additive manufacturingunit 22202 that manufactures the part according to specifications whileoptimizing for the lowest cost of manufacture.

Referring to FIGS. 224, 226, and 228 , when manufacturing is complete,part and process data related to the outcome of the 3D printing processis collected by the data collection and management system 22302. Outcomedata is provided to the machine learning system 22310, along withsimulation, external, and training data to train or improve the initialmachine learning model 22313.

The following is an example of autonomous design validation andselection of a 3D printing process and material. Referencing FIGS. 222and 223 , part design data is entered at the user interface 22212 and isthen provided as input to the design and simulation 22216 for partvalidation. The part design data provided at the user interface 22212may include the following part specifications and order requirements: Aform or shape described by a 3D CAD solid model; Use-case loading asapplied to the provided 3D CAD model; Part design stress factor ofsafety: >2; Maximum part weight; Corrosion requirement; Compatibilitywith seawater and salt spray; Order part quantity 10; and Delivery time.

With help from the artificial intelligence system 22312, the design andsimulation 22216 performs multiple screening analyses as follows: amaterial analysis that identifies titanium, Inconel, and 22416 stainlesssteel as materials that meet corrosion requirements; a materialanalysis, assisted by simulations from the printer twin 22606 and theprocess and material selection twin 23502 that identifies powder bedfusion or metal material extrusion as 3D printing processes that matchavailability of the additive manufacturing units 22202; a stress andweight matrix analysis calculated for part geometry and loading thateliminates Inconel and 22416 stainless steel due to weightconsiderations, but qualifies titanium for both weight and maximumstress. Following completion of the screening analysis, process andselection system 23504 is used to complete final additive manufacturingunit 22202 selection from the subset of additive manufacturing units22202 available for manufacturing.

Hybrid Part Workflows

The selection and use of one or more hybrid manufacturing workflowsoptimized for applying additive material on existing parts may beautomated to produce a modified part assembly. Hybrid part workflows canbe used to develop new manufacturing processes, repair existing parts,and modify existing parts to improve transaction enablement platformoutcomes.

The autonomous additive manufacturing platform 22210 may take as inputexisting and OEM part information comprising physical, chemical,manufacturers specifications, etc., including information based onexpert feedback and/or feedback from trials/outcomes. The AI system22312 uses input data to help with automatic validation of a part forone or more hybrid workflows in the workflow management applications22308.

In a part repair example, data from the user interface 22212 and thedata sources 22214 are provided to the design and simulation 22216.Example data includes a combination of measurements and expertobservations and/or OEM part information such as specifications and CADmodels. The design and simulation system 22216 analyzes part dimensionaland material repair requirements with reference to their compatibilitywith at least one type of additive manufacturing unit 22202 in themanufacturing node 22200. The design and simulation 22216 may beassisted by the artificial intelligence 22312, the simulation management22614, and the digital twins 22314, for example, analyses may includethe use of the printer twin 22606 and the part twin 22604 in the digitaltwins 22314 to simulate modified part manufacturing outcomes usingavailable 3D printer capabilities or determine compatibility of OEM partmaterial with available 3D printer materials.

After a modified part is validated by the design and simulation 22216 tobe compatible with one or more of the additive manufacturing units 22202in the manufacturing node 22200, modified part data is exported to theautonomous additive manufacturing platform 22210 where the process andmaterial selection system 23504 selects one or more of the additivemanufacturing units 22202 for manufacturing using one or more hybridworkflows. Example hybrid workflows include the build-up of worn partareas or replacement of chipped or cracked areas of parts.

Referring to FIGS. 226 and 227 , when modified part manufacturing iscomplete, part and process data related to the outcome of the 3Dprinting process is collected by the data collection and the managementsystem 22302, where data comprising modified part parameters,measurements, and so on can be exported to systems responsible formanaging warranty, safety, and related compliance, for example, the ERPsystem 22744, the certification system 22754, the compliance system22756, etc. In embodiments, data may be used to set parameters for asmart contract, such as populating warranty-related, safety-related,liability-related, or other terms of a smart contract. The platformand/or smart contract may store the data in a blockchain.

In embodiments, hybrid manufacturing workflows may be used to modify anexisting part design to produce a new design, for example, whenincorporating new functional or safety features that improve partperformance.

In embodiments, hybrid manufacturing workflows may be used to producenew parts comprising multiple materials that may require more than one3D printer or 3D printing process to produce targeted part or productcharacteristics.

Referring to FIGS. 222 and 223 , in embodiments, hybrid manufacturingworkflows may specify and manage specialized pre-processing 22204 andpost-processing 22206 for the additive manufacturing unit 22202manufacturing. Examples include part cleaning, machining, grinding,surface finishing, etc. to enable 3D printing or to produce modifiedparts that meet original equipment part specifications.

Feedstock Formulation

The selection, purchase, and management of 3D printer feedstock may beautomated and optimized to improve manufacturing efficiency, controltransaction logistics and cost, and to provide new part productioncapabilities.

Referring now to FIG. 228 , a feedstock formulation system 23506, helpedby the artificial intelligence 22312 and a feedback formulation twin23508, automatically formulates and adjusts 3D printer feedstockaccording to production requirements, transaction conditions, pricingand availability information, or other data. For example, the feedstockformulation system 23506 may select commercially available feedstocksuch as Ni Alloy 23518 from GE Additive or suggest local manufacture ofan equivalent material at lower cost from commercially availableconstituent materials. In embodiments, pricing and availabilityinformation may be managed by processing, such as by an API of theplatform and/or the feedstock formulation system, a set of the terms andconditions of a set of smart contracts, such as smart contracts thatprovide current and/or future (e.g., in a spot market at designed timesin the future) pricing information, availability information (includingby volume, by time and by delivery location) for various classes offeedstock materials, including by material type, material quality (e.g.,where there are varying grades of the material that can be purchased asfeedstock), or other properties (such as material origin (e.g.,reclaimed from recycling or other sustainable sources, mined withsustainable practices, purchased from ethical sources, and the like)).In embodiments, the platform may aggregate availability information,pricing, and the like across multiple smart contracts or a blend ofsmart contracts and other sources (e.g., offers that are placed in theplatform by data entry and/or API) to provide an aggregated feedstockavailability data structure upon which the system may operate, such aswhere feedstock may come in lots or batches from different suppliers,places of origin, and the like. The platform may automatically generatea feedstock purchasing plan, which may include a set of currentpurchases, purchases of options or futures, and a plan for futurepurchases. In embodiments, the platform may automatically modify thefeedstock purchasing plan based on changes in conditions, such as needs(e.g., where production varies relative to plan and/or demand variesrelative to plan), pricing (of end products and/or materials),availability, and the like. This may occur using artificialintelligence, such as by robotic process automation trained on atraining set of feedstock purchasing management data, which may use anyof the machine learning or other artificial intelligence techniquesdescribed herein, including supervised, semi-supervised, and/or deeplearning. The artificial intelligence system may further adjust a set ofcontract terms and conditions for feedstock purchasing according to themodified plan, such as by operating on a set of smart contracts viatheir APIs or other interfaces and/or by providing a set ofrecommendations for execution by a user or a hybrid of a user and anintelligent agent or other artificial intelligence system.

In embodiments, the feedstock formulation system 23506 may formulate oneor more custom feedstocks with help from the machine learning system22310, the artificial intelligence system 22312, the machine learningmodel 22313 for feedback formulation, the simulation management system22614, and the feedstock formulation twin 23508. The machine learningsystem 22310 may train a model using feedstock data that may be storedin a feedstock datastore, such as a graph DB that organizes differentfeedstocks according to performance properties. The simulationmanagement system 22614 may run simulations using the feedstockformulation twin 23508 to vary feedstock properties and to record theoutcome of each simulation. In embodiments, printer twin 22606 may alsobe used to simulate and compare future manufacturing outcomes whenvarying feedstock formulation.

Referring to FIGS. 224 and 228 , the feedstock formulation system 23504works with the artificial intelligence system 22312 and the machinelearning system 22310. A combination of training, manufacturing outcome,and external data such as pricing and availability information andexpert and customer feedback is collected at the data collection andmanagement system 22302, where it is used to train or improve theinitial machine learning model 22313 for feedback formulation.

Referring now to FIGS. 222, 223, and 228 , in embodiments, the feedstockformulation system 23506 may include a physical subsystem that isintegrated with the manufacturing node 22200 and one or more of theadditive manufacturing units 22202. This physical subsystem of thefeedback formulation system 23504 may be managed by the autonomousadditive manufacturing platform 22210. The manufacturing workflowmanagement applications 22308 may include an application that routesfeedstock material as necessary, and the data collection and managementsystem 22302 may provide feedstock inventory levels. The feedstockformulation system 23504 may include one or more automated productionand transport systems that deliver feedstock material and performfeedstock material changes for the additive manufacturing unit 22202.

Design Optimization

Optimizing part design for use with additive manufacturing processestypically requires special software, equipment, training, technicalknowledge, and the ability to provide and interpret process data andmanufacturing outcomes. Autonomous or guided product design can be usedto improve transaction enablement platform outcomes by usingpre-engineered part libraries or expert systems to provide eitherautonomous part design or expert-assisted designs that are optimized formetal additive manufacturing processes. Resulting workflow and processfunctionality may be further optimized by incorporating limitations orrecommendations based on real-time analysis of transaction enablementplatform entities that provide data on the availability of a selectedmaterial or 3D printer, part cost and delivery time, and so on.

Referring to FIGS. 227A, 227B, and 227C, part design optimization for 3Dprinting processes may be automated using the design and simulation22216, where part function and/or class criteria are organized in adesign library 22718 and used to guide or fully automate part design formanufacturing. Part functions and classes have inherent minimum designcriteria imposed by standards, best practices, engineering experts, andso on. Part function examples include a self-lubricating bearing madefrom sintered metal that must meet chemical, mechanical, and otherproperties found in the ISO 5755 standard or an electrical hand toolwhere materials must meet 1000V electrical insulation standards found inthe IEC 60900 standard. Part classification examples include parts foruse in explosive atmospheres, where materials of construction must benon-sparking or parts for medical tools used in surgery, where corrosioncharacteristics must comply with the ASTM F1089 standard.

Referring to FIGS. 223, 224, 227, and 228 , in one example embodiment, anew part request that has a specific function is received by the userinterface 22212 and communicated to the design and simulation 22216,where the design libraries 22718 are searched for tested and viable 3Dprinted part models that match part function. In embodiments, one ormore parts from the design library 22718 are recommended to the user,such as via the interface 22212, as a design recommendation or guidance.In embodiments, design libraries may also include product assemblies,wherein completed assemblies and all parts in the assembly meetfunctional or class criteria.

In embodiments, one or more candidate parts are automatically selectedby a design optimization system 23510. With help from the machinelearning system 22310 and the artificial intelligence system 22312, thedesign optimization system 23510 optimizes the part design and submitsthe same to the autonomous additive manufacturing platform 22210 formanufacturing.

In embodiments, the design optimization system 23510 may use machinelearning models trained by product design experts. In embodiments, thedesign optimization system 23510 may use machine learning models trainedusing data of prior designs and their outcomes.

In embodiments, the design optimization system 23510 may use agenerative or evolutionary approach to design. The system may start withdesign goals and then explore innumerable variations by addingconstraints before selecting a final design based on evolutionarymodels. The evolutionary models are based on the principle of naturalselection, such as where the most optimal designs are selected fromamong an initial population of potential designs through a series ofevolutionary stages. Generative models may include models like DALL-E™that mix visual and text-based artificial intelligence systems, as wellas further hybrids for generating visual, 3D, text, color, texture,strength, flexibility, and many other properties, including usingspecialized artificial intelligence systems for generating variations ofeach of a large set of properties and generating combinations, such aspairs, triplets, and higher-order n-tuples of properties. Inembodiments, generative models may generate and/or select designinstance that represent combinations of properties that are shared amongsemantically distinct objects or topics, such as a cat and basket inorder to produce and/or select a set of designs that embody the sharedset of properties.

In embodiments, evolutionary models may be based on genetic algorithms(GA), evolution strategy (ES) algorithms, evolutionary programming (EP),genetic programming (GP), and other suitable evolutionary algorithms. Inembodiments, the evolutionary models may use various feedback andfiltering functions, such as ones based on semantic properties, onesbased on design constraints (such as acceptable color palette forbrand), ones based on physical or functional requirements, ones createdby consumer engagement (such as surveys, engagement tracking and/or ABtesting), ones based on outcomes (such as sales, profits, or others),ones based on cost (of materials, manufacturing, logistics, or others),ones based on safety or liability, ones based on regulatory requirementsor certification, and many others. In embodiments, feedback to designevolution is taken from a set of smart contracts, such as a set of smartcontracts that offer various design variations for purchase,reservation, or the like. For example, a design may be evolved based onfavorable smart contract engagement, such as where a particular designis reserved via the set of smart contracts at a profitable price and infavorable volumes.

In embodiments, an evolutionary design system coupled to a set ofadditive manufacturing units 22202 continuously offers a set of productsvia smart reservation contracts by which users may reserve units formanufacturing according to the offered designs, such that the capacityof the additive manufacturing system is continuously engaged in evolvingthe designs to provide the most favorable outcomes in the smartcontracts (based on measures of profitability, for example) and sellingthe products to the users who reserved them via the smart contracts.Smart contract parameters, including prices, terms of delivery, and thelike, may be automatically adjusted, such as to account for time tomanufacture, logistics factors, and the like. The system may beconfigured to integrate with an e-commerce system, such as to offerproducts on a marketplace, an auction site, a mobile application, or thelike, as well as with other environments where purchasing is enabled,such as on-site systems (kiosks), in-game transaction environments,AR/VR environments, smart displays, and many others.

Referring to FIG. 224 and FIG. 228 , when manufacturing is complete,part and process data related to the outcome of the 3D printing processis collected by the data collection and management system 22302. Outcomedata is provided to the machine learning system 22310 as feedback alongwith simulation, external, and training data to train or improve thelearning model 22313.

Risk Prediction and Management

Referring now to FIG. 228 , a risk prediction and management system23512 interfaces with, links to, or integrates the artificialintelligence system 22312. In example embodiments, the risk predictionand management system 23512 may be configured to predict and manage riskor liability with respect to manufacturing, delivery, utilization and/ordisposal of a part, product or other item by the distributedmanufacturing network 22230, among other risks or liabilities.

In embodiments, the machine-learning system 22310 trains one or more ofthe models 22313 that are utilized by the artificial intelligence system22312 to make classifications, predictions, and/or other decisionsrelating to risk management, including for parts and productsmanufactured by the distributed manufacturing network 22230 and for thesystems, workflows, and other activities in which they are involved.

In example embodiments, the model 22313 may be trained to predict riskof part failure by detecting the condition of a part. The machinelearning system 22310 may train the model using part data and one ormore outcomes associated with the part condition, such as on a trainingset of data on outcomes of similar parts, similar materials, and thelike, including historical data on wear-and-tear during usage,historical data on material deterioration under various ambient orenvironmental conditions, data on defects or faults discovered duringinspection or reported by customers or others, and other data sources.Part data may include any of the attributes or parameters notedthroughout this disclosure and the documents incorporated by referenceherein, such as part material, part properties, manufacturing date,material supplier, part specifications, and the like. In this example,outcomes used to train the machine learning system 22310 to predictrisk, including, but not limited to, failure of liability, may includeprojected outcomes from models, such as scientific models of varioustypes described throughout this disclosure and the documentsincorporated by reference herein (e.g., physics, chemistry, biology,materials science, and others), economic models, and many others, which,in embodiments, may be embedded into a digital twin system, such as tomodel whether a part twin 22604, product twin, or other twin is in afavorable operating condition during or after simulation of a set ofevents, a passage of time, or the like. In this example, one or moreproperties of the part twin 22604 are varied for different simulationsand the outcomes of each simulation may be recorded. Other examples oftraining risk prediction and management models may include the model22313 that is trained to optimize product safety, a model that istrained to identify parts with a high likelihood of failure, and thelike.

In example embodiments, the model 22313 may be trained to predict riskof non-delivery of a product to a customer, such as due to supply chainand other disruptions, such as ones caused by various external eventslike equipment failures, strikes, and other labor disruptions, bordercontrol activities (such as customs inspections, travel bans, andothers), limits on shipping, traffic congestion, power outages, stormsand other natural disasters, catastrophes, economic disruptions (such aslarge changes in tariffs), regulatory changes (such as bans on import orexport or changes in where products may be legally sold or used),pandemics, political unrest, and the like. In this example, a model maybe trained to predict supply chain disruption by discovering,extracting, transforming, normalizing, processing, and/or analyzing datafrom one or more external sources like social media feeds, weatherpatterns, news feeds, websites (e.g., websites providing contentrelevant to the above, marketplace websites, research websites, andothers), crowdsourcing systems (which may include posing queries orprojects to crowds in order to solicit input on specific factors, suchas economic factors, behavioral factors, trends, and the like),algorithms (such as ones trained to provide specific predictions ofevents), and many others.

Marketing and Customer Service

Referring now to FIG. 228 , a marketing and customer service system23516 interfaces with, links to, or integrates the artificialintelligence system 22312. In example embodiments, the marketing andcustomer service system 23516 may be configured to provide personalizedsales, marketing, advertising, promotion and/or customer service withrespect to a product or other item provided by the distributedmanufacturing network 22230.

In embodiments, the machine-learning system 22310 trains one or more ofthe models 22313 that are utilized by the artificial intelligence system22312 to make classifications, predictions, and/or other decisionsrelating to sales, marketing, advertising, promotion, and/or customerservice for products manufactured by the distributed manufacturingnetwork 22230.

In example embodiments, the model 22313 may be trained to predictbehavior and purchase patterns of one or more customers to providepersonalized sales, marketing, advertising, promotion, and/or customerservice. In embodiments, the machine learning system 22310 may train themodel using customer data and one or more outcomes associated withcustomer response to a personalized campaign, such as using various datasources that provide insight into consumer sentiment, behavior, or thelike, including search engines, news sites, websites, behavioralanalytic systems and algorithms, consumer sentiment measures,microeconomic measures, macroeconomic measures, and many others. A modelmay be seeded with various economic, behavioral, and other models,including demographic, psychological, economic, game theoretic,cognitive, and other models. Customer data may include any of the typesdescribed throughout this disclosure and the documents incorporated byreference herein, such as identity data, transactional and payment data,location data, demographic data, psychographic data, location data,wealth data, income data, sentiment data, affinity data, loyalty programdata, clickstream data (including interactions with social media,applications, websites, mobile devices, AR/VR systems, video games,entertainment content, and other digital content), point-of-sale data,in-store behavioral data (such as path tracing data within stores, dwelltimes associated with particular types of products, and the like), brandloyalty data, shopping data, search engine data (such as search topicsinvolving shopping), social media footprint, purchase history, loyaltyprogram data, and many others. The customer twin 23518 may capture a setof customer responses to a marketing or advertising campaign or one ormore product recommendations, offers, advertisements, or othercommunications by tracking outcomes like customer attention or actions(including mouse movements, mouse clicks, cursor movements, navigationactions, menu selections, and many others) measured through a softwareinteraction observation system or purchase of a product by a customer.In this example, one or more parameters of the marketing or advertisingcampaign may be varied for different simulations of a customer twin andthe outcomes of each simulation may be recorded.

In embodiments, the marketing and customer service system 23516 mayinterface with the artificial intelligence system 22312 to providepersonalized sales, marketing, advertising, promotions, and/or customerservice, including providing personalized marketing and advertisingcampaigns and providing product recommendations. In embodiments, theartificial intelligence system 22312 may utilize one or more of themachine-learned models 22313 to determine a product recommendation. Inembodiments, the simulations run by the customer twin 23518 may be usedto train the product recommendation machine-learning models. In each ofthese examples, a campaign communication, recommendation, or the likemay involve a product or other item that can be manufactured by theadditive manufacturing unit 22202 with a set of attributes that aretailored to the customer and that can be delivered to a designated siteof the customer within a designated time frame at a proposed price.Customization of the offer/recommendation may include providing a designof a product or part to include attributes favored by the customer,including functional attributes, preferred materials (such as to matchmaterials of products already owned by the customer), preferred colors,preferred shapes, and many others. In embodiments, customization mayreference an understanding of products already owned by the customer,such as based on purchase history information, such as where arecommended product can be configured to work as part of a family ofproducts, such as by recommending a product that has compatible color,shape, size, material type, connectivity (e.g., to work as part of aconnected set of products), communication protocol, logo, or the like.

In embodiments, the additive manufacturing platform 22210, such as thatassociated with a transaction enablement platform network may beprepared, configured, and/or deployed to support printing ofpersonalized entertainment props, backdrops, and other items at themeparks, cruise ships, theater and film productions, and/or otherentertainment venues. For example, in connection with a cruise ship, theadditive manufacturing unit 22202 may be designated to support theprinting of cabins, themed rooms, or furniture to fit based on a giventheme. The customers may provide their preferences in terms of roomlayout and design and/or furniture and accessories, which can bedynamically printed. Similarly, for theme parks, the additivemanufacturing unit 22202 may be designated to support the printing ofrockwork, rides, and other attractions and, for theater and filmproductions, movie props, costumes, sets, artifacts and otheraccessories may be custom printed.

In embodiments, the platform may take inputs from or related to theentertainment venue owner, such as inputs indicating the item beingprinted (e.g., technical specifications, CAD designs, or the like);inputs indicating requirements (such as a need to improve an existingroller coaster attraction with custom rockwork, a need to build adinosaur replica, or the like); and inputs captured by cameras,microphones, data collectors, sensors, and other information sourcesassociated with the entertainment venue.

In embodiments, that recommend or configure instructions for additivemanufacturing, the platform 22210 may discover available materialsincluding fabrics, metals plastics etc., configure instructions,initiate additive manufacturing, and provide updates to the owner of theentertainment venue, such as updates as to when an element will be readyto use. The platform 22210 may, in some such embodiments, automaticallydetermine, such as by using the artificial intelligence system 22312trained on an expert data set and the like, whether a suitable item isreadily available and/or whether use of an additive manufacturing systemto produce the item(s) can reduce delay to save costs or the like.

In embodiments, the platform 22210, such as through a trained AI agent,may automatically configure and schedule a set of jobs across a set ofadditive manufacturing units 22202 with awareness of the status of otherrelevant entities involved in other workflows, such as what other workis being done (e.g., to allow for appropriate sequencing of additivemanufacturing outputs that align with overall workflows), the priorityof the printing job (e.g., whether it relates to a film scene beingshot), the cost of downtime, or other factors. In embodiments,optimization of workflows across a set of additive manufacturingentities may occur by having the artificial intelligence system 22312undertake a set of simulations, such as simulations involvingalternative scheduling sequences, design configurations, alternativeoutput types, and the like. In embodiments, simulations may includesequences involving additive manufacturing and other manufacturingentities (such as subtractive manufacturing entities that cut, dye, orthe like and/or finishing entities that sew, configure, add customerinitials, or the like), including handoffs between sets of differentmanufacturing entity types, such as where handoffs are handled byrobotic handling systems. In embodiments, a set of digital twins mayrepresent attributes and capabilities of the various manufacturingsystems, various handling systems (robotic systems, arms, conveyors, andthe like, as well as human workforce), and/or the surroundingenvironment.

It will be apparent that in the above decisions related to predictions,optimizations using the artificial intelligence system 22312 of platform22210 are only presented by way of example and should not be construedas limiting. There may be many other use cases including decisionsrelated to prediction and optimization of pricing by a CFO twin 23520;decisions related to new product launch by a CEO twin based onbehavioral patterns and market trends; and the like.

In embodiments, the autonomous additive manufacturing platform 22210enables the distributed manufacturing network 22230 by managing theproduction workflows within and across one or more manufacturing nodes,thereby facilitating collaboration across the manufacturing nodesthrough the sharing of resources, capabilities, and intelligence. Inembodiments, the manufacturing nodes may collaborate for forecasting andprediction of material supply and product demand. In embodiments, themanufacturing nodes may collaborate for design and product development.In embodiments, the manufacturing nodes may collaborate formanufacturing and assembling one or more parts of a product. Inembodiments, the manufacturing nodes may collaborate for distributionand delivery of manufactured products.

The distributed manufacturing network 22230 may thus provide“manufacturing as a service” by leveraging unutilized capacity of one ormore 3D printers by exposing the capacity to one or more users/designersseeking to fabricate 3D printed parts.

In embodiments, a method for facilitating the manufacture and deliveryof a 3D printed product to a customer using one or more manufacturingnodes of the distributed manufacturing network 22230 includes receivingone or more product requirements from the customer; determining one ormore manufacturing nodes, processes and materials based on the productrequirements; generating a quote including pricing and deliverytimelines; and upon acceptance of the quote by the customer,manufacturing and delivering the 3D printed product to the customer.

In embodiments, the product requirements may be a 3D printinginstruction set including a file (e.g., a CAD file and/or an STL file)and any accompanying instructions for printing the product defined inthe file.

In embodiments, the distributed manufacturing network may be implementedthrough a distributed ledger system integrated with the digital threadfor storing a set of entities, activities, and transactions related tothe distributed manufacturing network.

In embodiments, a smart contract system may communicate with thedistributed ledger system and may be configured to implement and managea smart contract via the distributed ledger. The smart contract may bestored in the distributed ledger and may include a triggering event. Thesmart contract may be configured to perform a smart contract action inresponse to an occurrence of the triggering event. The distributedmanufacturing network may be configured to receive from a user aninstance of the 3D printing instruction set. The 3D printing instructionset may be tokenized such that the instance of the 3D printinginstruction set can be manipulated as a token on the distributed ledger.The tokenized 3D printing instruction set may be stored via thedistributed ledger. Commitments of various parties (distributedmanufacturing network entities) to the smart contract may be processed.The use of smart contracts in the distributed manufacturing networkhelps in automating the distributed manufacturing workflow.

In embodiments, the distributed manufacturing network facilitates thecreation of a distributed manufacturing marketplace or exchange forbuying and selling of additive manufacturing parts, products, andinstruction sets with the manufacturing nodes constituting the sellersand customers constituting the buyers.

In embodiments, the distributed manufacturing network facilitates thecreation of a data marketplace for selling of operational additivemanufacturing data by manufacturing nodes to data aggregators. Inembodiments, the data marketplace is built on a distributed ledger, andmanufacturing nodes are compensated using digital token via smartcontracts. In embodiments, the data is anonymized to hide the identityof manufacturing nodes that own the data.

FIG. 229 is a diagrammatic view of a distributed manufacturing networkenabled by an autonomous additive manufacturing platform and built on adistributed ledger system according to some embodiments of the presentdisclosure.

The distributed manufacturing network 22230 is implemented with adistributed ledger system where the distributed ledger may bedistributed at least in part over nodes of the distributed manufacturingnetwork 22239 and may include blocks linked via cryptography. Thedistributed ledger system stores data related to a set of entities,activities, and transactions in the distributed manufacturing network22230.

The different manufacturing nodes 22200, manufacturing node 22228,manufacturing node 22900, and manufacturing node 22902 each represent anode in the distributed manufacturing network 22230. Also, the differentsystems within a manufacturing node including the additive manufacturingunit 22202, the pre-processing system 22204, the post-processing system22206, the material handling system 22208, the autonomous additivemanufacturing platform 22210, the user interface 22212, the data sources22214, and the design and simulation system 22216 referred to asdistributed manufacturing network entities constitute distributedcomputing nodes of the distributed ledger system.

The distributed computing node is essentially a computing device havinga processor and a computer-readable medium having machine-readableinstructions stored thereon and contains full copy of the transactionhistory of the distributed ledger. The nodes of the distributed ledgermay be implemented in a variety of computing systems including additivemanufacturing systems, enterprise systems, inventory management systems,packaging systems, shipping and/or delivery tracking systems, SKUdatabases, smart factories, and so on. Whenever additional transactionsare proposed to be added to the distributed ledger, one or more of thenodes typically validate the proposed additional transaction records,such as via a consensus algorithm. Typically, once the proposedtransaction has been validated e.g., through any consensus algorithm,the proposed transaction is added to each copy of the distributed ledgeracross all the nodes.

In embodiments, the transaction data is validated by the nodes through aproof-of-work (POW) consensus algorithm and hashed into an ongoing chainof cryptographically approved blocks of transaction records constitutingthe distributed ledger.

In embodiments, proof of work algorithms require the nodes to perform aseries of calculations to solve a cryptographic puzzle. For instance, inorder to validate a pending data record, the nodes may be required tocalculate a hash via a hash algorithm (e.g., SHA256) that satisfiescertain conditions set by the system. The calculating of a hash in thismanner may be referred to herein as “mining,” and the nodes performingthe mining may be referred to as “miners” or “miner nodes.” Thedistributed ledger may, for example, require the value of the hash to beunder a specific threshold. In such embodiments, the nodes may combine a“base string” (i.e., a combination of various types of metadata within ablock header, e.g., root hashes, hashes of previous blocks, timestamps,etc.) with a “nonce” (e.g., a whole number value) to be input into thePOW algorithm to produce a hash. In an exemplary embodiment, the noncemay initially be set to 0 when calculating a hash value using the POWalgorithm. The nonce may then be incremented by a value of 1 and used tocalculate a new hash value as necessary until a node is able todetermine a nonce value that results in a hash value under a specifiedthreshold (e.g., a requirement that the resulting hash begins with aspecified number of zeros). The first node to identify a valid nonce maybroadcast the solution (in this example, the nonce value) to the othernodes of the distributed ledger for validation. Once the other nodeshave validated the “winning” node's solution, the pending transactionrecord may be appended to the last block in the distributed ledger. Insome cases, a divergence in distributed ledger copies may occur ifmultiple nodes calculate a valid solution in a short timeframe. In suchcases, the nodes using the POW algorithm accept the longest chain ofblocks (i.e., the chain with the greatest proof of work) as the “true”version of the distributed ledger. Subsequently, all nodes having adivergent version of the distributed ledger may reconcile their copiesof the ledger to match the true version as determined by the consensusalgorithm.

In other embodiments, the consensus algorithm may be a “proof of stake”(“PoS”) algorithm, in which the validation of pending transactionrecords depends on a user's “stake” within the distributed ledger. Forexample, the user's “stake” may depend on the user's stake in a digitalcurrency or point system (e.g., a cryptocurrency, token system, assetshare system, reputation point system, etc.) within the distributedledger. The next block in the distributed ledger may then be decided bythe pending transaction record that collects the greatest number ofvotes. A greater stake (e.g., in a given digital currency or tokensystem) results in a greater number of votes that the user may allocateto particular pending transaction records, which in turn increases thechance for a particular user to create blocks in the distributed ledger.In embodiments, a distributed ledger need not be based on a token orcryptocurrency system, but rather may be secured by conventional orother security techniques, for example. In embodiments, such as onesinvolving a digital thread, proof of stake may be weighted, such aswhere a product manufacturer's votes, a customer's votes, or the likecount more than an arbitrary third party.

In yet other embodiments, a consensus algorithm may be a “practicalbyzantine fault tolerance” (“PBFT”) algorithm, in which each nodevalidates pending transaction records by using a stored internal statewithin the node. In particular, a user or node may submit a request topost a pending transaction record to the distributed ledger. Each of thenodes in the distributed ledger may then run the PBFT algorithm usingthe pending transaction record and each node's internal state to come toa conclusion about the pending transaction record's validity. Uponreaching said conclusion, each node may submit a vote (e.g., “yes” or“no”) to the other nodes in the distributed ledger. A consensus isreached amongst the nodes by taking into account the total number ofvotes submitted by the nodes. Subsequently, once a threshold number ofnodes have voted “yes,” the pending transaction record is treated as“valid” and is thereafter appended to the distributed ledger across allof the nodes.

In embodiments, the nodes are paid a transaction fee for their miningactivities. In embodiments, the distributed ledger is a private andpermissioned blockchain controlled by a single entity or a consortium oftrusted entities that is built using a pre-built API provided on CORDA,Hyperledger, and Quorum.

In embodiments, the distributed ledger is a public, permissionlessblockchain that is built on Ethereum or bitcoin blockchain.

In embodiments, transaction records stored in the distributed ledger maybe hashed, encrypted, or otherwise protected from unauthorized accessand may only be accessible utilizing a private key to decrypt the storedinformation/data.

The blockchain may be a single blockchain configured for storing alltransactions therein, or it may comprise a plurality of blockchains,wherein each blockchain is utilized to store transaction recordsindicative of a particular type of transaction. For example, a firstblockchain may be configured to store shipment data and transactions,and a second blockchain may be configured to store financialtransactions (e.g., via a virtual currency).

In embodiments, the distributed ledger system includes a decentralizedapplication downloadable by entities in the distributed manufacturingnetwork.

In embodiments, the distributed ledger system includes a user interfaceconfigured to provide a set of unified views of the workflows to the setof entities of a distributed manufacturing network.

In embodiments, the distributed ledger system includes a user interfaceconfigured to provide tracking and reporting on state and movement of aproduct from order through manufacture and assembly to final delivery tothe customer.

In embodiments, the distributed ledger system includes a system fordigital rights management of entities in the distributed manufacturingnetwork. In embodiments, the distributed ledger system stores digitalfingerprinting information of documents/files and other informationincluding creation, modification, and the like.

In embodiments, the distributed ledger system includes a cryptocurrencytoken to incentivize value creation and transfer value between entitiesin the distributed manufacturing network.

In embodiments, the distributed ledger system includes a system forattesting the experience of a manufacturing node.

In embodiments, the distributed ledger system includes a system forcapturing the end-to-end traceability of a part.

In embodiments, the distributed ledger system includes a system fortracking all transactions, modifications, quality checks, andcertifications on the distributed ledger.

In embodiments, the distributed ledger system includes a system forvalidating capabilities of a manufacturing node.

In embodiments, the distributed ledger system includes smart contractsfor automating and managing the workflows in the distributedmanufacturing network.

In embodiments, the distributed ledger system includes a smart contractfor executing a purchase order covering the scope of work, quotation,timelines, and payment terms.

In embodiments, the distributed ledger system includes a smart contractfor processing of payment by a customer upon delivery of product.

In embodiments, the distributed ledger system includes a smart contractfor processing insurance claims for a defective product.

In embodiments, the distributed ledger system includes a smart contractfor processing warranty claims.

In embodiments, the distributed ledger system includes a smart contractfor automated execution and payment for maintenance.

FIG. 230 is a schematic illustrating an example implementation of adistributed manufacturing network where the digital thread data istokenized and stored in a distributed ledger so as to ensuretraceability of parts printed at one or more manufacturing nodes in thenetwork according to some embodiments of the present disclosure. A userof the distributed manufacturing network 22230 may provide the productrequirements in the form of a purchase order or a 3D printinginstruction set 23002. The 3D printing instruction set 23002 containskey specifications and requirements like product design, material forprinting, quantity to be printed, price that the user is willing to payfor the print, and the timelines for completing the printing. The 3Dprinting instruction set 23002 may also include one or more files (e.g.,a CAD file and/or an STL file) and any accompanying instructions forprinting the product defined in the file.

Upon receipt, the 3D printing instruction set 23002 is tokenized andstored in the distributed ledger 22724 in the autonomous additivemanufacturing platform 22210. The underlying information in the 3Dprinting instruction set 23002 is stored in the form of a unique recordrepresented by a block number with an address on the distributed ledger,which in turn is represented by a cryptographic token. The cryptographictoken captures the value of the underlying information in the 3Dprinting instruction set 23002 as ownership or access rights to thedistributed ledger address and tracks the transfer of such ownershipbetween users of the distributed manufacturing network 22230. Forexample, in FIG. 230 , the 3D printing instruction set 23002 istokenized in the form of a random 22356 bit integer A091BC3, and storedin the distributed ledger 22724 represented by address BC22. As the newblock is added to the distributed ledger 22724 at node 22228, all thecopies stored at various nodes, including the manufacturing node 22200,the manufacturing node 22900, and the manufacturing node 22902, getupdated with the new block. The matching system 22732 in the autonomousadditive manufacturing platform 22210 may help with matching thepurchase order or the 3D printing instruction set 23002 with one or moremanufacturing nodes or 3D printers. The matching may be based on factorslike printer capabilities, locations of the customer and themanufacturing nodes, available capacity at each node, pricing, andtimelines requirements. In embodiments, a smart contract operates on theledger, such as to trigger conditional logic embodied in the smartcontract, such as tracking satisfaction of delivery obligations,releasing insurance obligations (such as insurance covering productsduring shipment), and the like. In embodiments, the smart contract mayallocate financial value, such as to tax and customs authorities, tocredit and debit card issuers, to distributers and resellers, torecipients of commissions, to recipients of royalties, to recipients ofrebates, credits and the like, to shippers/carriers, and to themanufacturer, among others.

In embodiments, the matching system 22732 may determine that the parts23004 and 23010 of the product be matched to the manufacturing node22200 for printing, parts 23006 and 23008 matched to the manufacturingnode 22228, and parts 23012 and 23014 matched to the manufacturing node22902. The assembly of all the parts into the final product may bematched to the manufacturing node 22900.

Each of the parts may also be tokenized to capture information includingpurchase order identifier (orderID), instruction set identifier(fileID), manufacturing node (manufacturerID), 3D printer (printerID),part number (partID), and part specifications containing informationlike material and quantity etc. and stored as a record or block in thedistributed ledger. The parts can then be tracked using a physicaltracker using a unique part number, engraving, RFID tag, bar code orsmart label linked to the block and unique to the token. In a similarmanner, the product assembled from all the parts may also be tokenizedand tracked as it moves through the distributed manufacturing network22230 and through various entities 22226 to the customer.

In embodiments, tokenizing the part, product, or 3D printed instructionset may include wrapping access, intellectual property, licensing,ownership, financial, time-sharing, leasing, rental, usage sharingand/or other suitable rights related to the part, product, orinstruction set into a token such that the access, licensing, ownership,and/or other suitable rights are managed by one or more of the tokens.

In embodiments, the distributed manufacturing network 22230 may definepermissions and/or operations associated with the tokens. For example,the token may allow the tokenized 3D printed instruction set to beviewed, edited, copied, bought, sold, and/or licensed based onpermissions set at a time of tokenization by the distributedmanufacturing network 22230. In embodiments, the distributedmanufacturing network 22230 may provide for orchestration of adistributed manufacturing marketplace or exchange, such as where 3Dprinted instruction sets may be exchanged, such as, without limitation,through tokens that are optionally governed by smart contracts that maybe configured by a host of the distributed manufacturing exchange ormarketplace and/or by manufacturing nodes. For example, an exchange ormarketplace may host exchanges for tokenized 3D printed instructionsets, parts, products, expertise, trade secrets, insight, wheretransaction terms are pre-defined and/or configurable (such as withconfigurable smart contracts that enable various transaction models,including bid/ask models, auction models, donation models, reverseauction models, fixed price models, variable price models, contingentpricing models and others), where metadata is collected and/orrepresented about categories of distributed manufacturing marketplace orexchange, and where relevant content is presented, including marketpricing data, substantive content about additive manufacturing, contentabout providers, and the like. Such an exchange may facilitatemonetization of tokenized 3D printed instruction set knowledgerepresented in tokens.

In embodiments, a distributed manufacturing marketplace as describedherein may be integrated with or within another exchange, such as adomain-specific exchange, a geography-specific exchange, or the like,where the distributed manufacturing marketplace may be configured toaddress the subject matter of the other exchange, such as: to accountfor changes in the other exchange in the models and algorithms used inthe distributed manufacturing marketplace (e.g., pricing models,predictive models, control systems, and others) to the extent that theyimpact supply, demand, pricing, volumes, operational factors, and otherfactors; to provide via distributed manufacturing units a set of itemsand/or a set of data that may be used by the other exchange (such as byproviding products that can be exchanged in the other exchange, byproviding data sets, analytic measures, or the like that may inform theoperation of the other exchange and the like); to provide for resourcesharing between the distributed manufacturing marketplace and the otherexchange (such as to enable shared computation, shared data storage,shared network resources, shared security resources, shared physicallocation, and the like); and/or to provide for integrated coordinationof the distributed manufacturing marketplace and the other exchange.Shared resource utilization may include embedding a set of services ofthe other exchange in one or more additive manufacturing units, such asto render it a hybrid of an additive manufacturing unit and a unitenabling another exchange. The other exchange may be a product exchange(such as an e-commerce marketplace, an auction marketplace, or thelike), a stock exchange, a commodities exchange, a derivatives exchange,a futures exchange, an advertising exchange, an energy exchange, arenewable energy credits exchange, a knowledge exchange, acryptocurrency exchange, a bonds exchange, a currency exchange, aprecious metals exchange, a petroleum exchange, an exchange for goods,an exchange for services, an exchange for legal rights (such asintellectual property, real property, likeness, publicity rights,privacy rights, or others), or any of a wide variety of others. This mayinclude integration by APIs, connectors, ports, brokers, and otherinterfaces, as well as integration by extraction, transformation andloading (ETL) technologies, smart contracts, wrappers, containers, orother capabilities.

In embodiments, the digital twin system 22314 may be configured topresent a simulation of a marketplace, an exchange, a product, a seller,a buyer, a transaction, or a combination thereof via a marketplacedigital twin. The digital twin or replica may be a two-dimensional orthree-dimensional simulation of a marketplace, an exchange, a product, aseller, a buyer, a transaction, and the like. The digital twin may beviewable on a computer monitor, a television screen, a three-dimensionaldisplay, a virtual-reality display and/or headset, an augmented realitydisplay such as AR goggles or glasses, and the like. The digital twinmay be configured to be manipulated by one or more users of theautonomous additive manufacturing platform 22210. Manipulation by a usermay allow the user to view one or more portions of the digital twin ingreater or lesser detail. In embodiments, the digital twin system 22314may be configured such that the digital twin may simulate one or morepotential future states of a marketplace, an exchange, a product, aseller, a buyer, a transaction, etc. The digital twin may simulate theone or more potential future states of a marketplace, an exchange, aproduct, a seller, a buyer, a transaction, etc. based on simulationparameters provided by the user. Examples of simulation parametersinclude a progression of a period of time, potential actions by partiessuch as buyers or sellers, increases in supply and/or demand ofproducts, resources, etc., changes in government regulations, and anyother suitable parameters.

In embodiments, the autonomous additive manufacturing platform 22210 mayimplement gamification in the distributed manufacturing network 22230 byawarding points to various entities for performing tasks desirable tooperation of the distributed manufacturing network 22230. For example,points may be awarded for trading parts or products of a particular typeand/or within a particular region. Entities who have been awarded pointsmay compete with one another, and digital and/or physical prizes may beawarded to entities who have achieved one or more point thresholdsand/or have ranked above one or more other entities on a pointsleaderboard.

In embodiments, the scoring system 22734 can rate the one or moremanufacturing nodes or 3D printers in the distributed manufacturingnetwork 22230 based on a customer satisfaction score for meetingcustomer requirements. In embodiments, the score may form another basisfor matching customers to manufacturing nodes or 3D printers.

In embodiments, the scoring system 22734 crowdsources the customersatisfaction score from multiple entities in the distributedmanufacturing network 22230. Examples of crowd sources includecertifying entities, domain experts, customers, manufacturers,wholesalers, and any other suitable party.

In embodiments, certifying entities or domain experts may certify one ormore 3D printed parts as being good quality, accurate, and/or reliable.In embodiments, customers may review and certify one or more 3D printedparts or products, such as to indicate that the part or product is inworking order and/or of expected quality. In embodiments, manufacturersand/or wholesalers may sign an instance of 3D printed instruction set,such as by applying a serial number to a piece of a 3D printedinstruction set before it is transmittable to a customer.Certifications, reviews, signatures, and/or any other validation indiciamade by crowd sources may be recorded in the distributed ledger, such asby adding one or more new blocks to the distributed ledger that indicatethe certification, review, signature, or other validation indicia.

In embodiments, the autonomous additive manufacturing platform 22210utilizes a system for learning on a training set of outcomes,parameters, and data collected from data sources associated with thedistributed manufacturing network 22230 to train models in theartificial intelligence system 22312 to predict and manage productdemand from one or more customers of the distributed manufacturingnetwork 22230.

In embodiments, the autonomous additive manufacturing platform 22210utilizes a system for learning on a training set of outcomes,parameters, and data collected from data sources associated with thedistributed manufacturing network 22230 to train models in theartificial intelligence system 22312 to predict and manage materialsupply.

In embodiments, the autonomous additive manufacturing platform 22210utilizes a system for learning on a training set of outcomes,parameters, and data collected from data sources associated with thedistributed manufacturing network 22230 to train models in theartificial intelligence system 22312 to optimize production capacity fora distributed manufacturing network enabled by the autonomous additivemanufacturing platform.

In embodiments, the autonomous additive manufacturing platform 22210utilizes a system for learning on a training set of outcomes,parameters, and data collected from data sources associated with thedistributed manufacturing network 22230 to train models in theartificial intelligence system 22312 to schedule across multipleproduction processes, printers, manufacturing nodes, and to recalibrateschedules dynamically based on changes in real-time production andpriority data.

In embodiments, the autonomous additive manufacturing platform 22210 mayutilize a distributed ledger to manage a set of permission keys thatprovide access to one or more instances of the 3D printing instructionset 23002 and/or services associated with the distributed manufacturingnetwork 22230.

In embodiments, the distributed ledger provides provable access to the3D printing instruction set 23002, such as by one or more cryptographicproofs and/or techniques.

In embodiments, the distributed ledger may provide provable access tothe 3D printing instruction set 23002 by one or more zero-knowledgeproof techniques.

In embodiments, the autonomous additive manufacturing platform 22210 maymanage the distributed ledger to facilitate cooperation and/orcollaboration between two or more entities with regard to one or moreinstances of the 3D printing instruction set 23002.

In embodiments, a trusted authority (e.g., the autonomous additivemanufacturing platform 22210 or another suitable authority) may issueprivate key and public key pairs to each registered user of thedistributed manufacturing network 22230. The private key and public keypairs may be used to encrypt and decrypt data (e.g., messages, files,documents, etc.) and/or to perform operations with respect to thedistributed ledger.

In embodiments, the autonomous additive manufacturing platform 22210 oranother suitable authority may provide two or more levels of access tousers.

In embodiments, the autonomous additive manufacturing platform 22210 maydefine one or more classes of users, where each of the classes of usersis granted a respective level of access.

In embodiments, the autonomous additive manufacturing platform 22210 mayissue one or more access keys to one or more classes of users, where theone or more access keys each correspond to a respective level of access,thereby providing users of different levels of access via theirrespective issued access keys.

In embodiments, possession of certain access keys may be used todetermine a level of access to the distributed ledger. For example, afirst class of users may be granted full viewing access of a block,while a second class of users may be granted both viewing access ofblocks and an ability to verify and/or certify one or more instances oftransactions contained within a block, and a third class of users may begranted viewing access of blocks, an ability to verify and/or certifyone or more instances of transactions contained within a block, and anability to modify the one or more instances of transactions containedwithin the block. In some embodiments, a class of users may be verifiedas being a legitimate user of the distributed ledger in one or moreroles and allowed related permissions with respect to the distributedledger and content stored therein.

In embodiments, the distributed manufacturing network 22230 mayestablish a whitelist of trusted parties and/or devices, a blacklist ofuntrusted parties and/or devices, or a combination thereof for managingaccess.

In embodiments, the additive manufacturing platform 22210 may beconfigured to create customized products for shoppers (i.e., customers)in or traveling to a retail environment. The customized products may beprinted at the retail environment by the additive manufacturing unit22202, thereby attracting customers to the retail environment. Thecustomized products may include one or both of ornamental designs andfunctional designs. The ornamental designs may be configured to have oneor more aesthetic elements that are customized according to a profile ofthe customer. The functional designs may be configured to have one ormore functional features that are customized according to a profile ofthe customer. For example, the additive manufacturing platform may usecustomer profile information such as location data and/or search data todetermine that a customer will be visiting the retail environment. Upondetermining that the customer will be visiting the retail environment,the additive manufacturing platform may use information indicative ofaesthetic and/or functional desires of the customer to design acustomized product for the customer. The additive manufacturing unit22202 may manufacture the customized product such that the customizedproduct may be purchased by the customer from the retail environment.The customized product may be a product customized to fit the physiologyof the customer. For example, the customized product may be a case for acellular phone designed to fit a hand of the customer based on datarelated to the shape and/or size of the hand of the customer.

In embodiments, the additive manufacturing platform 22210 may beconfigured to create product samples tailored to shoppers. The additivemanufacturing platform 22210 may use data from the customer profile todetermine one or more types of product samples that may appeal to thecustomer. The additive manufacturing unit 22202 may print the productsamples that appeal to the customer prior to and/or during visitation tothe retail environment by the customer. The product samples may include,for example, material samples, fabric samples, food samples, or anyother suitable type of product sample.

In embodiments, the additive manufacturing platform 22210 may beconfigured to use images, text, and/or videos related to the customer tobuild the customer profile. The images, text, and/or videos may besourced from one or more of web crawlers, social media feeds, publicdatabases, and the like.

In embodiments, the additive manufacturing platform 22210 may includethe AI system 22312 configured to perform AI and/or machine learningtasks related to functions of the additive manufacturing platform. TheAI system 22312 may be configured to at least partially design thecustomized products for shoppers. The AI system 22312 may use one ormore machine learned models 22313 to analyze the customer profile anddetermine one or more customized products or features thereof that wouldbe desirable to the customer. The AI system 22312 may use one or moremachine learned models 22313 to analyze sources of images, text, and/orvideos to build the customer profile. The machine learned models 22313may be configured to allow the AI system 22312 to determine types ofimages, text, and/or videos that are more or less valuable and/oreffective to build the customer profile. The AI system 22312 may use oneor more machine learned models 22313 to determine types of customdesigns that may be more or less desirable to the customer.

In embodiments, the additive manufacturing platform 22210 may beconfigured to produce out-of-stock and/or low-stock products on-site atthe retail environment. The platform may receive data related to amountsof stock of products of the retail environment. The platform maydetermine that one or more products are out of stock and/or may becomeout of stock. The AI system 22312 may be configured to determine the outof stock products. Upon determining that one or more products are out ofstock and/or may become out of stock, the platform may, by using theadditive manufacturing unit 22202, produce more of the products.

In embodiments, the additive manufacturing platform 22210 may beconfigured to produce infrastructure for the retail environment. Theinfrastructure may be new infrastructure and/or replacementinfrastructure. The infrastructure may be produced via the additivemanufacturing unit 22202. Examples of infrastructure include pallets,storage racks, display environments, signs, packages, tags, escalatorparts, elevator parts, and the like. The additive manufacturing platform22210 may be configured to automatically determine infrastructure needsof the retail environment. The AI system 22312 may be configured to usea machine learned model to determine and/or predict infrastructure needsof the retail environment.

In embodiments, the additive manufacturing platform may be configured tocreate customized products for shoppers (i.e., customers) in ortraveling to a retail environment. The customized products may beprinted at the retail environment by a 3D printing device, therebyattracting customers to the retail environment. The customized productsmay include one or both of ornamental designs and functional designs.The ornamental designs may be configured to have one or more aestheticelements that are customized according to a profile of the customer. Thefunctional designs may be configured to have one or more functionalfeatures that are customized according to a profile of the customer. Forexample, the additive manufacturing platform may use customer profileinformation such as location data and/or search data to determine that acustomer will be visiting the retail environment. Upon determining thatthe customer will be visiting the retail environment, the additivemanufacturing platform may use information indicative of aestheticand/or functional desires of the customer to design a customized productfor the customer. The 3D printing device may manufacture the customizedproduct such that the customized product may be purchased by thecustomer from the retail environment. The customized product may be aproduct customized to fit the physiology of the customer. For example,the customized product may be a case for a cellular phone designed tofit a hand of the customer based on data related to the shape and/orsize of the hand of the customer.

In embodiments, the additive manufacturing platform may be configured tocreate product samples tailored to shoppers. The additive manufacturingplatform may use data from the customer profile to determine one or moretypes of product samples that may appeal to the customer. The 3Dprinting device may print the product samples that appeal to thecustomer prior to and/or during visitation to the retail environment bythe customer. The product samples may include, for example, materialsamples, fabric samples, food samples, or any other suitable type ofproduct sample.

In embodiments, the additive manufacturing platform may be configured touse images, text, audio, and/or videos related to the customer to buildthe customer profile. The images, text, audio, and/or videos may besourced from one or more of web crawlers, social media feeds, publicdatabases, and the like.

In embodiments, the additive manufacturing platform may include an AIsystem configured to perform AI and/or machine learning tasks related tofunctions of the additive manufacturing platform. The AI system may beconfigured to at least partially design the customized products forshoppers. The AI system may use one or more machine learned models toanalyze the customer profile and determine one or more customizedproducts or features thereof that would be desirable to the customer.The AI system may use one or more machine learned models to analyzesources of images, text, and/or videos to build the customer profile.The machine learned models may be configured to allow the AI system todetermine types of images, text, and/or videos that are more or lessvaluable and/or effective to build the customer profile. The AI systemmay use one or more machine learned models to determine types of customdesigns that may be more or less desirable to the customer.

In embodiments, the additive manufacturing platform may be configured toproduce out-of-stock and/or low-stock products on-site at the retailenvironment. The platform may receive data related to amounts of stockof products of the retail environment. The platform may determine thatone or more products are out of stock and/or may become out of stock.The AI system may be configured to determine restocking needs. Upondetermining that one or more products are out of stock and/or may becomeout of stock, the platform may, by the 3D printing device, produce moreof the products.

In embodiments, the additive manufacturing platform may be configured toproduce infrastructure for the retail environment. The infrastructuremay be new infrastructure and/or replacement infrastructure. Theinfrastructure may be produced via the 3D printing device. Examples ofinfrastructure include pallets, storage racks, display environments,signs, packages, tags, escalator parts, elevator parts, and the like.The additive manufacturing platform may be configured to automaticallydetermine infrastructure needs of the retail environment. The AI systemmay be configured to use a machine learned model to determine and/orpredict infrastructure needs of the retail environment.

In embodiments, an additive manufacturing platform 22210, such as thatassociated with a transaction enablement platform or other network, maybe designed, prepared, configured, and/or deployed to support thedesign, development, manufacture, and distribution of health and medicaldevices, components, parts, equipment, and the like. For example, inconnection with a patient consultation with a medical or health servicesprovider, an additive manufacturing unit may be designated to supportthe consultation, such as a mobile additive manufacturing unit 22202and/or a unit located in sufficiently close proximity to the medical orhealth services provider to facilitate rapid delivery of medical andhealthcare hard goods and devices produced by the additive manufacturingunit 22202.

Based on the nature of the healthcare consultation (e.g., medicalspecialty and its corresponding devices, equipment and parts), theadditive manufacturing unit 22202 may be equipped with appropriatematerials, such as a combination of metal and/or plastic printingmaterials, or other printing materials, that are suitable to print arange of possible health and medical devices, components, parts,equipment and the like to support healthcare providers and theirpatients.

In embodiments, the platform 22210 may take inputs from or related to ahealthcare consultation, such as inputs indicating a needed medicaldevice or part (e.g., technical specifications, CAD designs, and thelike); inputs indicating patient-specific data (e.g., clinical criteria,measurements such as sizing, weight, height, girth, circumference, orthe like); and inputs provided by medical and health service providersor other third parties, such as device specifications, requirements, andthe like (e.g., limitations on device size, such as thickness,requirements related to load- or stress-bearing minimums, or some othercriterion).

In embodiments, the platform 22210 may process the inputs from aplurality of sources including, but not limited to, medical records(e.g., patient measurements, material allergies, use of other relatedmedical devices, and the like), device specification data (e.g.,manufacturing specifications from the party(ies) holding rights to thedevice, part, or other object to be manufactured), patient-input data(e.g., aesthetic preferences such as color of the device),healthcare-provider-input data (e.g., medical office branding), or someother input. An artificial intelligence system (such as a roboticprocess automation system trained on a training set of expert medicaldevices or other data) may, in some embodiments, determine a recommendedaction, prototype, device, which in embodiments may involve productionof a device and/or a component of a device. The additive manufacturingplatform 22210 may, in some such embodiments, automatically determine(such as using an artificial intelligence system, such as roboticprocess automation trained on an expert data set) whether a medicaldevice is readily available from a manufacturer (including a device thatis currently in stock and/or on order) and/or whether an additivemanufacturing system should produce the device, such as to meet animmediate patient need, to save costs, or the like. Similarly, theadditive manufacturing platform may, in some embodiments, using similarsystems, automatically determine that an element should be additivelymanufactured to facilitate repair, such as where a complementarycomponent may be generated to replace a worn or absent element of amedical device.

In an example embodiment, an outpatient may visit an orthopedic officefor a healthcare consultation relating to a knee injury. Given theprobability that the patient will require some form of external kneesupport from a medical device, such as a brace, an attending physicianin advance of the healthcare consultation may access a user interface,dashboard, or some other user portal to the additive manufacturingplatform to determine the availability of knee braces and other medicaldevices to be manufactured by the additive manufacturing platform (e.g.,to confirm that the additive manufacturing platform 22210 has availabledesigns, CAD renderings and/or other specifications that will enable itto produce the needed medical device). If the additive manufacturingplatform 22210 has such device specifications, the attending physician(or other personnel associated with the upcoming patient healthcareconsultation) may place would-be wanted device designs in a queue hold,reserve, or some other means of recording potential interest in theirmanufacture. By having such recording, upon meeting with the patient,the attending physician (or other personnel associated with the upcomingpatient healthcare consultation) may be able to present device optionsto the patient to select from using the user interface, dashboard, orsome other user portal to the additive manufacturing platform. If aneeded medical device is not currently associated with the additivemanufacturing platform, this may cause the platform to automaticallysend out a request for corresponding device specifications, design, andother data that are needed to manufacture the device, component, orpart. Once such corresponding device specifications, design, and otherdata are located, an alert may be provided back to the attendingphysician (or other personnel associated with the upcoming patienthealthcare consultation) indicating that there are proposedproducts/devices for review that appear to conform with the listeddevice requirements. As part of the review of each availablespecification, design, or other data that is needed to manufacture thedevice, contract terms relating to costs, warranty, and otherconsiderations may be presented for review. Contract terms andcontractual relationships between users of the additive manufacturingplatform and third party holders of rights related to devicemanufacturing may be coordinated using smart contracts, as describedherein. Before, during, or after the patient's healthcare consultation,a medical device design may be selected and input for manufacture to theadditive manufacturing platform. As part of the order, data relating tothe specific patient may be submitted to the additive manufacturingplatform, such as data regarding the circumference of the patientslower-leg, knee, and upper-leg that are needed to make an appropriatelysized brace. Such information may be manually input to the additivemanufacturing platform or may be automatically input to the additivemanufacturing platform by transfer of data from a data source externalto the additive manufacturing platform 22210, such as an electronicmedical record, or some other data source storing data that is relevantto the device characteristics. Additional, preferential data may also beprovided, such as a child wanting images of koala bears engraved in theexterior of their brace or a businessperson wanting the brace to be aparticular color to better match her skin tone and/or business suitcolor to make the brace less apparent. The user interface, dashboard, orsome other user portal to the additive manufacturing platform may enableinteraction with the additive manufacturing platform that allows a user,like a patient, to see different prototypes and aesthetic flourishes ofthe to-be manufactured device prior to submitting a job to be built.Upon finalizing the design specifications, the additive manufacturingplatform 22210 may proceed with producing the device and/or a componentor part of the device, while the patient's healthcare consultationproceeds, or, this manufacture may be finalized following theconsultation, and the device automatically sent to the patient and/orhealthcare provider based on contact data input to the additivemanufacturing platform 22210 at the time of placing the order.

In embodiments, the additive manufacturing platform 22210, such as thatassociated with a transaction enablement platform network, may beprepared, configured, and/or deployed to support printing of customizedand/or personalized hotel textiles for a set of hotel guests. In oneexample, in connection with an upcoming hotel guest visit, the additivemanufacturing unit 22202 may be designated for support, such as a mobileadditive manufacturing unit 22202 and/or a unit located in sufficientlyclose proximity to the hotel to facilitate rapid delivery of itemsproduced by the additive manufacturing unit 22202. In embodiments,textiles that may be customized and/or personalized may include bedding,sheets, towels, robes, pillows, blankets, curtains, furniture, and thelike.

In embodiments, the additive manufacturing unit 22202 may be equippedwith appropriate materials, such as a combination of fabrics and otherprinting materials, that are suitable to print a range of possibletextiles or other elements to support the hotel visit. In embodiments,fabrics may include, but are not limited to, canvas, cashmere, chenille,chiffon, cotton, crepe, damask, georgette, gingham, jersey, lace,leather, linen, merino wool, modal, muslin, organza, polyester, satin,silk, spandex, suede, taffeta, toile, tweed, twill, velvet, viscose, andmany others.

In embodiments, the additive manufacturing platform 22210 may takeinputs related to the upcoming hotel visit, such as inputs indicatingthe type(s) of item to print (e.g., pillows, bedding, towels, and thelike); inputs indicating fabric type (such as cotton, silk, or thelike); inputs indicating item size (such as to fit a queen bed or kingbed); and inputs captured by cameras, microphones, data collectors,sensors, and other information sources associated with the upcominghotel visit. For example, a hotel employee may capture informationrelated to hotel guest preferences. In embodiments, the additivemanufacturing platform 22210 may process the inputs, such as using theartificial intelligence system 22312 (such as a robotic processautomation system trained on a training set of expert service visitdata), to determine a recommended action, which in embodiments mayinvolve printing of a textile. The additive manufacturing platform 22210may, in some such embodiments, automatically determine (such as using anartificial intelligence system 22312, such as robotic process automationtrained on an expert data set) whether the additive manufacturing unit22202 should produce the textile.

In any such embodiment that recommends or configures instructions foradditive manufacturing, the additive manufacturing platform 22210 maydiscover available materials/fabrics, configure instructions, initiateadditive manufacturing, and provide updates to a hotel employee, such asupdates as to when an element will be ready to use.

In embodiments, the additive manufacturing platform 22210, such asthrough a trained AI agent, may automatically configure and schedule aset of jobs across a set of additive manufacturing units 22202 withawareness of the status of other relevant entities involved in otherworkflows, such as what other work is being done (e.g., to allow forappropriate sequencing of additive manufacturing outputs that align withoverall workflows), the priority of the printing job (e.g., whether itrelates to a loyal hotel guest), or other factors. In embodiments,optimization of workflows across a set of additive manufacturingentities may occur by having the artificial intelligence system 22312undertake a set of simulations, such as simulations involvingalternative scheduling sequences, design configurations, alternativeoutput types, and the like. In embodiments, simulations may includesequences involving additive manufacturing and other manufacturingentities (such as subtractive manufacturing entities that cut, dye, orthe like and/or finishing entities that sew, configure, add hotel guestinitials, or the like), including handoffs between sets of differentmanufacturing entity types, such as where handoffs are handled byrobotic handling systems. In embodiments, a set of digital twins mayrepresent attributes and capabilities of the various manufacturingsystems, various handling systems (robotic systems, arms, conveyors, andthe like, as well as human workforce), and/or the surroundingenvironment (such as a hotel, a manufacturing facility, or the like).

In embodiments, the additive manufacturing platform 22210 such as thatassociated with a transaction enablement platform network may beprepared, configured, and/or deployed to support restaurant operations.For example, in connection with a customer reservation at a restaurant,the additive manufacturing unit 22202 may be designated to support thecustomer reservation, such as a table-side additive manufacturing unit22202 and/or a portable unit to facilitate direct-to-table delivery ofitems produced by the additive manufacturing unit 22202.

Based on the nature of the reservation (e.g., special dietaryrequirements, accessibility requirements, occasion of the reservation)and the services and supplies available at the restaurant, the additivemanufacturing unit 22202 may be equipped with appropriate materials,such as a combination of food grade service/storage materials and otherprinting materials, that are suitable to print a range of possibleservice items, specialized flatware, customizedcommemorative/celebration items, or other elements to support thereservation. In embodiments, the additive manufacturing platform 22210may take inputs from or related to the reservation, such as inputsindicating time of day, size of the party, special requests, affiliationwith principals of the restaurant, loyalty participation, and the like;inputs indicating service support capabilities at the restaurant andoptions for timely access to locally available service supportmaterial/equipment (such as a status of ovens, cook tops, food storage,meal prep material, customizable service items, or the like); and inputscaptured by cameras, microphones, data collectors, sensors, and otherinformation sources associated with the reservation, including selectinput capture device(s) associated with one or more participants in thereservation (e.g., a personal mobile phone with image capture features).For example, a hostess station camera may capture a set of photos of theparticipants, such as images of the reservation participant(s) facesthat are suitable for generation of a 3D data set for additivemanufacturing printing use.

In embodiments, the additive manufacturing platform 22210 may processthe inputs, such as by using the artificial intelligence system 22312,to determine a recommended action for servicing participants in thereservation, which in embodiments may involve use of a service item,such as a standard service item adapted to meet a service requirement ofthe reservation, such as a customized serving tray with separatedcompartments for each participant in the reservation, an item offlatware and/or serving spoon adapted for use by a person without anormal appendage, and the like. The additive manufacturing platform22210 may, in some such embodiments, automatically determine, such as byusing the artificial intelligence system 22312 trained on an expert dataset and the like, whether a suitable service item is readily availableand/or whether use of an additive manufacturing system to produce theservice item(s) can reduce delay to save costs or the like. Similarly,the additive manufacturing platform 22210 may, in some embodiments,using similar systems, automatically determine that an element should beadditively manufactured to facilitate use of additional kitchenequipment, such as cook tops to ensure timely meal service for thereservation, such as where a complementary component may be generated toreplace a worn or absent component, such as a gas setting knob on a gasrange regulator.

In embodiments, automatic determination may occur using a machine visionsystem that captures a set of facial images of reservation participantsand produces an instruction set for additively manufacturing acomplementary service item, such as a drinking glass that matches thefacial image. In any such embodiment that recommends or configuresinstructions for additive manufacturing, the additive manufacturingplatform 22210 may discover available additive manufacturing units 22202(e.g., a drinking glass additive manufacturing unit on the restaurantpremises), configure compatible instructions, initiate additivemanufacturing, and provide updates to the service staff, such as updatesas to when the custom printed drinking glass will be ready to use. Inembodiments, the additive manufacturing platform 22210, such as througha trained AI agent, may automatically configure and schedule a set ofjobs across a set of additive manufacturing units 22202 (drinking glassadditive manufacturing units, kitchen equipment parts additivemanufacturing units, takeaway/takeout food storage systems additivemanufacturing units, and the like) with awareness of the status of otherrelevant reservations at the restaurant and other kitchens/serviceworkflows, such as the timing of food preparation/meal courses (e.g., toallow de-prioritization of additive manufacturing jobs that are toproduce reservation-related service items that won't be used immediatelyupon the start of the reservation), what other additive manufacturingwork is being done for other reservations (e.g., to allow forappropriate sequencing of additive manufacturing outputs that align withoverall kitchen workflows, meal service, and the like), the cost (bothdirect and indirect) of delays in additive manufacturing element access(e.g., poor reviews, discounted charges, lower service tip, freefood/beverage items as compensation for delays, and the like), or otherfactors.

In embodiments, restaurant service items that may be enhanced and/orproduce through additive manufacturing techniques include, withoutlimitation, takeout/away containers constructed to meet individual fooditem needs, such as keeping salad cool, keeping a hot meal warm, orkeeping a serving of French fries crispy, containers shaped to meet foodservice item size/shape (e.g., a triangle sized container for a slice ofpie, round for a pancake, oblong/square for a sandwich item), and thelike. In embodiments, user-specific flatware, such as age range-specificflatware suitable for use by a baby just learning to use a fork andspoon or a child honing her skill with a knife, an unconventionalflatware item based on user preferences (explicitly expressed inassociation with the reservation or implicitly derived from usercontext/imagery), and the like. Further in embodiments, table andservice items, such as mugs, coasters, chargers, plates, and the likemay be produced to meet reservation aspects, such as a logo suppliedwith the reservation, an occasion-specific design/embellishmentrecommended during the reservation process, and the like. Inembodiments, optimization of workflows across a set of additivemanufacturing entities/units may occur by having an artificialintelligence system undertake a set of simulations, such as simulationsinvolving alternative food preparation and/or reservation sequences,design configurations, alternative output/material types, and the like.

In embodiments, reservation service items that rely on a mix of additivemanufacturing materials, such as paper-like material and thermalinsulation structures, may provide performance benefits oversingle-material items, such as lower thermal transfer from an interiorof a service item (e.g., a custom printed drinking glass) to an exteriorof the item (e.g., for maintaining the interior temperature andimproving comfort of a user holding the glass).

In embodiments, the additive manufacturing platform 22210, such as thatassociated with a transaction enablement platform network, may beprepared, configured, and/or deployed to support printing ofpersonalized food at campuses in universities and/or enterprises. In oneexample, an additive manufacturing unit 22202 may be designated toprovide ethnic and personalized food to students and workers on the go.In embodiments, the additive manufacturing unit 22202 may be equippedwith materials, such as a combination of ingredients and other printingmaterials, that are suitable to print a range of possible food items tosupport the students or workers. For example, pizza making may beautomated by the additive manufacturing unit 22202 and a multi-nozzleprint head may deposit dough, sauce, and cheese along with personalizedchoice of pizza toppings. Similarly, desserts, chocolates, cakes,pastries, even edible plates, utensils and cutlery, and the like may beprinted by the additive manufacturing unit 22202.

In embodiments, the additive manufacturing platform 22210 may takeinputs from or related to the customer, such as inputs indicating thetype(s) of food items to print (e.g., pizza, pasta, desserts, and thelike); inputs indicating taste preferences (such as spicy, sweet, or thelike); inputs indicating aesthetic preferences (such as texture, color,or the like); inputs indicating food item size (such as small, medium,or large); inputs indicating nutritional requirements (proteins,carbohydrates, fats, vitamins, minerals etc.) inputs indicating healthneeds (such as allergies or the like); inputs captured by cameras,microphones, data collectors, sensors, and other information sourcesassociated with the upcoming campus visit; or some other input type. Forexample, information related to customer biological information may becaptured to determine that the customer does not have any seafoodallergies. In embodiments, the additive manufacturing platform 22210 mayprocess the inputs, such as using the artificial intelligence system22312 (such as a robotic process automation system trained on a trainingset of expert service visit data), to determine a recommended action,which, in embodiments, may involve printing of, for example, a customsushi that optimizes ingredients that fulfill the nutritionalrequirements of the customer.

In embodiments, the additive manufacturing unit 22202 may print takeoutcontainers to meet individual food item needs, such as keeping saladcool, keeping a hot meal warm, keeping a serving of French fries crispy,containers shaped to meet food service item size/shape, and the like.

In embodiments, the food items may be printed at a mobile additivemanufacturing unit 22202 near or at the point of use on an on-demandbasis thereby reducing food inventory and the cost involved with storageand transportation.

In embodiments, the additive manufacturing platform 22210, such asthrough a trained AI agent, may automatically configure and schedule aset of jobs across a set of additive manufacturing units 22202 (e.g.,units creating food, desserts, plates, utensils, cutlery, kitchenequipment, and the like) with awareness of the status of other relevantentities involved in other workflows, such as what other work is beingdone (e.g., to allow for appropriate sequencing of additivemanufacturing outputs that align with overall workflows), the priorityof the printing job (e.g., based on the timing of a customer order), orother factors. In embodiments, optimization of workflows across a set ofadditive manufacturing entities may occur by having an artificialintelligence system undertake a set of simulations, such as simulationsinvolving alternative scheduling sequences, design configurations,alternative output types, and the like. In embodiments, simulations mayinclude sequences involving additive manufacturing and othermanufacturing entities (such as subtractive manufacturing entities thatcut, drill, or the like) and/or finishing entities (that decorate,plate, garnish, arrange, glaze, or the like), including handoffs betweensets of different manufacturing entity types, such as where handoffs arehandled by robotic handling systems.

In embodiments, the additive manufacturing platform 22210 may beconfigured as a fixed or mobile system that operates individually or aspart of a network, to combine live inputs, library data, personal data,licensed data, and so forth to autonomously design and produce uniqueparts associated with a live event, for example, personalized mementos,sample products, limited edition artwork, and the like.

In embodiments, the additive manufacturing platform 22210 may acquirereal-time or personalized input from the user or venue using 3D scanningsuch as laser or white light scanners, image recognition, photography,publicly available data, etc. and combine and process the informationwith existing public or licensed part and data libraries to produce acombined 3D printable dataset and finished products that may bedelivered as the customer waits or at a later time to a home, business,or venue seat.

In embodiments, the additive manufacturing platform 22210, such as thatassociated with a transaction enablement platform network, may beconfigured and deployed by first responders to support first responderevents. For example, in connection with a first responder request, theadditive manufacturing units 22202 may be designated to support designand print custom components, parts, equipment, medical devices,accessories, and the like on an on-demand real time basis. Some examplesof equipment that may be printed include Personal Protective Equipment(PPE), face shields, goggles or medical glasses, protective eyewear,boots, surgical hoods, earplugs, valves, nozzles, helmets, body shields,extrication tools, and the like.

In embodiments, the equipment may be printed near or at the point of useon a need basis. For example, eyewear, earplugs, helmets, and/or bootsmay be custom printed based on the patient measurement. Similarly,equipment including respirators, ventilators, custom valves, and nozzlesmay be printed at a mobile additive manufacturing platform based onimmediate patient needs and delivered at the point of care.

In embodiments, the additive manufacturing platform 22210 mayautomatically determine (such as using the artificial intelligencesystem 22312 trained on an expert data set) that one or more partsshould be additively manufactured to facilitate repair, such as where acomplementary part may be generated to replace a worn or absent elementof a first responder equipment or device. The additive manufacturingplatform 22210 may then process the inputs, such as by using theartificial intelligence system 22312, to determine a recommended actionfor servicing the repair request.

In embodiments, a set of additive manufacturing units 22202 may beprovided as shared resources for multiple tenants of a building, such asa commercial real estate building, where the additive manufacturingunits 22202 are integrated with other building resources, such asnetworking resources (e.g., RF, cellular, Wi-Fi, fiber optic, and otherresources), computational resources (e.g., data storage resources, edge,and cloud computational resources), IoT resources (e.g., cameras,sensors, and the like), and others, such that the capabilities of theadditive manufacturing units 22202 may be accessed by tenants accordingto terms and conditions of a lease (which, in embodiments, may beembodied, at least in part, as a smart contract that operates on datafrom or about the additive manufacturing units 22202). In embodiments,the additive manufacturing platform 22210 may include, link to, orintegrate with a set of devices, systems, services, and other resourcesin a backbone for building, campus, or the like, including a set ofnetwork backbone and/or connectivity resources (such as 5G and othercellular network devices and infrastructure, such as switches, accesspoints, gateways, routers, wireless mesh network systems, satellitesystems, Wifi systems, long-range RF systems (such as LORA), Zigbee,Bluetooth, and other wireless systems, as well as fixed network systems,such as fiber access gateways and other systems, modems, and othergateway devices for cable, ethernet, digital subscriber line, analogtelephone line, and other wired networking systems, each using any of awide range of protocols, such as ethernet, TCP/IP, UDP, and manyothers). Shared connectivity resources may include resources forInternet connectivity (such as wireless internet service provider (WISP)resources and fixed ISP connectivity), cellular connectivity (e.g.,shared 5G), mesh network connectivity, and many others. In embodiments,the additive manufacturing platform 22210 may include, link to, orintegrate with a set of shared data storage resources, such as ablockchain dedicated to the building, campus, or the like, a distributedledger, a database or other data repository, a distributed memory systemusing memory of devices and systems that provide the building's ITinfrastructure, and others. In embodiments, the additive manufacturingunits 22202 and other shared resources may be provisioned, such as by ahost or a trained intelligent agent operating on behalf of the host, toenable rapid customization and fulfillment of needs of tenants, such astenants of a building, campus, city, or the like, including operationalneeds (such as for spare parts, products, tools, accessories, supplies,replacement parts, and the like, among many others), and many others.Among many examples, additive manufacturing units 22202 may produceelements needed for specialized tenants, such as personal protectiveequipment, ventilators, wearable items, tools, or the like, as well aselements needed for IT infrastructure (such as connectors, plugs, andthe like, such as to fiber optic cables, Ethernet ports, and the like),and many others. In embodiments, the shared resources may be monitored,such as with various utilization tracking techniques, such as event logsof networking nodes, logs of software systems, and the like, and may beprovisioned by an automated provisioning system, including allocatingpayment responsibilities, allocating usage rights, settingprioritization of resource utilization (such as by tenant, by time, bytask, and the like), and the like. This may include automated managementby an artificial intelligence agent that is trained by a training set ofdata of expert resource managers. This may be a supervised,semi-supervised, or deep learning process and may include training onoutcomes, such as profitability outcomes, tenant feedback outcomes, usersatisfaction outcomes, security outcomes, operational outcomes, and manyothers. Resource sharing and payments may be governed and controlled bya smart contract, such as with governing rules for allocating resourcesand conditional logic determining prioritization and/or paymentresponsibilities, optionally operating on a distributed ledger of eventsinvolving the resources. In embodiments, the smart contract frameworkmay itself be a shared resource offered to tenants, such as to enablethem to offer services, share resources (such as with other tenants,including any of the resources noted herein as well as others), and thelike.

Combinations of embodiments are contemplated in yet further embodiments.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to generatehighly customized shapes, such as for compatibility with very specificsituations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to createcombinations of metals with other materials (including functionallygraded materials (FGMs) and/or graded combinations where there is nosharp boundary between material types).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility includingmultiple source materials.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility using multipleextrusion nozzles for simultaneous work on multiple areas.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility using AI tooptimize product design, manufacturing process configuration, jobscheduling, prioritization, and/or logistics.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to provideadditive manufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to integrateonboard edge intelligence and smart connectivity.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to integrateinto mobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to enrich AIwith input/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to coupleinputs, process data, and outputs with digital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to coupleprocesses with blockchains and smart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility to networkadditive manufacturing nodes in meshes and/or fleets for coordinatedoperation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having a manufacturing facility using robotsthat are able to attach to machines and then print directly onto areplacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having fused Deposition Modeling (FDM)™ a/k/aFused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having selective laser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having selective laser sintering (SLS) where alaser melts flame-retardant plastic powder that solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having direct metal laser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having fused deposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having metal extrusion where a filament or rodconsisting of polymer and heavily loaded with metal powder is extrudedthrough a nozzle (like in FDM) to form the “green” part that ispost-processed (debinded and sintered) to create a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having metal binder jetting that usesprint-heads to apply a liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having nanoparticle jetting that uses jettingof metal nanoparticles from inkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having electron beam freeform fabrication(EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having selective heat sintering using athermal printhead heat layer(s) of powdered material to render itthermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having stereo-lithography (SLA) using a UVlaser to cure a resin of liquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having digital light processing (DLP)projecting an image of a cross-section of an object into a quantity ofphotopolymer (light reactive plastic) that selectively hardens the imagearea.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having light polymerization where light causesthe polymer to harden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having inkjet type printhead deliveringliquid/colloidal binder to layers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to make unusual shapes out ofmetal (e.g., fluid handling without hoses; biomimicry for heatdissipation and/or turbulence reduction; prosthetic replacements;partial replacements) and having rotary build table deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility to create combinations of metals withother materials (including functionally graded materials (FGMs) and/orgraded combinations where there is no sharp boundary between materialtypes).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility including multiple source materials.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility using multiple extrusion nozzles forsimultaneous work on multiple areas.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility using AI to optimize product design,manufacturing process configuration, job scheduling, prioritization,and/or logistics.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility to provide additive manufacturing unitsas shared resources/“as-a-service” nodes/multi-tenant resources(including through smart contracts/blockchains).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility to integrate onboard edge intelligenceand smart connectivity.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility to enrich AI with input/source/trainingset data relevant to design factors, economic factors, quality factors,and the like customized to particular use cases, embodiments,applications, and apparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility to couple inputs, process data, andoutputs with digital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility to couple processes with blockchains andsmart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility to network additive manufacturing nodesin meshes and/or fleets for coordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving a manufacturing facility using robots that are able to attach tomachines and then print directly onto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving fused Deposition Modeling (FDM)™ a/k/a Fused FilamentFabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving selective laser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving selective laser sintering (SLS) where a laser meltsflame-retardant plastic powder that solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving direct metal laser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving fused deposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving metal extrusion where a filament or rod consisting of polymer andheavily loaded with metal powder is extruded through a nozzle (like inFDM) to form the “green” part that is post-processed (debinded andsintered) to create a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving metal binder jetting that uses print-heads to apply a liquidbinding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving nanoparticle jetting that uses jetting of metal nanoparticlesfrom inkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving electron beam freeform fabrication (EBFFF) using electron beamwelding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving selective heat sintering using a thermal printhead heat layer(s)of powdered material to render it thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving stereo-lithography (SLA) using a UV laser to cure a resin ofliquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving digital light processing (DLP) projecting an image of across-section of an object into a quantity of photopolymer (lightreactive plastic) that selectively hardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving light polymerization where light causes polymer to harden inchanging areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving inkjet type printhead delivering liquid/colloidal binder tolayers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to generate highly customizedshapes, such as for compatibility with very specific situations andhaving rotary build table deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility includingmultiple source materials.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility usingmultiple extrusion nozzles for simultaneous work on multiple areas.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility using AI tooptimize product design, manufacturing process configuration, jobscheduling, prioritization, and/or logistics.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility to provideadditive manufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility to integrateonboard edge intelligence and smart connectivity.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility to integrateinto mobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility to enrich AIwith input/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility to coupleinputs, process data, and outputs with digital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility to coupleprocesses with blockchains and smart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility to networkadditive manufacturing nodes in meshes and/or fleets for coordinatedoperation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having a manufacturing facility using robotsthat are able to attach to machines and then print directly onto areplacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having fused Deposition Modeling (FDM)™ a/k/aFused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having selective laser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having selective laser sintering (SLS) wherea laser melts flame-retardant plastic powder that solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having direct metal laser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having fused deposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having metal extrusion where a filament orrod consisting of polymer and heavily loaded with metal powder isextruded through a nozzle (like in FDM) to form the “green” part that ispost-processed (debinded and sintered) to create a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having metal binder jetting that usesprint-heads to apply a liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having nanoparticle jetting that uses jettingof metal nanoparticles from inkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having electron beam freeform fabrication(EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having selective heat sintering using athermal printhead heat layer(s) of powdered material to render itthermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having stereo-lithography (SLA) using a UVlaser to cure a resin of liquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having digital light processing (DLP)projecting an image of a cross-section of an object into a quantity ofphotopolymer (light reactive plastic) that selectively hardens the imagearea.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having light polymerization where lightcauses polymer to harden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having inkjet type printhead deliveringliquid/colloidal binder to layers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to create combinations ofmetals with other materials including functionally graded materials(FGMs) and/or graded combinations where there is no sharp boundarybetween material types and having rotary build table deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility using AI to optimizeproduct design, manufacturing process configuration, job scheduling,prioritization, and/or logistics.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility to couple inputs, processdata, and outputs with digital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility to couple processes withblockchains and smart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having a manufacturing facility using robots that are ableto attach to machines and then print directly onto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having fused Deposition Modeling (FDM)™ a/k/a FusedFilament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having selective laser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having selective laser sintering (SLS) where a laser meltsflame-retardant plastic powder that solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having direct metal laser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having fused deposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having metal extrusion where a filament or rod consistingof polymer and heavily loaded with metal powder is extruded through anozzle (like in FDM) to form the “green” part that is post-processed(debinded and sintered) to create a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having metal binder jetting that uses print-heads to applya liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having nanoparticle jetting that uses jetting of metalnanoparticles from inkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having electron beam freeform fabrication (EBFFF) usingelectron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having selective heat sintering using a thermal printheadheat layer(s) of powdered material to render it thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having stereo-lithography (SLA) using a UV laser to cure aresin of liquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having digital light processing (DLP) projecting an imageof a cross-section of an object into a quantity of photopolymer (lightreactive plastic) that selectively hardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having light polymerization where light causes polymer toharden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having inkjet type printhead delivering liquid/colloidalbinder to layers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility including multiple sourcematerials and having rotary build table deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility using AI to optimize product design,manufacturing process configuration, job scheduling, prioritization,and/or logistics.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility to provide additive manufacturing units as sharedresources/“as-a-service” nodes/multi-tenant resources (including throughsmart contracts/blockchains).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility to integrate onboard edge intelligence and smartconnectivity.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility to enrich AI with input/source/training set datarelevant to design factors, economic factors, quality factors, and thelike customized to particular use cases, embodiments, applications, andapparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility to couple inputs, process data, and outputs withdigital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility to couple processes with blockchains and smartcontracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility to network additive manufacturing nodes in meshesand/or fleets for coordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having amanufacturing facility using robots that are able to attach to machinesand then print directly onto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having fusedDeposition Modeling (FDM)™ a/k/a Fused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having selectivelaser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having selectivelaser sintering (SLS) where a laser melts flame-retardant plastic powderthat solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having direct metallaser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having fuseddeposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having metalextrusion where a filament or rod consisting of polymer and heavilyloaded with metal powder is extruded through a nozzle (like in FDM) toform the “green” part that is post-processed (debinded and sintered) tocreate a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having metal binderjetting that uses print-heads to apply a liquid binding agent ontolayers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having nanoparticlejetting that uses jetting of metal nanoparticles from inkjet nozzles insuper-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having electron beamfreeform fabrication (EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having selectiveheat sintering using a thermal printhead heat layer(s) of powderedmaterial to render it thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and havingstereo-lithography (SLA) using a UV laser to cure a resin of liquidUV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having digital lightprocessing (DLP) projecting an image of a cross-section of an objectinto a quantity of photopolymer (light reactive plastic) thatselectively hardens the image area. In embodiments, provided herein isan additive manufacturing management platform having a manufacturingfacility using multiple extrusion nozzles for simultaneous work onmultiple areas and having light polymerization where light causespolymer to harden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having inkjet typeprinthead delivering liquid/colloidal binder to layers of powderedmaterial.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using multiple extrusionnozzles for simultaneous work on multiple areas and having rotary buildtable deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having a manufacturing facility toprovide additive manufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having a manufacturing facility tointegrate onboard edge intelligence and smart connectivity.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having a manufacturing facility tointegrate into mobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having a manufacturing facility toenrich AI with input/source/training set data relevant to designfactors, economic factors, quality factors, and the like customized toparticular use cases, embodiments, applications, and apparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having a manufacturing facility tocouple inputs, process data, and outputs with digital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having a manufacturing facility tocouple processes with blockchains and smart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having a manufacturing facility tonetwork additive manufacturing nodes in meshes and/or fleets forcoordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having a manufacturing facilityusing robots that are able to attach to machines and then print directlyonto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having fused Deposition Modeling(FDM)™ a/k/a Fused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having selective laser melting(SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having selective laser sintering(SLS) where a laser melts flame-retardant plastic powder thatsolidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having direct metal laser sintering(DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having fused deposition modeling(FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having metal extrusion where afilament or rod consisting of polymer and heavily loaded with metalpowder is extruded through a nozzle (like in FDM) to form the “green”part that is post-processed (debinded and sintered) to create afully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having metal binder jetting thatuses print-heads to apply a liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having nanoparticle jetting thatuses jetting of metal nanoparticles from inkjet nozzles in super-thinlayers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having electron beam freeformfabrication (EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having selective heat sinteringusing a thermal printhead heat layer(s) of powdered material to renderit thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having stereo-lithography (SLA)using a UV laser to cure a resin of liquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having digital light processing(DLP) projecting an image of a cross-section of an object into aquantity of photopolymer (light reactive plastic) that selectivelyhardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having light polymerization wherelight causes polymer to harden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having inkjet type printheaddelivering liquid/colloidal binder to layers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using AI to optimize productdesign, manufacturing process configuration, job scheduling,prioritization, and/or logistics and having rotary build tabledeposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having a manufacturing facility to integrateonboard edge intelligence and smart connectivity.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having a manufacturing facility to integrateinto mobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having a manufacturing facility to enrich AIwith input/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having a manufacturing facility to coupleinputs, process data, and outputs with digital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having a manufacturing facility to coupleprocesses with blockchains and smart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having a manufacturing facility to networkadditive manufacturing nodes in meshes and/or fleets for coordinatedoperation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having a manufacturing facility using robotsthat are able to attach to machines and then print directly onto areplacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having fused Deposition Modeling (FDM)™ a/k/aFused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having selective laser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having selective laser sintering (SLS) wherea laser melts flame-retardant plastic powder that solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having direct metal laser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having fused deposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having metal extrusion where a filament orrod consisting of polymer and heavily loaded with metal powder isextruded through a nozzle (like in FDM) to form the “green” part that ispost-processed (debinded and sintered) to create a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having metal binder jetting that usesprint-heads to apply a liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having nanoparticle jetting that uses jettingof metal nanoparticles from inkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having electron beam freeform fabrication(EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having selective heat sintering using athermal printhead heat layer(s) of powdered material to render itthermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having stereo-lithography (SLA) using a UVlaser to cure a resin of liquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having digital light processing (DLP)projecting an image of a cross-section of an object into a quantity ofphotopolymer (light reactive plastic) that selectively hardens the imagearea.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having light polymerization where lightcauses polymer to harden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having inkjet type printhead deliveringliquid/colloidal binder to layers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to provide additivemanufacturing units as shared resources/“as-a-service”nodes/multi-tenant resources (including through smartcontracts/blockchains) and having rotary build table deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having a manufacturing facilityto integrate into mobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having a manufacturing facilityto enrich AI with input/source/training set data relevant to designfactors, economic factors, quality factors, and the like customized toparticular use cases, embodiments, applications, and apparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having a manufacturing facilityto couple inputs, process data, and outputs with digital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having a manufacturing facilityto couple processes with blockchains and smart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having a manufacturing facilityto network additive manufacturing nodes in meshes and/or fleets forcoordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having a manufacturing facilityusing robots that are able to attach to machines and then print directlyonto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having fused Deposition Modeling(FDM)™ a/k/a Fused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having selective laser melting(SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having selective laser sintering(SLS) where a laser melts flame-retardant plastic powder thatsolidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having direct metal lasersintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having fused deposition modeling(FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having metal extrusion where afilament or rod consisting of polymer and heavily loaded with metalpowder is extruded through a nozzle (like in FDM) to form the “green”part that is post-processed (debinded and sintered) to create afully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having metal binder jetting thatuses print-heads to apply a liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having nanoparticle jetting thatuses jetting of metal nanoparticles from inkjet nozzles in super-thinlayers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having electron beam freeformfabrication (EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having selective heat sinteringusing a thermal printhead heat layer(s) of powdered material to renderit thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having stereo-lithography (SLA)using a UV laser to cure a resin of liquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having digital light processing(DLP) projecting an image of a cross-section of an object into aquantity of photopolymer (light reactive plastic) that selectivelyhardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having light polymerizationwhere light causes polymer to harden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having inkjet type printheaddelivering liquid/colloidal binder to layers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate onboard edgeintelligence and smart connectivity and having rotary build tabledeposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having amanufacturing facility to enrich AI with input/source/training set datarelevant to design factors, economic factors, quality factors, and thelike customized to particular use cases, embodiments, applications, andapparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having amanufacturing facility to couple inputs, process data, and outputs withdigital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having amanufacturing facility to couple processes with blockchains and smartcontracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having amanufacturing facility to network additive manufacturing nodes in meshesand/or fleets for coordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having amanufacturing facility using robots that are able to attach to machinesand then print directly onto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having fusedDeposition Modeling (FDM)™ a/k/a Fused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having selectivelaser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having selectivelaser sintering (SLS) where a laser melts flame-retardant plastic powderthat solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having directmetal laser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having fuseddeposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having metalextrusion where a filament or rod consisting of polymer and heavilyloaded with metal powder is extruded through a nozzle (like in FDM) toform the “green” part that is post-processed (debinded and sintered) tocreate a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having metalbinder jetting that uses print-heads to apply a liquid binding agentonto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and havingnanoparticle jetting that uses jetting of metal nanoparticles frominkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having electronbeam freeform fabrication (EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having selectiveheat sintering using a thermal printhead heat layer(s) of powderedmaterial to render it thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and havingstereo-lithography (SLA) using a UV laser to cure a resin of liquidUV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having digitallight processing (DLP) projecting an image of a cross-section of anobject into a quantity of photopolymer (light reactive plastic) thatselectively hardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having lightpolymerization where light causes polymer to harden in changing areasover time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having inkjettype printhead delivering liquid/colloidal binder to layers of powderedmaterial.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to integrate intomobile/vehicle-integrated/autonomous configurations and having rotarybuild table deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having amanufacturing facility to couple inputs, process data, and outputs withdigital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having amanufacturing facility to couple processes with blockchains and smartcontracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having amanufacturing facility to network additive manufacturing nodes in meshesand/or fleets for coordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having amanufacturing facility using robots that are able to attach to machinesand then print directly onto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having fusedDeposition Modeling (FDM)™ a/k/a Fused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having selectivelaser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having selectivelaser sintering (SLS) where a laser melts flame-retardant plastic powderthat solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having direct metallaser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having fuseddeposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having metalextrusion where a filament or rod consisting of polymer and heavilyloaded with metal powder is extruded through a nozzle (like in FDM) toform the “green” part that is post-processed (debinded and sintered) tocreate a fully-metal part). In embodiments, provided herein is anadditive manufacturing management platform having a manufacturingfacility to enrich AI with input/source/training set data relevant todesign factors, economic factors, quality factors, and the likecustomized to particular use cases, embodiments, applications, andapparatus and having metal binder jetting that uses print-heads to applya liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having nanoparticlejetting that uses jetting of metal nanoparticles from inkjet nozzles insuper-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having electron beamfreeform fabrication (EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having selectiveheat sintering using a thermal printhead heat layer(s) of powderedmaterial to render it thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and havingstereo-lithography (SLA) using a UV laser to cure a resin of liquidUV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having digital lightprocessing (DLP) projecting an image of a cross-section of an objectinto a quantity of photopolymer (light reactive plastic) thatselectively hardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having lightpolymerization where light causes polymer to harden in changing areasover time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having inkjet typeprinthead delivering liquid/colloidal binder to layers of powderedmaterial.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to enrich AI withinput/source/training set data relevant to design factors, economicfactors, quality factors, and the like customized to particular usecases, embodiments, applications, and apparatus and having rotary buildtable deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having a manufacturing facility tocouple processes with blockchains and smart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having a manufacturing facility tonetwork additive manufacturing nodes in meshes and/or fleets forcoordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having a manufacturing facility usingrobots that are able to attach to machines and then print directly ontoa replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having fused Deposition Modeling(FDM)™ a/k/a Fused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having selective laser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having selective laser sintering(SLS) where a laser melts flame-retardant plastic powder thatsolidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having direct metal laser sintering(DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having fused deposition modeling(FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having metal extrusion where afilament or rod consisting of polymer and heavily loaded with metalpowder is extruded through a nozzle (like in FDM) to form the “green”part that is post-processed (debinded and sintered) to create afully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having metal binder jetting that usesprint-heads to apply a liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having nanoparticle jetting that usesjetting of metal nanoparticles from inkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having electron beam freeformfabrication (EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having selective heat sintering usinga thermal printhead heat layer(s) of powdered material to render itthermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having stereo-lithography (SLA) usinga UV laser to cure a resin of liquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having digital light processing (DLP)projecting an image of a cross-section of an object into a quantity ofphotopolymer (light reactive plastic) that selectively hardens the imagearea.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having light polymerization wherelight causes polymer to harden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having inkjet type printheaddelivering liquid/colloidal binder to layers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple inputs, process data,and outputs with digital twins and having rotary build table deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having a manufacturing facility tonetwork additive manufacturing nodes in meshes and/or fleets forcoordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having a manufacturing facilityusing robots that are able to attach to machines and then print directlyonto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having fused Deposition Modeling(FDM)™ a/k/a Fused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having selective laser melting(SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having selective laser sintering(SLS) where a laser melts flame-retardant plastic powder thatsolidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having direct metal laser sintering(DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having fused deposition modeling(FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having metal extrusion where afilament or rod consisting of polymer and heavily loaded with metalpowder is extruded through a nozzle (like in FDM) to form the “green”part that is post-processed (debinded and sintered) to create afully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having metal binder jetting thatuses print-heads to apply a liquid binding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having nanoparticle jetting thatuses jetting of metal nanoparticles from inkjet nozzles in super-thinlayers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having electron beam freeformfabrication (EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having selective heat sinteringusing a thermal printhead heat layer(s) of powdered material to renderit thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having stereo-lithography (SLA)using a UV laser to cure a resin of liquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having digital light processing(DLP) projecting an image of a cross-section of an object into aquantity of photopolymer (light reactive plastic) that selectivelyhardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having light polymerization wherelight causes polymer to harden in changing areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having inkjet type printheaddelivering liquid/colloidal binder to layers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to couple processes withblockchains and smart contracts and having rotary build tabledeposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operation.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having a manufacturing facility using robots that are able to attachto machines and then print directly onto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having fused Deposition Modeling (FDM)™ a/k/a Fused FilamentFabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having selective laser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having selective laser sintering (SLS) where a laser meltsflame-retardant plastic powder that solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having direct metal laser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having fused deposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having metal extrusion where a filament or rod consisting of polymerand heavily loaded with metal powder is extruded through a nozzle (likein FDM) to form the “green” part that is post-processed (debinded andsintered) to create a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having metal binder jetting that uses print-heads to apply a liquidbinding agent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having nanoparticle jetting that uses jetting of metal nanoparticlesfrom inkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having electron beam freeform fabrication (EBFFF) using electronbeam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having selective heat sintering using a thermal printhead heatlayer(s) of powdered material to render it thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having stereo-lithography (SLA) using a UV laser to cure a resin ofliquid UV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having digital light processing (DLP) projecting an image of across-section of an object into a quantity of photopolymer (lightreactive plastic) that selectively hardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having light polymerization where light causes polymer to harden inchanging areas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having inkjet type printhead delivering liquid/colloidal binder tolayers of powdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility to network additivemanufacturing nodes in meshes and/or fleets for coordinated operationand having rotary build table deposition.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingfused Deposition Modeling (FDM)™ a/k/a Fused Filament Fabrication™.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingselective laser melting (SLM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingselective laser sintering (SLS) where a laser melts flame-retardantplastic powder that solidifies.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingdirect metal laser sintering (DMLS).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingfused deposition modeling (FDM).

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingmetal extrusion where a filament or rod consisting of polymer andheavily loaded with metal powder is extruded through a nozzle (like inFDM) to form the “green” part that is post-processed (debinded andsintered) to create a fully-metal part.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingmetal binder jetting that uses print-heads to apply a liquid bindingagent onto layers of powder.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingnanoparticle jetting that uses jetting of metal nanoparticles frominkjet nozzles in super-thin layers.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingelectron beam freeform fabrication (EBFFF) using electron beam welding.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingselective heat sintering using a thermal printhead heat layer(s) ofpowdered material to render it thermoplastic.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingstereo-lithography (SLA) using a UV laser to cure a resin of liquidUV-curable photopolymer.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingdigital light processing (DLP) projecting an image of a cross-section ofan object into a quantity of photopolymer (light reactive plastic) thatselectively hardens the image area.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havinglight polymerization where light causes polymer to harden in changingareas over time.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havinginkjet type printhead delivering liquid/colloidal binder to layers ofpowdered material.

In embodiments, provided herein is an additive manufacturing managementplatform having a manufacturing facility using robots that are able toattach to machines and then print directly onto a replacement and havingrotary build table deposition.

Enterprise Access Layer

In network computing, an access layer generally refers to one or morelayers in an information technology infrastructure that provides accessto the infrastructure. The overarching purpose of the access layer is togrant a user, for example via a system or a device, access to resourcesof the infrastructure, such as network resources, storage resources,processing resources, and others. For example, in a wide area network(WAN) environment, a network access layer provides access to thecorporate network across wide-area technology, such as Frame Relay,Multiprotocol Label Switching (MPLS), Integrated Services DigitalNetwork, leased lines, digital subscriber lines (DSL) over traditionaltelephone lines or coaxial cable. Since the access layer provides localand remote access to a network, the access layer may function as aconcentration point where remote users (e.g., clients, partners, etc.)meet local users or infrastructure.

Protocols in the access layer provide a means for one or more systems todeliver data to other devices or systems connected to a set ofinfrastructure, such as by a communication network. For instance, theseprotocols may provide a means to deliver data from a private network toa public network. In this sense, the access layer may be considered aninterface that is public or client-facing while also beingprivate-facing. An access layer's private-facing capability may refer toits ability to receive, translate, and/or communicate data correspondingto private resources (e.g., private digital assets) from a privatenetwork, while its public or client-facing capability may refer to itsability to communicate with or provide access to users that are externalto the private network (e.g., public market participants). Inembodiments, to perform its functionality as a network intermediary, anetwork access layer may have protocols and systems that understanddetails about the endpoints for which it is a facilitator. Inembodiments, an access layer may include various sublayers, services,modules, and components, operating according to a variety of differentprotocols, such as to enable access among a wide range of participatingentities.

One environment that can utilize the functionality of an access layer isan enterprise. An enterprise generally refers to an organization with aparticular overarching purpose, goal, or objective. For instance, apurpose may be to produce and market a particular set of one or moreproduct lines, to undertake a charitable activity, to provide a publicservice, or other purpose. To achieve its purpose, an enterprise mayhave a structure that includes various business units, such as executiveofficers, a board of trustees or directors, divisions, departments,managers and other job roles, facilities and other assets, a wide arrayof projects, activities, processes and workflows, etc. Some enterprisesspan multiple business sectors and therefore have business units, suchas divisions, that can be dedicated to a particular business sector.

Enterprises, usually by their size and nature, can have a wide array ofresources and assets. For instance, their resources may include rawmaterials, equipment, devices, systems, products (e.g., parts,components, sub-assemblies, assemblies), capital, knowledge, andtechnology among others. Some examples of knowledge resources includeresources that are customer-based (e.g., customer lists or customertransactional history such as order history, contact information, demandfrequency, etc.), vender/supplier-based (e.g., suppliers, procurementinformation, supply transactional history, etc.), process-based (e.g.,formulations, procedures such as standard operating procedures,technical data sheets, process reports such as material compliancereports or quality reports, or other memorialized process expertise),and research-based (e.g., research and development information orreports). Enterprise resources may also include human resources,including expertise and knowledge of enterprise personnel andcontractors, or personnel and contractors of customers, suppliers,vendors, partners, or the like. Technology resources may includeresources such as inventions, trade secrets, designs, proprietaryinformation of the enterprise (e.g., proprietary software or processes),and/or the like.

In some embodiments, some or all of the resources of the enterprise maybe represented in some digital form (e.g., a particular file format),such that these resources may undergo management and processing actionssuch as being copied, edited, shared, transferred, exchanged, updated,recorded, monitored, accessed, extracted, transformed, loaded,compressed, decompressed, deleted, obsoleted or otherwise processed,such as in digital form or between digital form and another form (suchas where knowledge of an expert worker or other individual is accessedby querying the worker through a crowdsourcing system). Even resourcesthat have not had a conventional digital format (e.g., physical goods orequipment) may be represented in a digital format. For example, anon-fungible token may be used to represent resources that are notdigital. Additionally or alternatively, some aspect of a resource (e.g.,a physical good) can be represented as a digital form or via a digitalproxy. For instance, a physical resource may have an associated digitalcertificate of authenticity, proof of purchase, deed, or a title.

Due to the expanding evolution of digital assets, it is inevitable thatenterprises demand an efficient and robust manner of managing digitalassets. For example, just as enterprises have historically andefficiently engaged in the transaction of physical goods and thelogistics involved in those transactions, enterprises will likely needto address similar aspects for digital transactions. Furthermore, withdigital assets, there may be different issues that need to be addresseddue to the digital nature of these assets when compared to physicalassets. For instance, although unauthentic copies of physical goods arefeasible, often, depending on the physical good, the energy, expertise,or equipment needed to generate a copy of physical goods can by itselfinhibit copying and help promote the authenticity of a physical asset.In comparison, a digital asset may be easier to replicate. For example,computing has predominantly evolved with a particular simplicity toread/write functionality; making digital files/formats in many caseseffortless to duplicate often with minimal loss. Ease of duplication canresult in complications, such as where a digital asset is copied andwidely distributed and some copies are subsequently modified, making itdifficult to determine which versions, among many, are valid. Problemsof provenance and validity are compounded with the increasing presenceof dynamic digital such as smart contracts and dynamic objects, that areserially updated without human intervention through a network, often bylinkage to other dynamic objects that are of uncertain provenance.

Another aspect that is different between physical assets and digitalassets is interoperability. Interoperability refers to the ability ofsystems to exchange and use information. For a physical asset, supplychains are typically structured by participating enterprises tofacilitate structural interoperability, such as among the componentparts of a system, chemical operability, such as among constituentingredients in a recipe, or the like. For digital assets (such termincluding physical assets that have a digital component or capability(such as smart devices and systems)), interoperability may have avariety of different issues. For example, having the computing resourcesto interact with a digital asset may not be cost prohibitive. Therefore,there may be a large number of entities that are able to cooperate withregard to a digital asset. Additionally, the number of entities isfairly elastic because it may quickly increase or decrease depending onthe scarcity or demand for the digital asset (e.g., due to its low-costbarrier to entry). Yet a potential outgrowth of the large number ofentities that are able to interact with a digital asset is that theaccess point should have the capability to accommodate for variancebetween the entities and/or the volume of entities; as a result,communication protocols, authentication protocols, validation protocols,formatting protocols, etc. need to consider the many actors that areable to participate in the digital asset ecosystem.

The management of digital assets and the transactions they involve mayalso be able to capitalize on their digital ecosystem. That is, themechanism involved in transactions for digital assets may leveragecomputing resources to promote optimal transactions. In other words,with digital assets being digital, they are inherently associated withcomputing resources and therefore a transaction ecosystem can utilizethe associated computing capabilities to potentially enhance thecircumstances of a transaction involving a digital asset. As an example,it is not uncommon for an asset to have some inventory period where theowner or controller of the asset has the asset available but needs toidentify a receiving party and/or terms for the transaction of theasset.

With the computing resources associated with the digital asset oravailable to the holder of the digital asset, a transactional ecosystemcan be configured that can provide autonomy and/or self-promotion fortransactions or asset management actions for a digital asset; that is,instead of the manual execution or facilitation of agreements regardingthe transactions of digital assets, a transactional ecosystem for thedigital asset can automate and/or facilitate one or more phasesassociated with digital asset transactions. These phases may include adiscovery/identification phase that identifies a candidate transactionopportunity involving a digital asset, a diligence/evaluation phase thatmay evaluate the parameters of the transaction opportunity, aconfiguration phase that may configure the proposed terms of thetransaction (e.g., an exchange rate or a time for the transaction), anegotiation phase that may adjust the terms of the transaction throughone or more rounds of negotiation, an execution phase that executes theconfigured transaction for the digital asset and/or a performance phasethat executes performance of one or more actions called for by the termsof the transaction (e.g., delivery of a digital asset to a definedaddress at a defined time). In this sense, the transactional ecosystemmay be capable of self-promoting because the transactional systems canidentify candidate transactions for a digital asset without potentiallyneeding human intervention. Although this level of autonomy is feasible,the digital ecosystem may also operate as a hybrid such that certainaspects of the transaction request require some form of authorizationprior to automatic execution (e.g., authorization from external sourcesuch as a manual input). Additional aspects of various phases of digitalasset transactions, such as relating to counterparty discovery,monitoring of collateral, automation of underwriting, automatednegotiation, and many others are described in the documents incorporatedherein by reference and are intended to be encompassed herein exceptwhere context prevents) and/or direct instruction to perform one or moreof the phases associated with a digital asset transaction.

To address the growing demands for effective digital asset ecosystems,the approach described herein may include an enterprise access layer. Insome implementations, an “enterprise” access layer refers to a networkaccess layer by which an enterprise may access various digital assetsand resources (including various entities described in connection withthe transaction platforms and systems described herein and in thedocuments incorporated herein by reference) that may be involved in aset of transactions (such as bilateral or multilateral transactionsinvolving the enterprise, as well as ones enabled by a set ofmarketplaces, exchanges or the like with which an enterprise interacts)via a set of network resources. The enterprise may have control (e.g.,direct control), management authority, and/or rights to use or access aset of digital assets that are presented to or accessible via the accesslayer. In embodiments, an enterprise access layer is capable ofsimplifying transactions for an enterprise (such as reflecting“consumerization”) because it allows an enterprise to interface withmultiple markets, marketplaces, exchanges, and/or platforms (e.g.,relating to different business segments) through a common point ofaccess.

One advantage of an enterprise access layer is that it may be configuredto operate in conjunction with technologies that enterprises deploy intheir own environments (i.e., on their private networks, includingon-premises and cloud resources and platforms). This may include a widerange of software applications, programs and modules, services andmicroservices, and the like, including blockchains, distributed ledgertechnology (DLT), decentralized applications (dApps), intelligentagents, robotic process automation systems, and a wide variety of bigdata, analytics and artificial intelligence systems. In one non-limitingexample, as enterprises deploy DLT and/or dApps, many enterprises willlikely want this technology to assimilate with the other systems,structures and workflows of the enterprise.

Throughout an enterprise, different entities may have different rolesand responsibilities that can result in varying levels of permissionand/or access to enterprise resources. For example, a human resourceemployee is unlikely to be able to access machinery or equipment of amanufacturing engineer for the same business. Similarly, it is notlikely that the manufacturing engineer can access other employee'spersonnel files like the human resource employee. Based on suchdifferences, technology deployed internally for an enterprise is likelyto have some level of permissioning. In embodiments, an enterprise mayprefer for the permissioning of technologies like DLTs and dApps to besimilar to or aligned with the physical resource access that iscustomary to a particular role. For example, when a resource isauthenticated and stored on an enterprise's blockchain, that humanresource employee would not be an authentication stakeholder for anoperations-based resource (e.g., a manufacturing resources), or viceversa.

Generally speaking, a permissioned distributed ledger (e.g., ablockchain) refers to a ledger design where the ledger is not open foreveryone to participate in a similar manner like a permissionless ledger(e.g., a public blockchain). Rather, a permissioned ledger may beconfigured such that participants have particular control/access rights.Enterprises may tend to deploy permissioned systems in their privatenetworks to have access safeguards for enterprise resources while publicdistributed ledgers attempt to be wholly decentralized and allow anyoneto participate with the ledger. For example, enterprises may prefer todeploy permissioned systems because these systems can shield sensitiveinformation, ensure member compliance, and ease the rollout ofparticular, member-level deployments such as updates andreconfigurations.

FIG. 231 is an example of a general structure for an enterpriseecosystem 23100. In embodiments, the enterprise ecosystem 23100 is anecosystem where marketplace participants 23110 are able to utilizepublic or third-party services 23120 to interface with an enterprise23200 via an enterprise access layer (EAL) 23300. In some embodiments,the market participants 23110 may be any entity that interacts with theenterprise 23200, such as buyers, sellers, vendors, suppliers,manufacturers, service providers, partners, distributors, resellers,agents, retailers, brokers, promotors, advertisers, clients, escrowagents, advisors, customers, bankers, insurers, regulatory entities,hosts (e.g., of marketplaces, exchanges, platforms or infrastructure,among others), logistics and transportation providers, infrastructureproviders, platform providers, and others (including various entitiesdescribed elsewhere herein and/or in the documents incorporated byreference herein). As shown in FIG. 231 , some market participants 23110may be buyers 23112 (also referred to as purchasers or customers) whenthe enterprise 23200 is the asset provider (e.g., the enterprise is theselling, giving, or sharing party). Market participants 23110 may alsobe sellers 23114 (also referred to as venders or providers) when theenterprise 23200 is the receiving party or asset acquirer.

The EAL 23300 may be configured to interact with the market participants23110 (and the ecosystem(s) in which they interact) in a variety ofways. For example, the EAL 23300 may be integrated or associated withone or more marketplaces 23122 such that the EAL 23300 functions as itsown market participant on behalf of the enterprise 23200. By beingassociated with potentially numerous marketplaces (e.g., marketplacesthat correspond to the type or nature of the enterprise assets), the EAL23300 can perform complex or multi-stage transactions with enterpriseassets (e.g., in a series or sequence of timed stages, simultaneously ina set of parallel transactions, or a combination of both).

In an example of a multi-stage transaction, the enterprise 23200 mayperform a sequence of transactions. For example, the sequence oftransactions may be for the purpose of acquiring or accessing a resourcefrom another source (e.g., one of the sellers 23114). For instance, theenterprise 23200 demands resource ALPHA. However, the enterprise 23200may not have any assets that are directly exchangeable for resourceALPHA. Therefore, the EAL 23300 may be configured to recognize how toacquire one or more assets that are exchangeable for resource ALPHAusing the available digital assets of the enterprise 23200. Toillustrate, the enterprise 23200 may have resources BETA and GAMMA. Toacquire resource ALPHA, the EAL 23300 identifies that resource DELTA isdirectly exchangeable for resource ALPHA. In this example, the EAL 23300may perform transactions with BETA and GAMMA to acquire DELTA in orderto finally acquire resource ALPHA. For instance, the EAL 23300 exchangesresource BETA with a first asset source for resource EPSILON and then isable to exchange both resources GAMMA and EPSILON for resource DELTAfrom a second asset source. With the acquisition of resource DELTA, theEAL 23300 exchanges resource DELTA with a third asset source forresource ALPHA. Without an EAL 23300, acquiring resource ALPHA may berather difficult because it demands access to multiple sources (e.g.,across multiple marketplaces) and mapping how resources associated withthose sources can be leveraged to obtain a target resource. Yet with theEAL 23300 that has access to multiple marketplaces 23122 and marketplaceparticipants 23110, the EAL 23300 can configure and/or execute atransaction sequence or routine that maps how to obtain the targetresource (e.g., resource ALPHA). This may occur regardless ofrelationship between marketplaces 23122 and/or marketplace participants23110 such that the EAL 23300 may leverage disparate and independentmarkets to perform a transaction for a target resource. In other words,resource E may be offered or available in a marketplace 23122 that is adifferent and distinct marketplace 23122 from the marketplace 23122 thatoffers the target resource, resource ALPHA.

In embodiments, elements of a multi-stage sequence may be conditional,such that a contingent condition must be satisfied in order for a laterstage to commence after completion of a prior stage. Conditions mayinclude ones based on pricing, timing, and other transaction parameters.

In addition to marketplaces 23122, the EAL 23300 may interact withmarketplace participants 23110 via third-party systems 23124 (some orall of which may be implemented as third-party services). Some examplesof third-party systems 23124 include various financial services/systemssuch as operated by banks, insurers, lending institutions, valuationservices, trading services, or escrow services, authenticationservices/systems, auditing services/systems, security system/services,etc.

In some examples, the market participants 23110 and/or marketplaces23122 may use or be associated with a storage system 23126 (which may beimplemented as a storage service). In some configurations, the storagesystem 23126 may include an append-only persistent storage system suchas a blockchain (e.g., as labelled in FIG. 231 ). An append-onlypersistent storage system refers to a storage system that, when storingdata, appends blocks of the newest data to be stored to the most recentblock previously stored. In this sense, the chain of storage blocks mayfunction as a time sequence, which may be cryptographically secured toform an immutable time sequence. This structure may be advantageousbecause someone who has access to the storage system may be able todetermine a history of data storage transactions with relative ease. Ablockchain storage system may be a permissionless storage system that isopen to all of its members (e.g., all or some portion of participants23110 in a marketplace 23122) or a permissioned storage system dependingon the nature of the marketplace 23122 or the third-party system 23124associated with the storage system 23126.

As described previously, the enterprise 23200 may include enterprisedevices 23220 (e.g., enterprise equipment such as user devices,on-premises, cloud and other network infrastructure, general and/orspecialty processors (e.g., edge processors), internet of things (IOT)and industrial internet of things (IIoT) devices), systems, processes,etc.) that generate, interface, or generally impact enterprise resources23210. As with the non-enterprise aspect of the enterprise ecosystem23100 (i.e., the market-participant side 23104 shown in FIG. 231 ), insome examples the enterprise 23200 includes one or more privateappend-only storage systems 23240 (e.g., a private blockchain). Thestorage system 23240 may be considered private in that the enterprise23200 controls the access and permission for the private storage system23240. For example, the private storage system 23240 may be onlyaccessible to devices that have access to a private network associatedwith the enterprise 23200, such as a WAN. In some implementations, theenterprise 23200 has more than one private blockchains in order totailor to, for example, the organizational structure of the enterprise23200. For instance, the enterprise 23200 has one private blockchainthat corresponds to a storage system for operations or aproduct-generating portion of the enterprise 23200 and another privateblockchain that corresponds to storage systems for administrativeportions of the enterprise 23200. As another example, the enterprise23200 has a single blockchain with a set of sidechains for components ororganizational units of its organizational structure.

In addition to a private blockchain, the enterprise 23200 may include aset of enterprise data stores 23230. When compared to a blockchain, adata store refers to a set of data storage types that is not limited toan append-only persistent data storage structure. Rather, an enterprisedata store 23230 may be any one or combination of a relational database(e.g., a structured query language (SQL) database), a non-relationaldatabase (e.g., a non-SQL database), a key-value store (i.e., a map fromkeys to values), a full-text search engine, a distributed database, aset of network-attached storage resources, a message queue or other datastorage system or service of any of the many types described herein orin the documents incorporated by reference herein.

The data store 23230 may store enterprise data that is obtained fromenterprise resources 23210 or from other various data sources 23250 ofthe enterprise 23200. For example, FIG. 232 depicts that the enterprise23200 may include internal or private enterprise systems that generatedata specific to the enterprise 23200 (i.e., enterprise data). Someexamples of these private enterprise systems that at least partiallyfunction as data sources 23250 include enterprise resource planning(ERP) systems, customer relationship management (CRM) systems thatcontain customer-related information, healthcare systems, supply chainsystems (e.g., supply chain management (SCM) systems) that includeinter-organizational supply chain information, product life cyclemanagement (PLM) systems that include product or service lifecycleinformation (e.g., data characterizing items, parts, products,documents, product/service requirements, engineering change orders, andquality information), accounting systems, research and developmentsystems, and HR systems, and/or the like.

In some examples, as shown in FIG. 232 , the enterprise 23200 includes aset of analytical systems 23260. These analytical systems 23260 mayrefer to tools deployed by the enterprise 23200 to perform analysis forvarious processes or systems associated with the enterprise 23200. Forinstance, an enterprise 23200 may find it pertinent to their operationsto perform market analytics (e.g., for advertising, new productdevelopment, and/or marketing purposes). Another type of analytics thatthe enterprise 23200 may perform is demographic analytics. Demographicanalytics may aid an enterprise to understand relevant demographic,psychographic, location, behavioral and other information aboutcustomers, venders, employees, potential employees, or a targetmarketplace. For instance, an enterprise 23200 uses demographicanalytics to determine how a new product can reach a particular targetdemographic or how an existing product/service is perceived by variousdemographics. Additionally or alternatively, to market analytics and/ordemographic analytics, the analytical system 23260 of the enterprise23200 may be configured to perform an array of statistical analysis.This statistical analysis may be used to support many differentactivities throughout the enterprise 23200 including analytics performedby other systems of the enterprise 23200 or of the analytical system23260 itself (e.g., supporting the market analytics, the demographicanalytics, or any of a wide variety of other analytics described hereinor in the documents incorporated by reference herein).

FIGS. 231 and 232 illustrate examples of the EAL 23300. In both of theseexamples, the EAL 23300 is shown to include a number of EAL systems(also referred to modules or EAL modules) that enable the functionalityof the EAL 23300. In some examples, these EAL systems are deployed in acontainer that is specific to the EAL 23300. When deployed in acontainer for the EAL 23300, this containerized instance means that theEAL 23300 includes the necessary tools and computing resources tooperate (i.e., host) the EAL systems without reliance on other computingresources associated with the enterprise 23200 (e.g., computingresources such as processors and memory dedicated to the EAL 23300). Forexample, the container for the EAL 23300 may include a set of one ormore systems, such as software development kits, application programminginterfaces (APIs), libraries, services (including microservices),applications, data stores, and/or processors or the like to execute thefunctions of the EAL systems that may enable the EAL 23300 to provideenterprise asset transactional management and other functions andcapabilities described throughout this disclosure. References herein to“EAL systems” should be understood to encompass any of the foregoingexcept where context dictates otherwise.

In some implementations, a set of the EAL systems leverages computingresources considered to be external to the EAL 23300 (e.g., separatefrom computing resources that have been dedicated to the EAL 23300, suchas, in embodiments, computing resources shared with other enterpriseapplications or systems). In these implementations, the set of EALsystems leveraging external computing resources may be in communicationwith computing resources specific to the EAL 23300. This type ofarrangement may be advantageous when one or more of the EAL systems arecomputationally expensive and would increase the computationalrequirements for an entirely contained EAL 23300, such as when one ormore of the EAL systems causes the EAL 23300 to be a relativelyexpensive EAL deployment. For instance, an arrangement leveragingexternal (e.g., shared) systems may be beneficial for EAL systems thatare infrequently utilized. To illustrate, a first enterprise may rarelyuse an EAL system, such as a reporting system. Here, instead of ensuringthat the EAL 23300 has the computational capacity to support a reportingsystem by itself, the enterprise 23200 configures the reporting systemto be hosted by and/or supported by computing resources external to theEAL 23300 to deploy a relatively lean form of the EAL 23300 (i.e., anEAL container that does not include resources dedicated to a reportingsystem or that includes only limited resources dedicated to thereporting system with the capability to access additional, externalresources as needed)).

In some configurations, the EAL 23300 or a set of the EAL systemsleverages computing resources considered to be external to the EAL 23300for support. An example of this support may be that the EAL 23300 or theset of EAL systems demands greater computing resources at some point intime (e.g., over a resource intensive time period). For instance,greater being more computing resources than a normal or baselineoperation state. In this example, for instance, the enterprises resourcenot dedicated to the EAL 23300 or EAL systems can assist or augment theservices provided by some aspect of the EAL 23300. To illustrate, theEAL leverages enterprise resources to assist or augment the performanceof analysis, such as managing and/or analyzing governance for healthcare data associated with clients of a particular enterprise.

In embodiments, the deployment of the EAL 23300 may be configurable. Forexample, the enterprise 23200 or some associated developer can functionas a type of architect for the EAL 23300 that best serves the particularenterprise 23200. Additionally, or alternatively, the deployed locationof the EAL 23300 may influence its configuration. For instance, the EAL23300 may be embedded within an enterprise (e.g., non-dynamically) whereit can be specifically configured using various module libraries,interface tools, etc. (e.g., as described in later detail). In someexamples, the configuring entity is able to select what EAL systems willbe included in its EAL 23300. For instance, the enterprise 23200 selectsfrom a menu of EAL systems. Here, when an EAL system is selected by theconfiguring entity, a configuration routine may request the appropriateresources for that EAL system including SDKs, computing resources,storage space, APIs, graphical elements (e.g., graphical user interface(GUI) elements), data feeds, microservices, etc. In someimplementations, in response to the request, the configuring entity candedicate the identified resources of each selected EAL system. Forinstance, the configuring entity associates the dedicated resources to acontainerized deployment of the EAL 23300 that includes the selected EALsystems.

Referring specifically to FIGS. 231 and 232 , the EAL 23300 includes aset of eight EAL systems. The set includes an interface system 23310, adata services system 23320, an intelligence system 23330, a workflowsystem 23340, a wallet system 23350 (also referred to as a digitalwallet system), a governance system 23360, a permissions system 23370,and a reporting system 23380. It should be noted that even though bothFIGS. 231 and 232 include the set of eight EAL systems, the EAL 23300may include any number of EAL systems (e.g., three systems, fivesystems, or seven systems, or any other suitable number of EAL systems).Additionally, although particular types of EAL systems are describedherein, the functionality of one or more EAL systems is not limited toonly that particular EAL system, but may be shared or configured tooccur at another EAL system. For instance, in some configurations, somefunctionality of the wallet system 23350 may be performed by the dataservices system 23320 or functionality of the governance system 23360may be incorporated with the intelligence system 23330. In this respect,the EAL systems may be representative of the capabilities of the EAL23300 more broadly. In embodiments, the set of EAL systems involved inany particular configuration of the EAL 23300 may include any of thesystems described throughout this disclosure and the documentsincorporated by reference herein, such as systems for counterpartydiscovery, opportunity mining, automated contract configuration,automated negotiation, automated crowdsourcing, automated facilitationof robotic process automation, one or more intelligent agents, automatedresource optimization, resource tracking, and others.

The interface system 23310 is an EAL system that communicates on behalfof the EAL 23300 and/or enables communication with the EAL 23300 by oneor more entities, which may include human operators and/or machines. Tocommunicate on behalf of the EAL 23300, the interface system 23310 iscapable of communicating with some or all portions of the enterprise23200 (e.g., enterprise devices 23220, representatives of the enterprise23200, and/or private storage systems 23240 of the enterprise 23200). Insome examples, to communicate with the enterprise 23200, the EAL 23300is configured with access rights to the private network of theenterprise 23200. With access to the private network of the enterprise23200, the interface system 23310 can function as a communicationconduit to call a system or device of the enterprise 23200 in order tosupport another EAL system.

Additionally, the interface system 23310 enables there to be a centralcommunication hub that members of an enterprise 23200 may use to engagewith functions of the EAL 23300. For instance, a business unit decidesto offer an enterprise resource 23210 as a digital enterprise asset thatis available to market participants 23110. Here, a member of theenterprise 23200 or an enterprise device 23220 responsible for theenterprise resource 23210 communicates the enterprise resource 23210 tothe wallet system 23350 via the interface system 23310.

As a central communication hub, the interface system 23310 may alsofunction as a communication means that the EAL systems use tocommunicate with endpoints at the enterprise side (e.g., shown as anenterprise-side 23102 in FIG. 231 ) or the market participant side(e.g., shown as the market-side 23104 in FIG. 231 ). For example, theinterface system 23310 operates in conjunction with the EAL systems ofthe EAL 23300 to ensure that the interface system 23310 includes theappropriate APIs, links, brokers, connectors, bridges, gateways,portals, services, data integration systems or other means oftranslating communications (e.g., data packets or data messages) ofintra-EAL systems (e.g., between EAL systems) and/or from the EALsystems to an endpoint on either of the enterprise side (e.g., anenterprise device) or market participant side (e.g., a marketplace23122, the storage system 23126, or marketplace participant 23110). Forexample, among many others, the interface system 23310 may have an APIthat the enterprise 23200 uses to receive or to obtain reports from thereporting system of the EAL 23300.

As shown in FIG. 232 , the interface system 23310 may includecapabilities such as authentication and/or security protocols as a meansto enforce who has the ability to use the EAL 23300. For instance, anentity that is able to use to use the EAL 23300 may receive credentialsthat indicate the entity's access permission(s) with respect to the EAL23300. These credentials may be login credentials, an authenticationtoken, digitized cards/documents, biometric feature(s), one-timepasswords, or any other information that functions as proof that theentity has a right to access the EAL 23300 via the interface system23310. In embodiments, credentials may be managed by anidentity-as-a-service platform or other identity management systems. Itis appreciated that authentication of an entity may includeauthentication of human users and/or to specific devices/softwaresystems that are authorized to interact with the EAL 23300.

In some examples, the credentials issued to an entity are configured toidentify the access rights of the entity. When the credentials identifythe access rights of the entity, the interface system 23310 may be ableto determine the access rights and tailor which portions of theinterface system 23310 that the entity can access. In embodiments, theinterface system 23310 is capable of restricting portions of variousinterfaces or communication channels to EAL systems of the EAL 23300using the information contained or indicated by credentials that havebeen associated or issued to an entity.

The data services system 23320 refers to an EAL system that performsdata services for the EAL 23300. Data services may include dataprocessing and/or data storage. This may range from more generic dataprocessing and data storage to specialty data processing and storagethat demands specialty hardware or software. In some examples, the dataservices system 23320 includes a database manager to manage the datastorage services provided by the data services system 23320. In someconfigurations, the database manager is able to perform managementfunctions such as querying the data being managed, organizing data for,during, or upon ingestion, coordinating storage sequences (e.g.,chunking, blocking, sharding), cleansing the data, compressing ordecompressing the data, distributing the data (including redistributingblocks of data to improve performance of storage systems) and/orfacilitating processing threads or queues, and the like. In someexamples, the data services system 23320 couples with otherfunctionality of the EAL 23300. As an example, operations of the dataservices system 23320, such as data processing and/or data storage, maybe dictated by decision-making or information from other EAL systemssuch as the intelligence system 23330, the workflow system 23340, thewallet system 23350, the governance system 23360, the permissions system23370, the reporting system 23380, and/or some combination thereof.

In some implementations, the data services system 23320 includesencryption/decryption capabilities that pair with the dataprocessing/storage. For instance, the data services system 23320 maydecrypt data when encrypted data is retrieved from its data store(s). Inother situations, the data services system 23320 may encrypt data thatis being used, processed, and/or stored at the EAL 23300. For instance,the data services system 23320 receives data to be stored, determinesthat the received data includes one or more characteristics that satisfyan encryption rule, and encrypts the data prior to, during, or after thedata is transferred to a storage location. In this respect, the dataservices system 23320 may receive an encryption or decryption requestthat specifies data associated with the data services system 23320 andthe data services system 23320 is capable of fulfilling the request andproviding the encrypted/decrypted data to the requesting entity. Thedata services system 23320 may be configured to provide symmetricalencryption, asymmetrical encryption, or other suitable types ofencryption. Some encryption algorithms that the data services system23320 may use are Advanced Encryption Standard (AES),Rivest-Shamir-Adleman (RSA), and variations of Data Encryption Standard(DES) (e.g., 3DES), among others. Additionally or alternatively, thedata services system 23320 may also perform hashing or othercryptographic functions to verify data that it manages for the EAL23300.

The intelligence system 23330 of the EAL 23300 functions to provideintelligent functionality to the EAL 23300. Among other aspects, theintelligence system 23330 is a system that the EAL 23300 can utilize fordecision-making regarding transactions for enterprise digital assets.For instance, the intelligence system 23330 may recruit and/orcoordinate a set of EAL systems (e.g., including enterprise sources) asnecessary to provide a set of outputs in response to one or moreintelligent requests (i.e., decision-making request). Some intelligentor decision-making functionality that the intelligence system 42230 iscapable of providing includes peer or counterparty discovery (i.e.,identifying parties for a transaction, such as one using enterpriseassets or assets that are desired to be acquired by or for anenterprise, among others), automated asset allocation and positionmaintenance (e.g., automated acquisition or disposition of assets tomaintain a desired allocation of assets across asset classes, such as tomaintain a desired balance of risk and return across the asset classes),automated asset management (e.g., determining which wallets of thewallet system that an available enterprise asset should be associatedwith), automated transaction configuration (e.g., assembling smartcontract and/or smart contract terms for a set of digital assettransactions), automated negotiation of transaction terms, automatedsettlement (e.g., by execution of on-chain transfers), modeling oranalysis of a set of transactions or a transactions strategy,forecasting or predicting asset or transaction parameters (e.g., prices,trading volumes, trading timings, etc.), automated prioritization (e.g.,prioritization of transactions among a set of transactions, of assetsamong a set of assets, of workflows (e.g., prioritizing a set ofworkflows among others for access to available resources of the EAL23300), configuration of transaction timing, and/or automated managementof a set of policies (e.g., enterprise governance policies, regulatoryor legal policies, risk management policies, and others).

In embodiments, the intelligence system 23330 is capable of learningfrom prior transactions to inform future transactions. To have thislearning capability, the intelligence system 23330 may include a set oflearning models that identify data and relationships in transactionaldata, such as transactional training data set consisting of historicaltraining data (which, in embodiments, may be augmented by generated orsimulated training data). Models may include financial, economic,econometric, and other models described herein or in the documentsincorporated by reference herein. Learning may use an expert system,decision tree, rule-based workflow, directed acyclic workflow, iterative(e.g., looping) workflow, or other transaction model. Some examples oflearning models include supervised learning models, unsupervisedlearning models, semi-supervised learning models, deep learning models,regression models, decision tree models, random forest or ensemblemodels, and the like. Learning models may use neural networks (e.g.,feedback and/or feed forward neural networks, convolutional neuralnetworks, recurrent neural networks, gated recurrent neural networks,long short-term memory networks, or other neural networks described inthis disclosure or in the documents incorporated herein by reference).Learning may be based on outcomes (e.g., financial yield and othermetrics of enterprise performance), on supervisory feedback (e.g., froma set of supervisors, such as human experts and/or supervisoryintelligent agents), or on a combination.

In some examples, the learning models of the intelligence system 23330may train using enterprise data that relates to transactions for digitalenterprise assets. In this case, training data sets may be proprietaryto the enterprise. By having enterprise specific training data sets(i.e., with enterprise training examples), the enterprise 23200 learnshow to predict transactional behavior with data tailored specifically tothe enterprise 23200 and characteristics of its assets (such termincluding, except where context indicates otherwise, assets controlledby the enterprise as well as other assets that may be involved in theworkflows of the enterprise, such as assets being pursued foracquisition, borrowing, lending, or the like). In some examples, thelearning models may train first from a larger corpus of training data(e.g., public training data set) and then undergo a fine-tuning processthat trains with a specialized data set that is particular to digitalenterprise assets. In these examples, the weights or biases that areconfigured during the first stage of training with the larger corpus maythen be fine-tuned or adjusted during the second stage. In someexamples, the fine-tuning of the second stage also assists to prunenodes that have low impact on enterprise-specific data that would nothave been pruned by solely training with the larger corpus. In otherwords, the enterprise-specific data of the second stage of training thatfine-tunes the model reduces nodes that do not influence (e.g., theprobability) a transaction event regarding an enterprise digital asset.

In some configurations, the intelligence system 23330 includes one ormore modules that function to gather data for purposes of training amodel of the intelligence system 23330. For example, the intelligencesystem 23330 includes data pipelines that include data thatcharacterizes digital enterprise assets that are available in a walletsystem (e.g., the wallet system 23350), data that characterizeshistorical, current or predicted state/status data about entitiesinvolved in enterprise transactions or workflows, data thatcharacterizes historical, current or predicted state/status data aboutenterprise assets or resources, or the like. In some examples, thesemodules that function to gather data for purposes of training a model ofthe intelligence system 23330 gather, derive, or generate training datafrom information associated with one more EAL systems. For instance, thetraining data may be governance/compliance information, such as rules,that can be used to develop models that provide decision-makingcompliance or predictive compliance. In this example, thegovernance/compliance data may be translated into enterprise-specificdata for the second state of training when the governance/compliancedata is specific to the enterprise.

In some implementations, each model, module, service, or the like of theintelligence system 23330 may correspond to a particular marketplace23122 or type of marketplace 23122. For instance, the training data totrain a marketplace's specific model may consist of transactional datafor that marketplace 23122 or type. By having a model that is specificto a particular marketplace 23122 or type, the model can be capable ofpredicting transactional information or transactional events for themarketplace 23122 or type. Therefore, the EAL 23300 can leverage theprediction from the model to inform transactional actions for a digitalenterprise asset available to the particular marketplace 23122 or type.

In embodiments, the intelligence system 23330 may include searchfunctionality, such as enabling searching for assets within a wallet ofthe enterprise or searching within other data resources of theenterprises for assets that may be appropriate for inclusion in thewallet. The search function may use similarity algorithms (e.g., k-meansclustering, nearest neighbor algorithms, or others) to discover assetsthat may be of interest by virtue of similarity to other transactedassets and/or ones presented in a wallet. A search algorithm may betrained, such as based on outcomes of transactions or enterprise or useractions, to identify relevant assets for wallet inclusion and or toidentify relevant assets within a wallet for a possible transaction. Inembodiments, the search functionality may enable recommendations, suchas recommendations of assets for inclusion in wallet, for inclusion in atransaction, for presentation, or the like. Recommendations may, inembodiments, be based on algorithms, including clustering and similarityalgorithms that recommend similar transactions to similar parties or thelike, collaborative filtering algorithms in which users indicatepreferences as to types of assets or transactions and based thereon areassociated with other similar users whose actions and transactionsinform recommendations, deep learning algorithms, that are trained ontransaction outcomes, and many others.

In embodiments, the intelligence system 23330 may facilitateprioritization, such as by alignment of functions and capabilitiesaccording to a set of prioritization rules, such as rules thatprioritize certain enterprise entities (such as particular workgroups),that prioritize certain types of transactions (such as time-sensitivetrading versus long-term resource acquisition), or the like. Inembodiments, the prioritization rules may be linked to and/or derivedfrom a set of enterprise plans, such as strategic plans, resource plans,or the like. This may include optionally translating a set of strategicor resource goals into a set of priorities that are applied as rules totransactions. In embodiments, prioritization rules are dynamically andautomatically updated based on changes to resource plans, strategicplans, or the like by virtue of integration between the intelligencesystem 23330 and one or more enterprise planning systems. For example,if a resource plan indicates a need to acquire a critical input resourcefor an operating function, the intelligence system 23330 may prioritizediscovery of candidate sources for that resource. As another example, ifa strategic plan indicates a need to dispose of an asset to reduceexposure to market volatility, the intelligence system 23330 mayprioritize presentation of the asset in wallet or other interface inorder to facilitate rapid disposal of the asset.

Additionally, or alternatively, the intelligence system 23330 may becapable of configuring other EAL systems (e.g., via an intelligenceservice controller shown in FIG. 232 ). For example, the intelligentfunctionality of the intelligence system 23330 may provide configurationdetails or configuration inputs to other EAL systems. When theintelligence system 23330 configures other EAL systems, the intelligencesystem 23330 enables the EAL 23300 to operate autonomously orsemi-autonomously. That is, the EAL 23300 is capable of operatingwithout human intervention (i.e., autonomously) such that the EAL 23300coordinates, controls, and/or executes transactions regarding digitalenterprise assets on its own accord. Configuration itself may beautonomous, such as using robotic process automation (where an agent istrained to undertake configuration based on training on a set of expertconfiguration actions), by learning on outcomes, or by other learningprocesses described herein or in the documents incorporated herein byreference.

In some configurations, a set of models of the intelligence system 23330functions to predict or recommend configurations for other EAL systemsof the EAL 23300. That is, each EAL system may have a configurationprotocol that includes parameters that enable a respective EAL system toperform a particular function. Here, a model of the intelligence system23330 may be trained to generate an output that serves as aconfiguration parameter for an EAL system. In this respect, one or moremodels of the intelligence system 23330 may be used to generatepredictions or recommendations to configure one or more EAL systems toperform a particular transaction for an enterprise digital asset.Prediction of configuration of one EAL system can be used in theconfiguration of another EAL system, such as to harmonize configurationsacross the systems (e.g., to allow development of a logical or efficientsequence of transactions that are governed by the respective systems, toallow effective coordination of EAL resource utilization, to avoidconflicts (e.g., where different systems seek to undertake inconsistentactions with respect to the same resource or asset), or the like.Additional examples of intelligence systems and services are describedelsewhere in the disclosure.

In embodiments, a workflow system 23340 is another EAL system of the EAL23300. A workflow system 23340 generally refers to a platform thatcombines several discrete workflow tools into a single application thatautomates processes involving machine and/or human tasks (e.g., in alinear sequence). The workflow system 23340 may integrate with othersystems (e.g., EAL systems) using APIs or via the interface system23310. To automate workflow processes, the workflow system 23340 mayinclude workflow definitions, workflow libraries, workflow optimization,and/or workflow management. For example, workflow definitions define theworkflows involved with any number of subject processes. For example, adefinition sets forth a series of tasks or actions to be performed in asequential manner to achieve a target end goal. Workflows may be linear(such as involving an invariant sequence of steps), contingent (such asfollowing a decision tree through a series of decision points thatdepend on inputs, such as defined by a directed, acyclic graph),looping/iterative (such as where steps are repeated until a threshold,goal or other conclusion is met), or a combination of the above.Workflow management tools may provide an infrastructure for the set-up,performance, and/or monitoring of a sequence of tasks that define agiven workflow. In some configurations, the workflow system includes aprocess builder that functions to build an automated process flow basedon pre-defined or configured business rules, transaction models, or thelike. In some examples, the workflow management includes form(s)designed to automate feedback or to generate dynamic data input from aworkflow or task within a sequence of the workflow. In some instances,the workflow management functions as a workflow engine that runs aworkflow system and/or makes decisions about the workflow automaticallybased on workflow rules. That is, the workflow management may executethe process built by a process builder. In some instances workflows maybe associated with policies, such that a policy is attached to and/orembedded into the workflow as a whole, or into individual steps of theworkflow. This may include access policies (e.g., which enterpriseentities are permitted to view, initiate, modify, terminate, orotherwise interact with workflow), resource utilization policies (e.g.,what resources a workflow can access, how and when), prioritizationpolicies (e.g., to resolve competition among workflows for resources),risk management policies, compliance policies, and many others. Policiesmay include enterprise governance policies, legal or regulatorypolicies, risk management policies, and many others. In embodiments,policies may also be embedded into or linked to processing nodes of theEAL, such that the processing node of an EAL resource is aware of theconditions under which it may be used, such as contextual conditions(including transaction and market conditions), network statusconditions, and many others. In embodiments, workflows or other EALprocesses or functions may be automatically governed or managed based onan enterprise resource plan or enterprise strategic plan, such as vialinking to, receipt from, or integration with an enterprise resourceplanning (ERP) system, such as where an EAL process or service onlyremains available for use within a budgeted or planned level ofutilization, or where a process, service, or workflow is prioritizedbased on a set of priorities embedded in a strategic plan, or the like.

In embodiments, a wallet system 23350 is an EAL system that supportsdigital transactions. The wallet system 23350 (also referred to as a“wallet” or “digital wallet”) can function in many ways akin to astorage device while also including increased functionality that allowsit to interface with other systems (e.g., EAL systems) especiallydigital or electronic systems. To support digital transactions, in someimplementations, the wallet system 23350 is configured to hold or tocontain (e.g., store) digital assets, such as enterprise digital assets,such as digital objects, tokens, or the like. In some examples, thewallet system 23350 functions as an index for digital assets such thatthe wallet system 23350 represents the status of digital assets withouthaving to store them. When used as an index, the wallet system 23350 maypoint to or reference the actual storage location of the digital asset(such as a bank account, stock exchange, custodial account, blockchain,distributed database, or the like). For instance, a digital asset thatis available for exchange in the wallet system 23350 may be actuallystored in data storage of the data services system 23320. Here, thewallet system 23350 may include some indication that the digital assetis available for exchange (e.g., an asset availability tag) along withinformation that the digital asset is stored in the data services system23320 (e.g., a storage location identifier) so that the digital assetcan be retrieved from the data services system 23320 to perform atransaction.

In some configurations, the wallet system 23350 also contains thedigitization of identity data. For instance, the wallet system 23350 mayhold identity data such as banking numbers, card numbers, coupons,tickets, credentials, tokens, tokenized assets, vital records, biometricdata, passwords, private keys, licenses, etc. For the enterprise 23200,this identity data may refer to identity information about theenterprise 23200 or information about one or more parties associatedwith the enterprise 23200 that is/are responsible for a respectivedigital asset. For instance, the identity data associated with an assetthat is available in the wallet system 23350 identifies information suchas the employee at the enterprise 23200 who made the digital assetavailable (e.g., an employee number or an employee name) or a departmentor business unit that the digital asset originated from at theenterprise 23200 or who is responsible for the digital asset. Identitydata may be associated with an identity management system or service, anidentity-as-a-service platform, or the like. Identity data for theenterprise may be managed based on a structure that represents a set ofroles, such as an organizational chart, such as represented by a graphstructure (optionally stored in a graph database) pursuant to which someroles are governed by other roles. For example, access layer accesspolicies and other capabilities may be based on the position of a rolewithin a hierarchy, such that access and other capabilities for a rolethat reports to another role are governed by the entity that holdssupervisory role. Role-based governance of workflows allows accesspolicies to be implemented based on the enterprise structure and rapidlyupdated in cases where the structure changes (e.g., a reorganization) orwhere individuals change roles.

The wallet system 23350 is also capable of managing, associating, orgenerating various date code information for a digital asset. Forinstance, the wallet includes a date code that defines the time at whichthe digital asset was created, a set of date codes for a window ofavailability for the digital asset, a date code that designates when thedigital asset was made available or added to the wallet, etc.

A wallet system 23350 generally includes at least one wallet as astorage resource (e.g., a partitioned container, a set of files, and/ora set of databases) for digital/electronic information. In this respect,a wallet may be software-based and referred to as a software wallet orphysical hardware and referred to as a hardware wallet (e.g., adedicated hardware storage device or location within a hardware device—ahardware wallet). Digital wallets, to some degree, have been used withcryptographic currency systems (also referred to as cryptocurrency). Insuch cases, a digital wallet may provide or serve as a digital ledgerthat includes references to the assets that are associated with thewallet, rather than being the actual holder of the asset. For instance,enterprise digital assets may be actually stored on a private storagesystem associated and/or controlled by the enterprise 23200. Here, ifone of these enterprise assets is associated with a wallet (e.g., madeavailable to market participants via a wallet), instead of transferringthe digital asset to the wallet during or following the association(e.g., moving the asset to a storage location dedicated to a wallet),the asset may remain in the private storage location while the walletincludes a record (e.g., an entry in a ledger) of the private storagelocation. In this configuration, the wallet maintains some type ofstorage address or identifier of the storage location for the asset(e.g., a type of pointer),

In digital transactions (e.g., wallet-based transactions), there doesnot necessarily need to be any movement of digital assets (e.g., achange of possession to pair with a change of ownership). Rather, theownership or controlling information associated with a digital asset canchange from one owner to another owner using data entry procedures. Forinstance, when a digital asset is exchanged from a first entity to asecond entity, the ownership information associated with the digitalasset is changed from the first entity to the second entity. This changemay occur by either overwriting the ownership information in datastorage (e.g., a database) or by appending data to non-overwritingstorage (e.g., adding blocks to sequential blockchain storage, such asin a distributed ledger that maintains transaction records that indicateownership transfers and other transaction details), in each case akin todeed or title recordation in tangible property, where the deed or titleregistry is a transaction ledger records a new deed event or record at alater time such that a timeline of the deed events can inform someone asto the changes in ownership over time. A blockchain for digital assetscan function similarly such that there is a first block at a first timethat indicates that the first entity owned the digital asset and then,when the digital asset is digitally “exchanged,” there is a second blockgenerated at a second time later than the first time that indicates thatthe second entity owns the digital asset. Accordingly, a query forinformation related to the digital asset (e.g., ownership information)would return two records that indicate a change of ownership from thefirst entity to the second entity. In this sense, when the word“exchange(d)” is used with respect to a digital asset, it can mean thatthe ownership or controlling information of a digital asset is modifiedwithout necessarily moving the digital asset in any way. While the assetmay remain in place, control may pass to the different owner; forexample, an asset may subsequently be managed (e.g., transferred) onlyby the valid owner who possesses the private key that is needed toinitiate a transfer. However, it is also still possible that the“exchange” of a digital asset can encompass some form of digital orphysical movement, such as changing the physical storage locations forthe digital asset, such as by locating the digital asset in a wallet orother storage location where only the owner of the wallet or storagelocation has the ability to interact with or transfer the asset.

When the wallet system 23350 creates or initializes a wallet, thatwallet may be unique from other wallets in that it has its own set ofunique digital keys. In some examples, the wallet system 23350 oranother system of the EAL 23300 may generate the set of unique keys forthe wallet when the wallet is created or configured. These digital keyscan allow the functionality of the wallet to act on behalf of a specificentity (e.g., the enterprise or an enterprise entity, or a set of roleswithin the enterprise) to perform or orchestrate digital transactions.In other words, to execute a digital transaction such as an ownershipchange, a unique key associated with wallet signs off ownership to thewallet's address that is dictated by another key (e.g., a key that iscryptographically related to the unique key signing off ownership). Inthis sense, digital keys are able to serve as ownership attestation suchthat trust, control, and security is present for a digital transaction.These digital keys may be independent (e.g., completely independent) ofother digital protocols and can be generated with or withoutconsideration for particular storage schemes (e.g., agnostic to aparticular storage structure like a blockchain or designed for aparticular storage structure). Digital keys may be managed by a keymanagement platform, may be based on identity of users (e.g., identitiesof a set of roles, such as in a hierarchy of roles), and the like.

As an example, with wallets configured for cryptocurrencies, the set ofdigital keys functions as secure digital codes needed to interact with ablockchain. Here, for instance, the blockchain stores the currency andthe wallet uses one or more keys from the set (e.g., a public key) tolocate the currency stored on the blockchain that is associated with thewallet (e.g., to locate the currency with the wallet's address). Withthe location of the currency, the wallet or an entity facilitating thewallet may perform actions using the currency (e.g., exchanging thecurrency) and authorize those actions with one or more keys from theset. For example, the wallet performs transactions with the assets andrecords those transactions on the blockchain or some other digitalledger by using the set of keys. In this sense, digital keys canfunction as an account and/or an identity to authorize the wallet toperform actions (e.g., on behalf of an entity).

In some examples, each wallet is associated with a pair of cryptographickeys as the set of digital keys. In these examples, one key of the pairmay be considered a public key while the other key is considered aprivate key. Here, a public key refers to a cryptographic key (e.g., analphanumeric string) associated with a particular entity (e.g., awallet) that is outward facing such that it may be published and sharedwith other entities to function as a public unique identifier or addressfor the particular entity. In other words, the public key may beassociated with a digital asset to indicate publicly (or to those whocan view the digital asset) who or what controls and/or owns the digitalasset. In contrast, a private key refers to a cryptographic key (e.g.,an alphanumeric string) that is generally associated with the sameentity of the public key, but is kept as a secret. Here, instead of anaddress function like the public key, the private key may serve as aform of digital signature (e.g., like a unique password) that provesthat the entity associated with the key has the authorization to performa transaction. In other words, the holder of the private key can serveas the controller for performing digital transactions.

The public and private key may be linked to each other in that thepublic key may be generated from the private key. For example, a randomnumber generator (or alphanumeric generator) generates a private key ofX length and then, from the private key, a one-way cryptographicfunction generates the public key. In some implementations, the publickey and private key operate in tandem such that the public key providesan address or destination for the private key holder such that a marketparticipant can request authorization of the private key holder toexecute a transaction. In some examples, this cooperation is such thatthe public key assigned to a wallet must match or prove its relation tothe private key to authenticate an asset transaction. Here, thismatching may be considered a form of verification for the transaction.In these examples, the public key may be able to “match” or exhibit arelation with the private key because the public key has been generatedfrom the private key.

In some configurations, the wallet uses a derivative form of the privatekey (e.g., a one-way hashing function) as a digital signature toauthorize a transaction. Since the private key can authorizetransactions on behalf of the owner/controller of a wallet, if anefarious party obtained the private key, that nefarious party couldremove or disassociate all of the assets from a wallet; thus, stealingassets. Therefore, the security of the private key for a wallet can becritical to the security of the assets associated with a wallet. Forreasons such as this, it may be advantageous to authorize a transactionwith a form (e.g., a cryptographic function) of the private key thatindicates that the authorizer (e.g., the entity digitally signing atransaction with the form of the private key) has/controls the privatekey, but that does not reveal the actual private key to another party.

In some implementations, securing the authorizing key, such as theprivate key, depends on the security of the wallet itself. This may bethe case when management and/or storage of the private key occurs at thewallet. For example, the wallet stores the set of keys including theprivate key. When a wallet stores the authorizing key, the wallet system23350 may use a variety of security techniques to secure the authorizingkey. For example, the wallet system 23350 may configure the wallet as acustodial wallet or non-custodial wallet. A custodial wallet generallyrefers to a wallet service where custody or digital possession of thewallet is outsourced to a third-party service who provides security forthe wallet (or keys associated with a wallet). In some examples, togenerate a custodial wallet, the wallet system 23350 transfers the oneor more keys of the set of keys (e.g., the private key) to the custodianservice provider. In some situations, custodial services may offer agreater degree of protection because a custodian service provider mayhave key security expertise. At the same time, the owner of the wallet(e.g., the enterprise 23200) has to trust the custodian with securityresponsibility. In some configurations, a custodian service provider maybe considered the same as or akin to a key management service (KMS).

In contrast, a wallet may be a non-custodial wallet. A non-custodialwallet refers to a wallet that is not outsourced to a custodian serviceprovider. An enterprise may prefer to use non-custodial wallets when,for example, the enterprise lacks trust in a custodial service provideror perhaps foresees there being a risk of censorship (e.g., limiting thetype of transactions or transactions generally for some period of time)from a custodian service provider.

In addition to a wallet being custodial or non-custodial, a wallet mayalso be considered a “hot” wallet or a “cold” wallet. A hot wallet is awallet that is connected to a gateway to perform transactions. Forinstance, the gateway is a wide area network (WAN) such as the internetand the hot wallet is a wallet that is connected to the internet. Someexamples of hot wallets include web-based wallets, mobile wallets, anddesktop wallets. Since a hot wallet is hot or online with the ability toperform transactions, a user of a hot wallet is able to directly issuetransactions, for example to a blockchain, in a relatively easy fashion.For this reason, it may be preferable to use a hot wallet for keys thatare frequently used for transactions or keys that have low risk of loss(e.g., keys used with only a particular threshold value of assets).Unfortunately, with this ease of use, the keys associated with the hotwallet are generally vulnerable to threat by the mere fact that theyexist online (e.g., connected to the internet).

On the other hand, a cold wallet refers to a wallet that is keptoff-line or disconnected from a gateway to perform transactions. Bybeing disconnected from a gateway (e.g., the internet), the cold walletminimizes potential vulnerability attacks. A cold wallet may anystorage-capable device that is disconnected or offline from marketplacetransactions (e.g., not connected to the internet) including a simplesheet of paper with the keys printed on the paper. When using a set ofkeys for a transaction that is stored in a cold wallet, the user maytemporarily connect the cold wallet to the transaction gateway andprovide the necessary keys prior to disconnecting the cold wallet fromthe gateway. Since a cold wallet is capable of being online, in someinstances, what defines the cold wallet is that it is generally offline(e.g., offline a majority of the time) and/or offline at the time when atransaction is requested for an asset associated with the wallet.

In some situations, the user does not connect the cold wallet, butrather accesses the offline keys and transfers them manually or by atransfer operation (e.g., cut and paste) for execution of thetransaction. In some configurations, the transfer operation copies thekeys from a cold wallet to a hot wallet to perform the transaction. Inthese configurations, the keys transferred to the hot wallet may beassigned a time of life (e.g., a temporary lifespan to consummate thetransaction) when transferred or otherwise undergo a removal procedurefollowing the execution of the transaction such that the hot wallet doesnot retain the keys. In other configurations, a transaction may use acombination of a hot wallet and a cold wallet. For instance, thetransaction is signed entirely on the cold wallet while the hot walletis used to issue/relay the signed transaction (e.g., to the blockchain).Due to the nature of cold wallets, cold wallets may be better suited forkeys that met a certain security threshold (e.g., a security clearanceor designated authorization level) or for keys that are infrequentlyused.

In some examples, whether the wallet system 23350 uses a hot wallet or acold wallet depends on the value of the asset associated (or to beassociated) with the wallet. For instance, the enterprise 23200 may seta threshold asset value for an individual asset that, if exceeded, mustbe stored in a secure cold wallet rather than a hot wallet. Similarly,if the asset value is below the threshold asset value, the EAL 23300 mayassociate the asset with a hot wallet. In some examples, whether thewallet system 23350 uses a hot wallet or a cold wallet depends on thecumulative value of the assets that are to be available for a givenwallet. In other words, rather than the threshold asset value being athreshold for the value (e.g., estimated value) of a single asset, thethreshold dictates when a hot or cold wallet should be used based on theaggregate value (e.g., estimated value) of the collection of assets thatare or will be associated with the wallet.

In some configurations, a wallet of the wallet system 23350 has a keybackup protocol to safeguard keys and to prevent assets from beinginaccessible due to lost or mismanaged keys. In some examples, the typeof wallet or value of the set of assets associated with the walletdictates the key backup protocol for the keys associated with thewallet. Some examples of key backup protocols include: (i) storing acopy of the set of keys in a designated private storage locationassociated with the enterprise 23200 (e.g., backup on enterpriseresources); (ii) having an agent or employee store a copy of the set ofkeys in a hardware device such as a Universal Serial Bus (USB) orhardware wallet; or (iii) storing a copy of the keys with a key servicemanagement (KSM) system (e.g., a third-party provider). As an example, aparticular protocol may be associated with a backup level. For instance,a first backup level may be associated with the key backup protocol (i)while a second backup level is associated with the key backup protocol(ii). Therefore, when a backup level for a wallet is satisfied, the keybackup protocol associated with the backup level is implemented as thekey backup protocol for the wallet. For example, the first backup levelis that the estimated value of the set of assets associated with thewallet is greater than X but less than Y. Here, when this is true, thekey backup protocol of (i) that has been associated with the firstbackup level is implemented as the key backup protocol for the wallet.In this situation, the key backup protocol for the wallet is that a copyof the set of keys is stored in a designated private storage locationassociated with the enterprise 23200.

In some implementations, the wallet system 23350 has the ability tomanage and/or to generate a plurality of wallets. Having a plurality ofwallets may be advantageous to partition or sandbox some digital assetsfrom other digital assets. In other words, the wallet system 23350 maygenerate multiple wallets that have specific attributes. When a digitalasset is received by the wallet system 23350, the wallet system 23350 isconfigured to determine a set of attributes of the digital asset and tomatch the determined attributes to one or more of the plurality ofwallets. For instance, a wallet may be dedicated to a particularmarketplace or business field. Here, in response to receiving a digitalasset that includes attributes that correspond to the particularmarketplace or business field, the wallet system 23350 associates thedigital asset with the wallet that shares or matches those attributes(e.g., exact match or a fuzzy match) and thus associating the digitalasset with the wallet that also corresponds to the particularmarketplace or business field.

As an example, the wallet system 23350 receives two digital assets thatare designated as available digital assets. Upon receiving each digitalasset, the wallet system 23350 determines that the first digital assethas a first set of attributes that define the first digital asset as acorporate bond and the second digital asset has a second set ofattributes that define the second digital asset as an insurance policydata set. In this example, the wallet system 23350 determines that thefirst set of attributes matches or shares the most attributes withattributes defined for a financial asset wallet. Based on thisdetermination, the wallet system 23350 associates the corporate bondwith the financial asset wallet. In some implementations, to associatethe digital asset with a particular wallet, the wallet system 23350generates an identifier such as a label or a tag for the digital assetthat indicates the wallet that the digital asset has been assigned to.In some examples, by having an associated identifier, digital assets canbe stored together regardless of their attributes, but yet also beretrieved or managed based on the identifier.

In some embodiments, a wallet system 23350 may be configured such that awallet holds another wallet (i.e., a “wallet-of-wallets”), such that inorder to access a “child” wallet, an entity must access the “parent”wallet that contains the child. For example, resources managed by aworkgroup may be contained in a set of wallets that can be accessed byemployees within the workgroup, but only if the manager of the workgroupwho controls the parent wallet provides access (such as through a set ofkeys for the parent wallet). In embodiments, multiple layers of walletsand sub-wallets may be provided in a hierarchy, such as ones containingall assets, all assets of a given type (e.g., financial, cryptocurrency,non-fungible tokens, intellectual property, or the like), assetscontrolled by a given workgroup, assets related to a particularmarketplace or exchange, or the like. A wallet-of-wallets can addressthe need for multiparty access control within an enterprise, such aswhere primary control of wallet usage needs to be governed by asupervisor, such as a manager.

In some configurations, the wallet system 23350 also functions as ameans for orchestrating digital asset transactions. To orchestrate adigital asset transaction, the wallet system 23350 may be configured tointegrate and/or to manage payment processes that are associated with adigital asset transaction. For example, as more enterprises becomeglobal or multi-regional market participants (e.g., a multi-regionalmerchant), it is quite likely that these enterprises need to processlocalized payments from an exchanging party (e.g., from a client or acustomer). For reasons such as this, the enterprise 23200 can integratewith multiple region-specific payment service providers (PSPs) via theorchestration functionality of the wallet system 23350. In embodiments,the orchestration functionality may be used to orchestrate currencyacquisitions and sales for a wallet, such as by automatically purchasingor selling a given currency based on an enterprise forecast of theamount of the currency that will be needed to achieve enterpriseworkflows or processes that will involve the currency. The forecast ofcurrency needs, which may be continuously updated, may be based on amodel of anticipated transaction workflows that are predicted based onhistorical transactions, current conditions (including market prices ofitems to be bought or sold using a currency), and enterprise plans(e.g., a transaction plan). Automated purchasing and sales may maintainan appropriate balance in the wallet of each currency anticipated to beneeded across a set of marketplaces or exchanges. Automation may beenabled by a model or other artificial intelligence, such as using anyof the learning and intelligence types described herein or in thedocuments incorporated herein by reference.

To facilitate a digital asset transaction, there may be several types ofpayment processes that need to be executed. For example, in some digitalasset transactions the payment processes may include paymentauthorization, transaction routing, and transaction settlement. In someexamples, in order to orchestrate these digital asset transactions, thewallet system 23350 is configured to electronically connect entitiesinvolved in these payment processes, such as PSPs, acquirers, and/orbanks and to communicate the appropriate information to these entitiesto facilitate/execute a transaction.

In some implementations, the orchestration of the wallet system 23350functions to optimize a digital asset transaction. For example, thetransaction optimization functions to determine a best payment route toconduct (e.g., send) a digital transaction. This best route may also bereferred to as a best or an optimal transaction rail. Here, the bestroute may depend on the type of digital asset (such as by selecting atransaction route or rail that is compatible with the asset), the volumeor size of the digital transaction (such as by selecting a transactionrail that is capable of handling the volume, one that provides avolume-based benefit, such as a discount, credit, or reward, or thelike), the format of the digital transaction, the location of thetransaction (e.g., the destination of the transaction and/or source ofthe transaction), the financing of the digital transaction, the cost ofthe digital transaction (including transaction cost, borrowing cost,processing costs, costs of energy, and the like) and/or the currencyinvolved in the transaction, among others. As an example, an acquirer ofa digital asset (e.g., a market participant 23110) may indicate thatthey desire a particular digital asset that is available in a wallet ofthe wallet system 23350. In that case, the acquirer may select a digitalasset listed or somehow indicated as available to the interestedacquirer. Here, the selection may be coordinated by a transactionfacilitator (e.g., an e-commerce interface) that facilitates thetransaction for the identified digital asset.

In some implementations, in addition to the selection of the digitalasset, the acquirer includes or selects details about the transactionfor the digital asset. To illustrate, an exchanging party (e.g., one ofthe buyers 23112, one of the sellers 23114, or the enterprise 23200) maydictate their preferred payment method (e.g., selected from a list ofpayment methods that the merchant accepts) using the transactionfacilitator. In some implementations, the details about the transactioninclude terms for the transaction, such as transfer terms (e.g.,shipping terms), payment terms (e.g., net 30/60/90), interest terms,licensing terms, or other contract terms (e.g., representations and/orwarranties). With the transaction details, the wallet system 23350 maybe configured to orchestrate the transaction using a payment ortransaction gateway. In some configurations, the wallet system 23350 oranother system (e.g., a third-party payment system) encrypts/decryptssome portion of the transaction details (e.g., payment information suchas card numbers, routing numbers, communication addresses, etc.) priorto or during communication of the transaction detail to a PSP.

In some configurations, the wallet system 23305 configures thetransaction details in order to orchestrate a transaction for anenterprise digital asset. When configuring the transaction details, thewallet system 23350 may specify transaction details that represent theinterest of the enterprise 23200. In some situations, to represent theinterest of the enterprise 23200, the wallet system 23350 generatestransactions details by use of one or more models of the intelligencesystem 23330. For instance, a model of the intelligence system 23330 maybe trained using historical enterprise transaction data to generate arecommendation or prediction of transaction details the enterprise 23200would prefer for a particular enterprise digital asset, which may befurther based on current enterprise conditions (including enterpriseresource plans, transaction plans, strategic plans, policies, and thelike), market conditions, and other contextual information. Arecommendation or prediction may be further used to configure a set ofinstructions to initiate the transaction, which may be automaticallyinitiated or triggered by an authorized entity. To illustrate, for aparticular asset, the wallet system 23350 determines a payment method orpayment rail for a transaction involving the particular asset. Someexamples of payment methods include clearing houses (e.g., AutomatedClearing House (ACH)), credit card providers (e.g., MASTERCARD®, VISA®),online payment systems (e.g., PayPal®), Real-time Payment (RTP) Network,blockchains, the Society of Worldwide Interbank FinancialTelecommunications (SWIFT), and Single Euro Payments Area (SEPA). Thewallet system 23350 may automatically determine which payment method touse based on characteristics regarding the asset (e.g., assetattributes), the parties involved in the transaction, the location ofthe transaction, and/or the currency of the transaction.

In some implementations, the wallet system 23350 (and/or other EALsystems) may be configured with an awareness for transactions acrosssets of assets. For example, in some embodiments, the wallet system23350 may be configured to identify transactions which would be moreefficient to combine or divide. For instance, the wallet system 23350can determine that instead of selling a first asset in a firstmarketplace and a second asset in a second marketplace, the enterprise23200 would receive the most value for these assets by bundling thefirst and second asset together with a third asset and selling thesethree assets as a package in one of the marketplaces or a thirdmarketplace. Similarly, the wallet system 23350 may combine acquisitionsby packaging multiple acquisitions for different enterprise entities orworkflows into a bundle, such as to access volume discounts or otherrewards. In other cases, unbundling purchases or sales may providebenefits, such as where discounts are offered for new or trial users ofa set of marketplaces or exchanges up to a maximum threshold oftransaction value. In other words, with the wallet system 23350 beingable to track multiple available assets (including ones desired to beacquired) for the enterprise 23200, the wallet system 23350 can likewiseleverage combination or disaggregation of assets to engage in complextransactions that benefit the enterprise 23200 more than unmanagedtransactions with the assets. As another example, the wallet system23350 can operate with supply-side knowledge for the enterprise 23200(e.g., the supply rate for enterprise digital assets) while alsotracking current and past demand-side knowledge across multiplemarketplaces for assets that have characteristics, properties, orattributes similar to enterprise assets used in historical workflows inorder to generate a recommendation, prediction or instruction aboutfurther acquisition. This may further include adjusting therecommendation, prediction or instruction based on an enterprise plan,contextual conditions, or the like.

Another transaction detail that the wallet system 23350 is capable ofdetermining is payment details. Here, one type of payment detail thatthe wallet system 23350 may coordinate or control is the type ofcurrency that is exchanged and/or when the exchange involving anenterprise digital asset occurs using a particular currency. Determiningthe type of currency or the timing of a transaction with a particularcurrency may allow the wallet system 23350 to have another approach tooptimize value for a transaction. For instance, the value of differenttypes of currencies is capable of fluctuating based on marketconditions. That is, conversion rates or exchanges rates may bedetermined by a floating rate that depends on market forces of supplyand demand for foreign exchange or a fixed rate. Due to the fluctuationof conversion rates, the timing of when a transaction occurs can dictatethe buying power or selling power of an asset. To illustrate, if theUnited States Dollar (USD) has an exchange rate greater than one withrespect to the British Pound, then the USD, at that time has greaterbuying power than when the USD has an exchange rate less than one withrespect to the United Kingdom Pound. In other words, with a ratio overone, the USD gets a greater return in British Pound than with a ratioless than one. Therefore, if a transaction for a US enterprise 23200 wasgoing to occur in British Pounds (e.g., with a British marketparticipant), the wallet system 23350 may track the conversation ratesand/or facilitate the execution of the transaction at a time within aparticular transaction window (i.e., a permitted time period to executethe transaction) that is most advantageous to the US enterprise (e.g.,when the USD has the greatest buying power). To facilitate suchactivity, the EAL system may access a set of predictions of currencyconversion rates, such as one generated based on market factors, such aseconomic data for respective jurisdictions, central bank interest rates,and the like.

Additionally or alternatively, the wallet system 23350 may be capable ofidentifying a marketplace 23122 or a market participant 23110 operatingin a specific marketplace 23122 that enables the conversion rate to befavorable to the enterprise 23200. As an example, the wallet system23350 may have an enterprise digital asset that is available anddemanded by a set of candidate marketplace participants 23110 (e.g.,that operate in one or more marketplaces 23122). For each candidatemarketplace participant 23110 in the set, the wallet system 23350determines the preferred currency that would be exchanged with thatmarketplace participant 23110 for the asset and the conversion rate withrespect to a particular base currency (e.g., the default or standardcurrency for the enterprise 23200). Here, the standard currency for theenterprise 23200 generally refers to the currency that the enterprise23200 typically holds or predominantly uses. For instance, a US companytends to have a standard currency of USD. In response to thedetermination of the preferred exchange currency and its associatedconversion rate, the wallet system 23350 selects the candidate marketparticipant 23110 with the most favorable conversion rate as themarketplace participant 23110 with which to perform the transaction.

In some configurations, with the selected marketplace participant 23110,the wallet system 23350 may then further optimize the transaction bydetermining an exchange time within a transaction window where theconversion rate is most favorable to the enterprise 23200. In thissense, the wallet system 23350 is not only capable of tuning atransaction to a favorable currency, but also fine tuning thetransaction to occur at a time where that currency has a most favorableconversion rate. This therefore may enable a two-stage optimization ofthe transaction. In other implementations, when the wallet system 23350identifies a time for the transaction to occur in a particular currency,the wallet system 23350 performs an assurance check to ensure that othercurrencies have not suddenly become a more valuable transaction currencyfor the enterprise 23200 due to market fluctuation. If, during theperformance of this assurance check, the wallet system 23350 determinesthat another currency associated with another one of the candidatemarketplace participants 23110 has a more valuable conversion rate thanthe selected candidate marketplace participant 23110, the wallet system23350 may instead perform the transaction for the asset with the othercandidate marketplace participant 23110.

Similar to currency, the wallet system 23350 may perform transactionsaccounting for other factors, such as environmental factors, marketconditions, economic conditions, or weather conditions. For example, ifthe exchange of a digital asset is associated with a physical good, thewallet system 23350 can coordinate transaction details, such as shippinglogistics or the timing of the performance of the transaction, based oninfluencing factors such as environmental factors, weather factors,and/or political factors. For instance, if the enterprise 23200 is awarethat a network is going to be offline for maintenance, the wallet system23350 can recognize this upcoming event, and adjust transaction detailsbased on the recognition (e.g., schedule the transactions to occuroutside the time when the network is offline). Similarly, if a resourceor asset needed by the enterprise is subject to consistent seasonal orother periodic variations in price or availability, the wallet system23350 can coordinate transactions to acquire the resource or asset at afavorable time (such as during an annual promotional event of asupplier). In embodiments, an acquisition or disposition plan of anenterprise, or instructions derived therefrom, may be linked to orintegrated with or into the wallet system 23350, such that the walletsystem 23350 is configured to optimize, and then execute, a series oftransactions that accomplish the plan (acquisition of needed resourcesand assets and disposition of others) while optimizing timing and othertransaction parameters as noted above.

In some examples, the wallet system 23350 links to or is integrated withan e-commerce engine that includes one or more interfaces. Theseinterfaces may refer to software modules that execute on hardware toprovide a portal or graphical user interface (GUI) to interact with thewallet system 23350. That is, the GUI may be designed such that the GUIrepresents the wallets of the wallet system 23350 and the functionalitythat is accessible to a particular entity interacting with the EAL23300. In some examples, the wallet system 23350 includes an interfacefor each type of entity that has access to the EAL 23300. In otherwords, an entity of the enterprise 23200 may use an enterprise interfaceof the wallet system 23350 to facilitate the functionality of the walletsystem 23350 for enterprise-based activities (e.g., submitting anenterprise asset available or facilitating transaction details on behalfof the enterprise 23200 for an asset). Similarly, the wallet system23350 may have a marketplace participant interface separate from theenterprise interface that functions to facilitate actions in the walletsystem 23350 that are available to the marketplace participant 23110.For instance, the marketplace participant interface may include ane-commerce shopping interface to discover what assets are available fortransactions, a checkout interface such as a shopping cart as a means tostage a series of assets for purchase, or the like.

In some implementations, instead of having multiple interfaces, thewallet system 23350 uses a single interface that is capable ofidentifying a user of the interface and configuring, presenting orrendering a GUI that matches the access and/or wallet activitypermissions associated with the user. In this sense, the singleinterface is capable of restricting a user from accessing or executingthe functionality associated with windows, menus, or other GUI elementsthat are tied to certain wallet-based activities that should not beaccessible to a particular user. For instance, the GUI elements mayinclude an identifier that designates the access permissions required torender the element for display. In this instance, at runtime, walletsystem 23350 determines the access permissions associated with a userand renders the GUI elements that satisfy or match the determined accesspermissions. For example, a purchasing manager in charge of acquiringsemiconductor chips may be presented GUI elements that display data frommarket participants who offer them while not being presented with GUIelements for other goods or services. In this respect, regardless ofwhether the wallet system 23350 uses one or more interfaces, the userexperience (UX) of the interface(s) for the wallet system 23350 differsdepending on the entity that is using the interface(s), such that GUIelements and their rendering is tied to access controls and permissionsfor the wallet system 23350.

Although the wallet interfaces are described with respect to anenterprise entity and a marketplace participant 23110, the walletinterfaces are capable of managing access to the wallet system 23350(e.g., wallets of the wallet system 23350) at a more granular level suchthat one enterprise entity may have access to some wallets while anotherenterprise entity may have access to a different set of wallets (e.g.,which may include access to at least one of the same wallets).Similarly, a marketplace participant 23110 (e.g., from a firstmarketplace 23122) may have access to some wallets (e.g., a first set ofwallets) while another marketplace participant 23110 (e.g., a secondmarketplace 23122 different than the first marketplace 23122) has accessto a different set of wallets (e.g., which may include access to atleast one of the same wallets). In this manner, the access to the walletsystem 23350 can be managed not only at the enterprise/non-enterpriselevel, but also at the entity level.

As illustrated in FIGS. 231 and 232 , the EAL systems of an EAL 23300may also include a governance system 23360. In some implementations, thegovernance system 23360 is a means to comply with various rules (e.g.,laws, regulations, standards, and/or practices) that impact anenterprise digital asset and transactions regarding the enterprisedigital asset. These rules may be government-imposed rules (e.g., lawsor regulations), industry-imposed rules (e.g., industry standards orspecifications), enterprise-imposed rules (e.g., dictated by anenterprise's code of conduct, mission statement, governance purpose), orconsumer-imposed rules (e.g., rules dictated by consumer advocacy groupsor consumer watchdogs). For instance, some types of assets may havetesting standards that have to be met for the asset to be considered anexchangeable asset. In some examples, the governance is market-specific,such that a specific market has requirements that a market participantneeds to satisfy in order to participate in the marketplace. Other typesof governance include financial governance, legal or regulatorygovernance, risk governance, ethical governance and custom governancethat may be set by a participating party of a transaction and/or anexternal entity, such as an operator of a marketplace or exchange, aregulatory body or the like. In order to enforce, monitor, and/or trackthe governance for an enterprise asset, the governance system 23360 mayinclude any number of libraries that include relevant polices,compliance rules or the like for resources, assets or activities of theenterprise 23200. In embodiments, the libraries may include parametersthat define or otherwise correspond to certain rules and/or scenarios.

In some configurations, when an enterprise digital asset is madeavailable in the wallet system 23350, the governance system 23360identifies any governance that is applicable to the asset. Anyidentified governances may be indicated in information associated withthe asset. In some situations, the governance system 23360, besidesmerely identifying applicable governance, is configured to determinewhether the asset complies with the identified governance. Here, forexample, if the asset complies with the identified governance, the assetis made fully available to outside marketplace participants 23110 (e.g.,via marketplaces 23122). On the other hand, in some implementations, ifthe asset fails to comply with the identified governance, the asset maybe removed from transactional availability.

In some instances, an asset that fails to comply with governanceparameters may be offered at some reduction of value that isproportional to the severity of the compliance failure. In some of theseinstances, an asset that fails comply with governances may be flaggedand include information that identifies the failure such that any suchfailure is conspicuous to a potential customer or investor in the asset.Here, this allows the asset to stay available, but the risk to be borneby the customer or purchaser is displayed in a transparent fashion. Inthese instances, the governance system 23360 may generatefault-identifying information that includes a disclaimer or theprominent inclusion of contract terms for the transaction.

FIGS. 231 and 232 also illustrate that the EAL 23300 may include apermissions system 23370. In embodiments, a permissions system 23370 mayfunction as a system for the EAL 23300 that assigns, manages, and/orfacilitates access controls and permissions for the EAL 23300. In thissense, the permissions system 23370 is capable of performing accesscontrol activities for the other EAL systems associated with the EAL23300. In other words, the permissions system 23370 can be configured tofield permission-based or access requests received by any EAL system.For instance, in response to receiving a request to access the walletsystem 23350 via a wallet interface, the permissions system 23370 can beinformed of the request and determine a set of permissions associatedwith the requesting entity. Here, once the permissions system 23370identifies the set of permissions or access controls associated with therequesting entity, the permissions system 23370 may communicate thesepermissions to the wallet system 23350 to enable the wallet system 23350to render the appropriate wallet interface for the requesting user.

The permissions system 23370 may be configured to assign one or morepermissions to a user of the EAL 23300. A permission generally refers toa rule that defines access to various portions (e.g., functions) of theEAL 23300. Permissions dictate access parameters in order to control whoor what is authorized to access resources. Therefore, permissions aretraditionally used to secure resources by permitting who, what, when, orhow a resource can be utilized. In some examples, the permissions system23370 uses access controls or access control lists (ACLs) to managepermissions that are associated with various users of the EAL 23300.These access controls may be discretionary access controls (e.g.,managed by business stakeholders of the enterprise 23200), mandatoryaccess controls (e.g., access controls that are deployed to comply withrequired security protocols for a resource), or role-based accesscontrols (e.g., access controls that correspond to a user's role in theEAL 23300).

As shown in FIG. 232 , in some examples, the permissions system 23370 iscapable of managing (e.g., assigning, modifying, removing) permissionsthat are privacy-based rules. That is, an enterprise asset managed bythe EAL 23300 may pose privacy concerns. For instance, the enterpriseasset (e.g., a medical record) may include personal/protected healthinformation (PHI) which dictates who and/or how a user of the EAL 23300may interact with that asset. To illustrate, an enterprise entitysubmits an enterprise asset that includes PHI to the wallet system23350. Here, the entity may include an indication that the assetincludes private or sensitive information or the EAL 23300 (e.g., viathe wallet system 23350) determines that one or more attributes for theasset indicate that the asset pertains to private or sensitiveinformation. Based on this determination and/or the precise attributeidentified, the permissions system 23370 applies one or more permissionsthat correspond to a privacy rule implicated by the determination orattribute.

In some implementations, a privacy rule may dictate not only what typesof users should access an asset, but also if further processing by theEAL 23300 should occur prior to making the asset available for amarketplace participant 23110 (e.g., in a wallet of the wallet system23350). For instance, certain assets that include sensitive informationmay trigger a permission that requires the asset or information includedwith an asset to be encrypted (e.g., prior to availability of thatasset). In this instance, the permissions system 23370 determines thatthe implicated permission for the asset indicates that the asset (or aportion thereof) should be encrypted. In some configurations, thepermissions system 23370 generates an encryption request for the dataservices system 23320 to enable the data services system 23320 toperform its encryption capabilities. The request may include the assetto be encrypted and the type of encryption being requested for theasset.

Besides implicating privacy rules, the permissions system 23370 can alsodetermine that one or more attributes of the asset or characteristicsassociated with an entity providing the enterprise asset dictate aparticular set of permissions. In some implementations, thecharacteristics or properties (e.g., entity identifiers) associated withan entity inform the permissions system 23370 which set of permissionsshould be associated with an asset for which the entity is/wasresponsible. For instance, when an enterprise entity responsible for anasset seeks to make that asset available via the wallet system 23350,the permissions system 23370 may generate a set of permissions for theasset that correspond to characteristics of the enterprise entity. Toillustrate, an enterprise entity may have certain access controls withthe enterprise (e.g., a particular level of clearance such as securityclearance or confidentiality clearance). The permissions system 23370may identify that the entity is associated with these access controlsand generates permissions for the asset at the EAL 23300 that aresimilar to or match the access controls associated with the entity atthe enterprise. For example, each employee of the enterprise may have anemployee identifier. The permissions system 23370 may be configured witha reference table that includes the permissions associated with thatemployee identifier. Using the table, the permissions system 23370generates a set of permissions for an asset based on the permissionsassociated with the employee identifier of an employee who submitted theasset to the EAL 23300. In some configurations, there may be anotherportion of that table or another table that designates which EAL-basedpermissions correspond to which enterprise permissions such that theEAL-based permissions can mirror or function in a manner similar to theenterprise permissions. As noted above, permissions may be associatedwith a set of roles that are managed by an identity management system orplatform, such that upon a change of role of an employee, thepermissions change (such as removing permissions for a departingemployee and applying the previous permissions of an employee to the newemployee that is taking the same role).

In embodiments, the permissions system 23370 may further be configuredas an approval system for an asset transaction; for instance, thepermissions system 23370 may receive an asset transaction request (i.e.,a request for a transaction involving the asset) and determine whetherthe requesting entity has the authorization or approval to proceed withand/or execute the transaction of the asset transaction request. Todetermine whether the requesting entity has the authorization to performthe transaction, the permissions system 23370 may perform some level ofdiligence on the details of the transaction. This diligence may include:determining whether the requesting entity has authorization to performthe transaction with the underlying asset(s), determining whether theunderlying asset has any conflicts that would inhibit the performance ofthe transaction, determining whether the transaction is in compliancewith one or more plans or policies, or the like.

To determine whether the requesting entity has authorization to performthe transaction, the permissions system 23370 may examine whether therequested transaction satisfies transactional terms for the asset. Forinstance, some assets or transactions may have transaction detailrequirements, such as particular contract terms, minimum pricing,delivery conditions, or timing constraints. When an asset transactionrequest implicates an asset or transaction that has transaction detailrequirements, the permissions system 23370 may identify theserequirements and determine whether the requirements are satisfied (e.g.,whether minimum thresholds are reached, whether limits are exceeded, orthe like). In response to the permissions system 23370 determining thatrequirements are satisfied, the permissions system 23370 may communicateits approval of the transaction (e.g., to the wallet system 23350). Onthe other hand, in response to the permissions system 23370 determiningthat the requirements are not satisfied, the permissions system 23370communicates that the EAL 23300 should decline the transaction (e.g., tothe wallet system 23350). In embodiments, the permissions system 23370may determine a modification of an otherwise non-compliant transactionthat would render it compliant and may communicate the modification,such that the EAL 23300 may execute a modified transaction, such as bypurchasing a reduced amount of an item or discovering an alternativesource of an item that has a lower price to keep a transaction below atransaction spending threshold, modifying a time of execution to satisfya waiting period, obtaining an additional approval to satisfypermissioning requirements, purchasing offsets or credits to allow atransaction to satisfy a sustainability objective, or the like.

In embodiments, the permissions system 23370 may also be configured todetermine whether the underlying asset has any conflicts that wouldinhibit the performance of the transaction. This may be importantbecause a large enterprise may have a large portfolio of assets. With alarge number of available assets, it is possible that one assettransaction request involves the same underlying asset as anothertransaction request; for example, both assets may be made subject torequests that they be used as collateral for two different loans, whereeach loan transaction requires a senior claim to the asset in the caseof default. As another example, two transactions may require sale of thesame asset to two different counterparties. Due to the possibility ofsuch conflicts, the permissions system 23370, upon receiving the assettransaction request, can determine what transactions are pending or havebeen requested. From the set of transactions that are pending or havebeen requested, the permissions system 23370 determines whether anytransactions of the set have been authorized for the asset specified bythe asset transaction request. If a transaction of the set has beenauthorized for the asset specified by the asset transaction request, thepermissions system 23370 may be configured to deny the asset transactionrequest (e.g., without disclosing the further details regarding theconflict). In some examples, when an asset transaction request isdenied, the permissions system 23370 may recommend a similar alternativeasset or set of assets as a substitution for the asset. Similarity maybe determined by asset type, asset value, or the like. In embodiments,the EAL 23300 may access capabilities of the transaction platformdescribed elsewhere herein or in the documents incorporated herein byreference for automatically determining similarity of assets based ontheir attributes and for automatically determining an alternative orsubstitute asset set based on such similarity, such as to recommend orinstruct a set of assets to be provided as substitute collateral for alending transaction and/or as substitute items for a purchase or sale.

In embodiments, the EAL systems of an EAL 23300 may include a reportingsystem 23380. Generally speaking, the reporting system 23380 functionsto provide some level of reporting to or from the EAL 23300, other EALsystems, non-EAL systems, and/or specified entities of an enterprise.For instance, the reporting system 23380 is capable of providingcompliance report(s) for one or more assets of the EAL 23300. Here, thetype of compliance report that the reporting system 23380 generates maydepend on the type of asset to be reported. For instance, a financialasset and a transaction regarding a financial asset may have compliancereporting requirements for accounting or tax purposes. In that regard,the reporting system 23380 generates a compliance report that fulfillsthe accounting/tax requirements.

Similarly, the reporting system 23380 may be customized to providedifferent types of reports. For instance, the reporting system 23380 maybe configured to generate a fraud report that conveys transactions thatwere not authorized or that triggered a fraud alert to occur at the EAL23300. Here, a fraud alert may come from a third party (e.g. PSP) orfrom another EAL system (e.g., the permissions system 23370). Thereporting system 23380 may also be configured to generate financialreports for financial activity at the EAL 23300. Here, the reportingsystem may compile financial information regarding transactions thathave been executed over some designated or customizable period of time.In some implementations, transactions at the EAL 23300 may have legalimplications, such as legal or regulatory reporting obligations. Inthese implementations, the reporting system 23380 may be configured togenerate a legal or regulatory report that is setup to identifytransactions that implicate a legal condition and to include theseidentified transactions in the legal report that the reporting system23380 generates.

In addition to reports like compliance reports, financial reports, andlegal or regulatory reports, the reporting system 23380 may also beconfigured to generate statistical reports that include statistics ormetrics regarding the assets managed by the EAL 23300 and/or activity(e.g., transaction activity) of the EAL 23300. Statistical reports maybe their own standalone reports or may be integrated into other types ofreports generated by the reporting system 23380 (e.g., part of afinancial report). Similarly, the reporting system 23380 may generateEAL activity reports that set forth instances of a particular activityor set of activities that are performed at the EAL 23300. For instance,among many other statistics and metrics, an EAL report may include howmany times a particular asset or type of asset is queried, how manytimes an asset or type is included in a transaction request, what assetsor types are available in which wallets of the wallet system 23350,volumes of asset transactions (purchases, sales, exchanges, loans),prices of asset transactions, characteristics of parties involved, andmany others.

FIGS. 233 and 234 depict different examples of how an EAL 23300 may beimplemented. For example, as shown in FIG. 233 , instead of beingintegrated with the enterprise 23200 (e.g., akin to FIG. 232 ), the EAL23300 may be integrated with different systems on the market side 23104of the enterprise ecosystem. To illustrate, FIG. 233 shows a set of EALs23300 a-n that are integrated with a set of marketplaces 23122 a-n. Whenintegrated with a particular marketplace 23122, some or all computingresources relied upon for the EAL 23300 may be hosted on the computingresources associated with the marketplace 23122 (e.g., marketplaceservers). Alternatively, when an EAL 23300 is integrated into aparticular marketplace 23122 there may be portions of the EAL 23300 thatremain hosted by enterprise resources to ensure aspects of securityand/or privacy for enterprise assets. Referring specifically to FIG. 233, a first EAL 23300 a is associated with or integrated with anorchestrated finance marketplace 23122 a. A second EAL 23300 b isintegrated with an orchestrated insurance marketplace 23122 b. A thirdEAL 23300 c is integrated with an orchestrated lending marketplace 23122c. A fourth EAL 23300 d is integrated with the third-party systems 23124a. An nth EAL 23300 n is integrated with an nth orchestrated marketplace23122 n since other types of marketplaces (not shown) can similarlyintegrate the functionality of the EAL 23300.

In some implementations, the functionality of the EAL 23300 isdistributed across market-side systems such that portions of the EAL23300 that interface with a particular marketplace 23122 are integratedwith that marketplace 23122 while other portions of the EAL 23300 thatinterface with another marketplace 23122 are integrated with the othermarketplace 23122. An example of this would be that the financialofferings of the EAL 23300 are integrated with the finance marketplace23122 a as the first EAL 23300 a while insurance offerings of the EAL23300 are integrated with the insurance marketplace 23122 b as thesecond EAL 23300 b. In some configurations, the distribution of the EAL23300 may be such that wallets of the wallet system 23350 are integratedamongst the marketplaces to which they relate. For instance, a walletthat includes financial enterprise assets is integrated with the financemarketplace 23122 a and is represented by the first EAL 23300 a. On theother hand, a wallet that includes insurance-related enterprise assets(e.g., data sets that may be integrated with insurance policies orcontracts) is integrated with the insurance marketplace 23122 b and isrepresented by the second EAL 23100 b.

FIG. 233 also illustrates another scenario on the right side of thefigure where an EAL 23300 n+1 can be a stand-alone system (e.g., amicroservice that enterprises leverage). In other words, the stand-alonesystem is capable of communicating with both the enterprise 23200 andthe market-side systems such as the storage system 23126, third-partysystems 23124 b, and the orchestrated marketplace 23122 n+1. As astand-alone system, the EAL 23300 n+1 may be configured such that theresources (e.g., computing resources) that the EAL 23300 n+1 relies uponfor operation are not hosted by, for example, the enterprise 23200 orthe orchestrated marketplace 23122 n+1. This may ensure that computingresources that the EAL 23300 may require are not occupied or beingconsumed by other resources at its host to compromise or somehow hinderthe performance of the EAL 23300. That is, if the EAL 23300 sharesresources with a system, that sharing may require priority procedureswhen resources are occupied or time in queue to wait for a particularresource to be available for utilization.

FIG. 234 is an example of the EAL 23300 integrated with the configuredmarket orchestration system 23400 (e.g., similar to a portion of FIG.233 ). The configured market orchestration system 23400 may refer to asystem that can control and/or manage a market ecosystem. In somerespects, the configured market orchestration system 23400 may beconsidered a “system of systems” because it is a structure that providescooperative coordination among a set of market-related systems that areconfigurable for the execution of various market services/tasks. In someexamples, the configured market orchestration system 23400 is a systemthat can function as a liaison for a set of systems or services. Forinstance, as shown by FIG. 232 , the configured market orchestrationsystem 23400 generally includes a configured intelligence service 23410and configured system services 23420. The configured marketorchestration system 23400 may also manage a set of transactionalsystems 23430. As shown in FIG. 232 some examples of these transactionalsystems 23430 include an asset valuation system, a collateralizationsystem, a tokenization market system, a market orchestration system, amarket making system, and a market governance and trust system. Some ofthese systems may be variations of the EAL system described previously.For instance, the market governance and trust system may be functionallysimilar to a combination of the governance system 23360 and thepermissions system 23370 of an example EAL 23300. In embodiments, thetransactional systems 23430 may be configured for the purpose ofgenerating and/or controlling particular aspects of a market (i.e.,transactional execution) while EAL systems may be configured foraccessing markets and performing transactions on behalf of anenterprise.

In order to manage the set of transactional systems 23430, theconfigured market orchestration system 23400 leverages the functionalityof the configured intelligence service 23410 and the configured systemservices 23420. The configured intelligence service 23410 is a frameworkfor providing intelligence services to one or more services, such as theconfigured system services 23420. In some implementations, theconfigured intelligence service 23410 receives an intelligence requestto perform a specific intelligence task (e.g., a decision, arecommendation, a report, an instruction, a classification, a pattern orobject recognition, a prediction, an optimization, a training action, anatural language processing request, etc.). In response, the configuredintelligence service 23410 executes the requested intelligence task andreturns a response to the intelligence service requestor (e.g., theconfigured system services 23420).

The configured intelligence service 23410 may include an intelligenceservice controller 23412 and a set of artificial intelligence (AI)modules 23414. When the configured intelligence service 23410 receivesan intelligence request (e.g., from a transactional system 23430 or fromthe configured system services 23420), the request may include anyspecific/required data to process the request. In response to therequest and the specific data, one or more implicated AI modules 23414perform the intelligence task and output an “intelligence response.”Examples of responses from AI modules 23414 may include a decision(e.g., a control instruction, a proposed action, machine-generated text,and/or the like), a prediction (e.g., a predicted meaning of a textsnippet, a predicted outcome associated with a proposed action, apredicted fault condition, an anticipated state of an entity or workflowrelevant to a transaction (such as a future price, interest rate, orconversion rate), and/or the like), a classification (e.g., aclassification of an object in an image, a classification of a spokenutterance, a classified fault condition based on sensor data, and/or thelike), a recommendation (e.g., a recommendation for an action tooptimize a transaction parameter), and/or other suitable outputs of anartificial intelligence system.

There may be a variety of AI modules 23414 associated with theconfigured intelligence service 23410 to have the broad capability tooutput the many types of intelligence responses that may be requested ofthe configured intelligence service 23410. Some examples of these AImodules 23414 include ML modules, rules-based modules, expert systemmodules, analytics modules (e.g., econometric models, behavioralanalytics, collaborative filtering, entity similarity and clustering,and others), automation modules, control system modules, robotic processautomation (RPA) modules, digital twin modules, machine vision modules,NLP modules, text-to-speech modules, and neural network modules, as wellas any other types of artificial intelligence systems described hereinor in the documents incorporated herein by reference and encompassinghybrids or combinations thereof (e.g., where an AI modules uses morethan one type of neural network). It is appreciated that the foregoingare non-limiting examples of AI modules 23414, and that some of themodules may be included or leveraged by other AI modules.

As shown in FIG. 234 , the AI modules 23414 interface with theintelligence service controller 23412, which is configured to determinea type of request issued to the configured intelligence service 23410(e.g., from an intelligence requestor such as the configured systemservices 23420 or a transactional system 23430) and, in response, maydetermine a set of governance standards and/or analyses that are to beapplied by or to the AI modules 23414 when responding to the request. Insome examples, the intelligence service controller 23412 may include ananalysis management module, a set of analysis modules (e.g., shown as afraud detection module, a risk analysis module, and a forecastingmodule), and a governance library.

In some implementations, the analysis management module receives arequest from the AI modules 23414 and determines the governancestandards and/or analyses implicated by the request. In some examples,the analysis management module may determine the governance standardsthat apply to the request based on the type of decision that wasrequested and/or whether certain analyses are to be performed withrespect to the requested decision. For example, a request for a controldecision that results in the configured system services 23420configuring an action for the transactional system 23430 may implicate acertain set of governance standards that apply, such as safetystandards, legal or regulatory standards (e.g., privacy standards, “knowyour customer” standards, reporting standards, export control standardsand many others), financial accounting regulatory standards, legalstandards, quality standards, or the like, and/or may implicate one ormore analyses regarding the control decision, such as a risk analysis, asafety analysis, an engineering analysis, or the like. In embodiments,the governance standards may apply to the AI modules; for example, atraining data set used for an AI module may be required to be satisfygovernance standards, such as representativeness of data, absence ofbias, adequacy of statistical significance, absence of inequity inresulting outcomes, and the like. As one such example, a training dataset of historical transactions used to train an AI module to identify afavorable counterparty may be governed by policy that requires that thetraining data set include historical transactions that are free ofracial, ethnic, or socioeconomic imbalances, compliance analysis, anengineering analysis, or the like.

In some instances, the analysis management module may determine thegovernance standards that apply to a decision request based on one ormore conditions. Non-limiting examples of such conditions may includethe type of decision that is requested, a location (e.g., geolocation,jurisdiction, data processing location, network location, or the like)in which a decision is being made, a location in which an activitygoverned by the decision will be executed (e.g., where an asset orresource will be purchased, stored, sold, or the like), an environmentor system that the decision will affect, current or predicted conditionsof the environment or system, a set of parties to a transaction affectedby the decision, and/or the like. The governance standards may bedefined as a set of standards, policies, rules, or the like in agovernance library, which may include a set of standards libraries. Theforegoing may define conditions, thresholds, rules, recommendations, orother suitable parameters by which a decision may be analyzed. Examplesof may include, legal standards library, a regulatory standards library,a quality standards library, a financial standards library, a riskmanagement standards library, an environmental standards library, asustainability standards library, an ethical standards library, a socialstandards library, and/or other suitable types of standards libraries.In some configurations, the governance library includes an index thatindexes certain standards defined in the respective standards librarybased on different conditions or context. Examples of conditions may bea jurisdiction or geographic area to which certain standards apply,environmental conditions to which certain standards apply, device typesto which certain standards apply, materials or products to which certainstandards apply, and/or the like.

In some implementations, the analysis management module may determinethe appropriate set of standards that must be applied with respect to aparticular decision and may provide the appropriate set of standards tothe artificial intelligence modules 23414, such that the AI modules23414 leverage the implicated governance standards when determining adecision. In these embodiments, the AI modules 23414 may be configuredto apply the standards in the decision-making process, such that adecision output by the AI modules 23414 is consistent with theimplicated governance standards. It is appreciated that the standardslibraries in the governance library may be defined by the platformprovider, customers, and/or third parties. The standards may be created,managed, promulgated and/or overseen by various sources, such asgovernment standards, industry standards, customer standards, enterprisestandards, non-governmental entity standards (e.g., internationalagencies), or standards from other suitable sources. Each set ofstandards may include a set of conditions that implicate the respectiveset of standards, such that the conditions may be used to determinewhich standards to apply given a situation. In embodiments, thestandards may be embodied in executable logic, such that elements ofstandards are automatically applied, optionally at the level of anindividual workload or service within a workflow or system, such as byprompting workload developers to embed standards compliance (and anyother policies) into the workload development and deployment process.

In some embodiments, the analysis management module may determine one ormore analyses that are to be performed with respect to a particulardecision and may provide corresponding analysis modules that performthose analyses to the AI modules 23414, such that the AI modules 23414leverage the corresponding analysis modules to analyze a decision beforeoutputting the decision to the requestor. In some examples, the analysismodules may include modules that are configured to perform specificanalyses with respect to certain types of decisions, whereby therespective modules are executed by a processing system that hosts theinstance of the configured intelligence service 23410. Non-limitingexamples of analysis modules may include one or more risk analysismodules, econometric analysis modules, financial analysis modules,behavioral analysis modules (e.g., of user behavior, system behavior, orthe like), security analysis modules, decision tree analysis modules,ethics analysis modules, forecasting analysis modules, quality analysismodules, safety analysis modules, regulatory analysis modules, legalanalysis modules, and/or other suitable analysis modules, including anyof the analysis types described herein or in the documents incorporatedherein by reference.

In some configurations, the analysis management module is configured todetermine which types of analyses to perform based on the type ofdecision that was requested to be performed by the configuredintelligence service 23410. In some of these configurations, theanalysis management module may include an index or other suitablemechanism that identifies a set of analysis modules based on a requesteddecision type. Here, the analysis management module may receive thedecision type and may determine a set of analysis modules that are to beexecuted based on the decision type. Additionally, or alternatively, oneor more governance standards may define when a particular analysis is tobe performed. For example, the regulatory standards may define whatscenarios necessitate a risk analysis. In this example, the regulatorystandards may have been implicated by a request for a particular type ofdecision and the regulatory standards may define scenarios when a riskanalysis is to be performed. In this example, AI modules 23414 mayexecute a risk analysis module and may determine an alternative decisionif the action would violate a respective legal standard. In response toanalyzing a proposed decision, AI modules 23414 may selectively outputthe proposed condition based on the results of the executed analyses. Ifa decision is allowed, AI modules 23414 may output the decision to therequestor. If the proposed configuration is flagged by one or more ofthe analyses, AI modules 23414 may determine an alternative decision andexecute the analyses with respect to the alternate proposed decisionuntil a conforming decision is obtained.

In embodiments, the configured system services 23420 function toconfigure a set of systems (e.g., the set of transactional systems23430) corresponding to the configured market orchestration system 23400to perform a set of services based on intelligence determined for theconfigured system services 23420. Like configured intelligence services23410, configured system services 23420 provide data storage, librarymanagement, data handling, and/or data processing services that aretailored to requirements associated with a particular marketorchestration system 23400 (e.g., in response to data requests and/ordirected market transactions by the EAL 23300). In some examples, theconfigured system services 23420 uses the configured intelligenceservice 23410 to generate decisions relating to configurations of theset of transactional systems 23430. For instance, if the configuredsystem service 23420 is to configure a smart contract as the configuredtransactional system, the configured system services 23420 leverages theintelligence of the configured intelligence service 23410 to formulatean intelligence request that will configure some portion of a smartcontract (e.g., determine one or more parameter values corresponding toconditions defined in the smart contract).

In some implementations, the systems services that are configured tobecome the configured system services 23420 are the EAL systems of theEAL 23300. In other words, the configured system services 23420 usesintelligence generated by the configured intelligence services toconfigure aspects of the EAL 23300, such as the wallet system 23350 orthe permissions system 23370. In some implementations, the configuredsystem services 23420 not only configure input or control parameters ofEAL systems that perform (e.g., the wallet system 23350) or evaluatetransactions (e.g., the permissions system 23370), but also configureinput or control parameters that impact the user experience or userinterface of the EAL 23300 (e.g., configuration parameters associatedwith the interface system 23310). Here, since EAL systems may beassociated with the configured system services 23420, an EAL system mayfunction via the configured system services 23420 as a requestor for aparticular intelligence response.

In some configurations, such as FIG. 234 , the configured systemservices 23420 is capable of performing general system services. Thesegeneral system services may include operations such as data storage,data processing, networking, etc. that are configured for a particularfunction or set of functions. As shown in FIG. 234 , these generalsystem services may be integrated or controlled by the configured systemservices 23420. However, in some configurations, it may be moreadvantageous for the general system services to be more widely availableto aspects of the configured market orchestration system 23400.Therefore, the general system services may be its own entity that isaccessible to both the configured intelligence service 23410 and theconfigured system services 23420, but not tethered specifically to thefunctionality or computing resources of either service.

In some configurations, a configured market orchestration system 23400is configured for a particular marketplace 23122. As an example, theconfigured market orchestration system 23400 is configured for a lendingmarketplace. For instance, the integrated enterprise access layer 23300c of the orchestrated lending marketplace 23122 c is a part of aconfigured market orchestration system 23400 for the orchestratedlending marketplace 23122 c. In this example, the configured marketorchestration system 23400 via the transactional systems 23430 mayperform tasks that may require external information (e.g., currentmarket data) for functions, such as asset valuations, inventory access,business profile management, market analysis, and the like. Depending onthe task, subsequent tasks or analyses may be handled (e.g., directlyhandled) by the configured market orchestration system 23400, by the EAL23300, or some combination of both.

In some implementations for a configured market orchestration system23400, the workflow system 23340 of the EAL 23300 can manage or assistin managing one or more of the task-based information exchanges,analyses, and/or transactions by assembling workflow components,identifying pre-existing workflows, or developing workflows based on MLand AI methods. Examples of workflow components include: lookup of anasset serial number to determine a date of manufacture, existing serviceinformation, verification of ownership, etc. for the task of assetvaluation and collateralization; reviewing business credit rating,claims, customer history, collateral to lending ratio, asset liquidity,etc. for the task of risk evaluation; determining minimum requirementsfor collateral, min/max allowable insurance for certain asset types,specific asset validation/verification requirements, etc. for the taskof regulatory compliance; obtaining bid requests and analyses for thetask of evaluation of insurance options and recommendations; anddetermining transaction type based on customer, client, regulation, etc.for the task of negotiation and completion of transactions.

To illustrate by an example, the workflow system 23340 may generate aset of workflow steps that define a task of a business loan request thatproposes the use of machine tools as collateral for a loan to expandbusiness. In this example, a first workflow step may be for theconfigured market orchestration system 23400 to parse loan applicationinformation to identify equipment (collateral) types andcharacteristics. Here, a second workflow step may be that the configuredmarket orchestration system 23400 submits a preconfiguredmarket-specific request to provide information associated withcollateral resale value, liquidity, and market depth, including searchesof relevant private or public marketplaces. Here, the EAL 23300 mayprovide a value range to the configured market orchestration system23400. A third workflow step may be that the configured marketorchestration system 23400 submits a preconfigured market-specificrequire for the EAL 23300 to obtain information associated with thebusiness requesting the loan. In this workflow step, the EAL 23300 mayreturn, for example, credit ratings, outstanding loans, and/ortransactions histories. A fourth workflow step may be that theconfigured market orchestration system 23400 submits a preconfiguredmarket-specific risk analysis request to the EAL 23300 based ongovernment and lender requirements. In some embodiments, this suggestedEAL analysis could be automatically selected from a library developedfor a type of loan or industry. As an alternative, this fourth workflowstep may be completed by the configured market orchestration system23400 and then verified by the EAL 23300. A fifth workflow step may bebased on the internal analyses and/or information provided by the EAL23300. For instance, in this fifth workflow step, the configured marketorchestration system 23400 develops or selects an insurance bid packagefor submission to market participants. Here, as an example, theconfigured market orchestration system 23400 may select the best optionfrom among bidders. A sixth workflow step may be that the configuredmarket orchestration system 23400 engages the EAL 23300 to complete thetransaction and submit the required documentation. This step may includea series of preconfigured functions selected for bid payment terms andmethods, reporting requirements, and the like.

With an EAL configuration, assets of an enterprise 23200 can be nativelyintegrated into marketplaces 23122 without the enterprise 23200 havingto necessarily conduct advertising or marketing campaigns. That is, thewallet system 23350 in combination with the interface system 23310 canenable enterprise assets associated with wallet(s) to be readilyavailable to marketplaces 23122. This allows assets of the enterprise23200 to be market-facing without having to orchestrate product/serviceoffering campaigns. In this respect, the assets can be offered nativelyon various platforms. Additionally, since the interface system 23310and/or wallet system 23350 has access to multiple marketplaces, the EAL23300 can offer assets in marketplaces that are not necessarily the sametype of goods/services as the assets, but rather complimentarymarketplaces or even marketplaces that are not traditionally offeringassets with attributes similar to the available enterprise assets. Forinstance, an enterprise asset may be a financial asset and yet beoffered or integrated into non-financial contexts. To facilitate themarket for an asset, in embodiments, a reserve price may be associatedwith the asset, at which an enterprise is willing to part with the assetif and when it is sought by a market participant in one of the marketsin which it can be viewed, such as by the aforementioned via walletintegration.

In some examples, the EAL 23300 allows the securitization and/ortokenization of future revenue streams for the enterprise 23200. Here,an enterprise 23200 can offer assets such as financial history, futurescontracts, or other valuable enterprise insights (e.g., as asset-backedtokens) to secure capital or credit in various lending marketplaces. Forinstance, the enterprise 23200 may request an instant cash advanceagainst the full annual value of the enterprise's subscriptions orsource of recurring revenue. This means that the enterprise 23200 canleverage its various assets in traditional or non-traditional lendingmarketplaces that the EAL 23300 has the capability with which tointerface. To illustrate, the EAL 23300 may be configured to translatesubscription or recurring payment revenue (e.g., future revenue streams)into instant capital (i.e., cash). For example, the EAL may seek tomitigate risk of a substantive portion of expiring revenue streams andengage the available marketplaces 23122 via the EAL 23300 to access alender for these future enterprise assets.

In some configurations, to induce or to support lender transactionsagainst future enterprise assets, the lender is able to request otherenterprise assets (e.g., proprietary data sets) to form a basis,collateral, escrow, representation, or warranty against the transaction.As one example, the lender may offer a cash advance for futuresubscription revenue streams of the enterprise 23200 with terms that anew product will launch according to some parameters indicated byenterprise data sets made available to the lender. In situations wherethe lender executes a transaction based on supporting enterprise datasets, the lender may also receive those enterprise data sets in thetransaction, allowing the lender to engage with marketplaces 23122 tosell the enterprise data sets if it so chooses. In this respect, lendersand marketplace participants 23110 transacting with an enterprise canleverage cross market transactions (e.g., as secondary revenue streamsto support primary transactions).

In some implementations, when the enterprise 23200 offers its revenuestream as an enterprise asset to secure lending (e.g., an instant cashadvance), the result of the lending can be represented digitally bytokenization. In other words, even though the enterprise 23200 hasreceived non-digital currency (e.g., cash), the wallet system 23350 mayrepresent that cash in digital form by means of a token such that thecash can operate as a digital enterprise asset that can participate indigital transactions using the EAL's capabilities. Additionally oralternatively, a smart contract corresponding to the loan/revenue streammay interface with an oracle that receives proof of payment from legacyoff-chain systems and that reports verification of the received paymentto the smart contract.

By being able to operate in a digital space, the EAL 23300 is able toemploy different digital advantages to transactions. For instance, theassets such as operational assets, financial assets, or other assets canutilize tokenization to permit only a particular set of actions byselected stakeholders. The actions permitted by a token can be agreedupon according to consensus mechanisms by a set of stakeholders, or theycan be dictated by a governing entity, such as an enterprise manager orexecutive. In some implementations, because these tokens are functioningto verify agreed upon actions, these tokens may be referred to as“verifiable action tokens.”

In some configurations, the tokenization can occur for any enterpriseasset. For instance, certain enterprise assets (e.g., enterprise datasets) may include confidential or private information for (i)individuals associated with the enterprise 23200, (ii) clients of theenterprise 23200, or (iii) confidential information or actions of theenterprise 23200, among others. Enterprise assets that includeconfidential or private information may be encoded or tokenized (e.g.,by the data services system 23320) at the EAL 23300. By encoding theasset or some determined portion thereof, the enterprise 23200 can offerassets relating to or including this information without compromisingsecurity, confidentiality, or privacy. In some examples, when tokenizingor encoding some or all of an enterprise asset, the reporting system23380 generates a report or stores a ledger of these encoded events. Bygenerating such as record, the EAL 23300 can allow the enterprise 23200to prove compliance or back trace its operations in case of an audit orother request of concern.

In some configurations, the EAL 23300 is able to facilitate transactionsfor market enterprise resources that may not be traditionally consideredas exchangeable assets to the enterprise 23200. It is becoming morecommon in the age of big data that data sets by themselves can be avaluable asset. For instance, with aspects of artificial intelligencebecoming more prevalent, its intelligent capabilities often demand datasets that are used for training, such as to allow the AI to learn toperform some type of task or function. As a large organizationalstructure, the enterprise 23200 can generate vast amount of data setsregarding its workings (e.g., operations, strategy, planning, sales,marketing, finances, human resource management, etc.) that can bevaluable in the training of particular types of AI. For instance, aninsurance company may be interested in the occupational conditions ofworkers that it insures, but finding a large, meaningful data set thatcharacterizes occupational conditions may be rather difficult to find,at least publicly. Yet many enterprises 23200 track or have dataregarding their own occupational conditions. In this example, theinsurance company would find it valuable to have access to data setscharacterizing the occupational conditions of at least the enterprise23200. The EAL may provide access to such data sets, such as byrepresenting them in a wallet or other system that can be accessed bymarket participants. Use of the data may be governed by governance andpermission systems as noted herein; for example, the data may bepermitted to be accessed only in a machine-readable form that isaccessible to a neural network or other AI system being trained. Inembodiments, portions of the data, such as representing privateinformation, may be anonymized, obfuscated, deleted, redacted, or thelike to allow data to be used for training AI while not being used forother purposes. In embodiments, a set of governance policies for thedata set may be configured such that the policies are automaticallyapplied to any AI system that is trained using the data; for example, inorder to access the training data set, the AI system may be required todemonstrate that it is governed by code or logic that validates that theAI system will be governed in the way required by the policies. As oneexample, the AI system may be permitted to operate only for a limitedpurpose, a limited time, in a limited location, by a limited type ofparty, or the like.

The EAL configuration can allow marketplace participants 23110 torequest or to form markets to which the enterprise 23200 may have assetsto contribute or from which the enterprise 23200 may wish to obtainassets. For example, an insurance company may request data setsregarding occupational conditions, and the EAL 23300 may parse orreceive that request and then determine whether it has the assetsavailable to fulfill that request. When the requested asset is notavailable at the time of the request, the EAL 23300 may be configured tointerface with the enterprise 23200 to present the opportunity to theenterprise 23200 and give the enterprise 23200 the opportunity forfulfillment of the request. In other words, the available enterpriseassets may not include an occupational conditions dataset, but when theEAL 23300 presents that request to the enterprise 23200, the enterprise23200 determines that it can supply one or more data sets to fulfillthat request and makes the one or more data sets available as enterpriseassets via the wallet system 23350.

In some implementations, “data-as-a-transaction” (e.g., data sets astransacted entities) can contribute to context-based accommodations totransactions between parties. As an example, access to data (e.g., anenterprise asset) could be used by a party to gain advantages in pricingwith an acceptance of an increase in risk. For instance, an insurer mayallow a partial premium payment based on the delivery by the insured(e.g., the enterprise 23200) of specified data types (i.e., specializedenterprise assets). Here, receipt of the specified data types mayautomatically trigger a smart contract to adjust or generate one or moreterms regarding, for example, pricing, interest rates, conversion rates,deductibles, underwriting requirements, ancillary offerings, promotions,term duration, limits on liability, warranties and representations, etc.To illustrate, a factory of an enterprise may have a liability andworkman's compensation policy with some amount of designated coverage.As party of the policy, there may be specified data thresholdsregarding, for example, the number of employees on the floor per shift,the number of machine hours of operation per day, the types of machinesin operation, the number of sick days, injury reports, and insurancestatus of employees. When the factory has enough data to satisfy (e.g.,surpass or exceed) the specified thresholds, the data may be transferredto the insurer and provisions of the policy affected are adjusted basedon the data transferred. For example, the factory sends data (i.e., anenterprise asset) that 83% of its employees are insured. Here, sincethis 83% exceeds an 80% threshold that allows for a reduction in thepolicy premium, the transfer of data causes the policy premiumadjustment for the factory's policy; in embodiments, the premium may befurther reduced if the insurer is permitted to use the data (possibly inanonymized, obfuscated, or otherwise modified form) for its ownpurposes, such as to facilitate more accurate underwriting or forgeneration of improved actuarial, economic or predictive models(including predictions of the emergence of insurable risks). In someconfigurations, the EAL transfers (i.e., a transaction of an enterpriseasset) or facilitates the transfer of data along with a protocol request(e.g., a request to adjust the premium). The insurer may also leverageenterprise asset transactions to inform their contracts and policies.For instance, the insurer may generate a query for data from theenterprise (e.g., the factory) to ensure or audit that the conditions ofthe policy are being met. In other words, the insurer may query orrequest an enterprise asset transaction for data regarding the number ofemployees on the floor per shift. Here, if the number increasedunbeknownst to the insurer, the query may inform the insurer to adjustthe premium (e.g., to increase the premium because the factory has movedto a greater risk level based on the query results for the number ofemployees on the floor per shift).

When enterprise assets are various types of data sets, the enterprise23200 may have difficulty understanding the value of a particular dataset. For instance, if an insurer would like to purchase data sets forworking conditions of the enterprise 23200 to facilitate products orservices of the insurer (such as to tailor premium offerings tomarketplace participant conditions, to improve underwriting, to improveprediction, or the like), the enterprise 23200 may be unable to properlyvalue this enterprise asset due to its unconventional nature or the merefact that it is not the type of asset with which the enterprise 23200 isused to dealing. In these situations, the EAL 23300 may request orgenerate an evaluation marketplace, such as by sourcing (optionally bycrowdsourcing) a set of target consumers (e.g., would-be data utilizers)to determine the estimated value for the data set. To generate anevaluation marketplace, the EAL 23300 may invite a set of would-be dataproviders (e.g., providers who could produce the type of data setsrequiring valuation) and/or a set of would-be data utilizers (e.g.,target consumers that could demand the types of data sets requiringvaluation). In some examples, the parties that accept the invitationsbecome virtual auction participants in order to provide a near-realmarket valuation of the data sets. That is, the participating would-bedata provider posts or submits their data set (e.g., having one or morecharacteristics similar to the enterprise data set) and theparticipating would-be data utilizer(s) bid (e.g., propose an estimatedvalue that they would pay) on the posted data set. In someconfigurations, this bidding process continues for each available dataset from the pool of participating would-be data providers. In theseconfigurations, the EAL 23300 may use statistical inference with theplurality of bids for the available data sets to generate a valuationfor the similar data set owned by the enterprise 23200. In someexamples, the virtual auction house actually performs the offering ofthe enterprise data set during the auction so that the would-be datautilizers are not biased in their bidding. In embodiments, the EAL may,additionally, or alternatively, facilitate a set of simulations to helpassess the value of the data, such as simulations that are informed byhistorical transactions in data sets having some similarity to availabledata sets, as well as informed by current marketplace conditions (suchas offered prices of other data sets). In some examples, theparticipants in the virtual auction house engage with the virtualauction for evaluation purposes such that a participant does not receivethe enterprise data set, but assists in its valuation for a futuremarket offering. When functioning for a future market offering, it maybe advantageous to include a large number of participants tostatistically overcome potential bidding biases.

In some situations, following the valuation (such as using a virtualauction house, simulation, or other approached noted above), the EAL23300 enables the enterprise 23200 to further adjust the valuation ofthe data set. For instance, the EAL 23300 generates a feedback requestto the enterprise 23200 to authorize the estimated value assigned to thedata set and the enterprise 23200 provides a message in response to thefeedback request that either approves the valuation or adjusts thevaluation in some manner. Here, this adjustment feedback loop allows theenterprise 23200 to determine if the valuation justifies the offering ofthe data set or if the enterprise 23200 would prefer to offer the dataset at a higher or lower transactional value compared to the valuation.For example, the value of the data set to the owner (i.e., theenterprise) may differ from the value of the data set to the market.Depending on the disconnect or gap between the owner value and themarket value, the enterprise 23200 may adjust the transaction valueaccordingly. Similarly, being informed by the valuation can also enablethe enterprise 23200 to opt out of offering the data set.

In some configurations, the EAL 23300 controlled by an enterprise 23200receives a data set from the enterprise 23200. Here, the data set maycharacterize one or more attributes associated with a group of resourcesprivately controlled by the enterprise 23200. For instance, the data setmay characterize information about a group of employees of theenterprise 23200 (e.g., factory workers) or a group of equipment (e.g.,production equipment of the enterprise 23200). Upon receipt of the dataset, the permissions system 23370 determines whether the data setsatisfies a set of permission criteria. The permission criteria mayrefer to criteria that indicates a set of privacy rules, access rules,security rules, compliance rules, or other rules applicable to assets,resources or other entities that are controlled by the enterprise 23200.The enterprise 23200 or its agent may configure these rules or generatethe rules to correspond to industry/legal standards (e.g., dictated bythe governance system 23360), such as of acceptable privacy (e.g., toabide by the Health Insurance Portability and Accountability (HIPA) Actor General Data Protection Regulation (GDRP)), or the like.

Depending on the determination of whether the data set satisfies the setof permission criteria, the permissions system 23370 may performdifferent operations. For instance, in response to the data set failingto satisfy the permission criteria, the permissions system 23370 maycommunicate the data set to the data services system 23320. Inembodiments, the permissions system 23370 recognizes that the data setneeds further data processing and cooperates with the data servicessystem 23320 of the EAL 23300 to perform that processing. In theseconfigurations, the further processing may be that the data servicessystem 23320 generates an encoded data set that satisfies the privacy orother rules identified by the permissions system 23370 for the data set.With the encoded data set that complies with the rules identified by thepermissions system 23370, the EAL 23300 converts the encoded data set toan exchangeable digital asset. This conversion may occur by the EAL23300 publishing the encoded data set to the wallet system 23350 andconfiguring the interface system 23310 with access to the encoded dataset in the wallet system 23350 such that market participants 23110 canaccess and/or request transactions for the encoded data set. On theother hand, if the permissions system 23370 determined that the data setsatisfies the permission criteria, the EAL 23300 may convert the dataset to an exchangeable digital asset in the same manner without the dataprocessing encoding operation. In embodiments, encoding operations mayinclude embedding applicable rules, such as licensing terms andconditions, for use of the data set, such that upon subsequent use ofthe data set such rules are automatically applied (e.g., to limit thenumber of seats that can access the data, to monitor and govern thenumber of queries or other restrictions permitted, to limit access tosensitive data contained in the data set (e.g., to allow aggregatequeries but to limit queries from which private information can bededuced), to limit the location of use, to limit duration of use, togovern which systems or types of systems can access the data, or thelike).

In embodiments, the EAL 23300 may be set up to operate as a data planeand control plane for the enterprise 23200. In embodiments, whenoperating as a data plane, the EAL 23300 may be configured to exchangeassets privately-generated by an enterprise 23200 or enterprise entitythat operates it. When configured in this manner, the EAL 23300 mayreceive an asset request from a requesting entity, such as a marketparticipant 23110 with access to the EAL 23300 (e.g., via the interfacesystem 23310). Here, the asset request indicates an asset that may beavailable for transaction, such as discovered in a wallet system 23350(e.g., is associated with a wallet of the wallet system 23350) or otherpresentation interface. Based on the request, the permissions system23370 identifies whether there are any asset controls (e.g., accesscontrols or permissions assigned to an asset) associated with therequested asset. Here, the permissions system 23370 may have configuredthe asset control for the asset to indicate a control parameter thatmust be satisfied prior to any transactional action occurring for theasset. In some examples, the intelligence system 23330 is able todetermine control parameters for the permissions system 23370 using dataderived from the enterprise 23200 that privately generated the asset. Inother words, the intelligence system 23330 can predict or determine acontrol parameter based on historical data modeling of controls forassets of the enterprise or for controls of assets similar to the assetsof the enterprise.

In response to the permissions system 23370 identifying an asset controlcondition associated with the requested asset, the permissions system23370 proceeds to determine whether the asset control condition issatisfied, such as, for example, by one or more parameters of the assetrequests and/or by one or more attributes of the requesting entity. Forinstance, the asset control may designate what type of entity is able toaccess the asset or some set of requirements that must be met by theasset request and/or requesting entity to gain permission to access theasset (e.g., perform a transaction with the asset). In response to theasset control condition being satisfied, the EAL 23300 may facilitatefulfillment of the asset request. On the other hand, if the permissionssystem 23370 determines that the asset control condition is notsatisfied, the requesting entity/asset request is denied. In someconfigurations, denial of the request generates a message that indicatesthe denial. This message may include some amount of informationdetailing the reasons for denial and/or prompting modifications in theasset request and/or requesting entity that would enable the request tobe satisfied.

In some implementations, the EAL 23300 receives an asset request from arequesting entity (e.g., a market participant 23110) where the assetrequest indicates an asset that is available in the wallet system 23350as an exchangeable digital asset. In these implementations, exchangeabledigital assets of the enterprise 23200 correspond to one or more assetsstored in a private data structure (e.g., a private blockchain)associated with an owner of the exchangeable digital assets (e.g., theenterprise 23200). Based on the request, the EAL 23300 identifieswhether there are any asset controls (e.g., access controls orpermissions assigned to an asset) associated with the requested asset.Here, the permissions system 23370 may have configured the asset controlfor the asset to indicate a control parameter that must be satisfiedprior to any transactional action occurring for the asset. Similar tothe prior discussed configurations of the EAL 23300, the intelligencesystem 23330 is able to determine control parameters for the permissionssystem 23370 using data derived from the enterprise 23200 that privatelygenerated the asset.

In response to the EAL 23300 (e.g., the permissions system 23370)identifying an asset control associated with the requested asset, thepermissions system 23370 proceeds to determine whether the asset controlis satisfied by at least one of the asset requests or by the requestingentity. For instance, the asset control designates what type of entityis able to access the asset or some set of requirements that must be metby the asset request and/or requesting entity to gain permission toaccess the asset (e.g., perform a transaction with the asset). Inresponse to the asset control being satisfied, the EAL 23300 mayfacilitate fulfillment of the asset request. Yet here, fulfillment ofthe asset request includes storing the asset in a public append-onlydata structure (e.g., a public blockchain) to represent a transactioninvolving the asset with the requesting entity. On the other hand, ifthe permissions system 23370 determines that the asset control fails tobe satisfied, the requesting entity/asset request is denied and a denialmessage (as previously discussed) may be communicated to the requestingentity. With this approach, the EAL 23300 is able to function as afacilitator or executor for transactions that demand operations on botha private data structure (e.g., a private blockchain) and a public datastructure (e.g., a public blockchain).

In some examples, the EAL 23300 receives a set of assets generated orcontrolled by the enterprise 23200. For each asset of the set of assets,the EAL 23300 may classify (e.g., using the intelligence system 23330)the respective asset into an asset category, which may includeclassifying the asset into an asset control category. Here, each assetcategory is associated with a set of rules, such as assets controls,that dictate one or more transaction parameters for the exchange of therespective asset with a third party (e.g., a market participant 23110).Moreover, for each asset of the set of assets, the EAL 23300 (e.g.,using the permissions system 23370) may assign the set of asset rulesfor the access category classified by the EAL 23300 for the respectiveasset. In these examples, the EAL 23300 then converts the set of assetsto exchangeable digital assets by publishing the set of assets to thewallet system 23350 and configuring the interface system 23310 withaccess to the set of the wallet system 23350. In embodiments, assetcategories may be associated with a defined set of marketplaces,exchanges, or other environments in which assets may be transacted, suchthat a set of rules appropriate for the classified asset may be derivedby reference to the governing rules of the applicable transactionenvironment; for example, assets classified as commodities may begoverned by rules of a commodities exchange, assets classified assecurities may be governed by rules of a securities exchange, assetsclassified as cryptocurrencies may be governed by rules of acryptocurrency exchange, and the like. Asset classification may belearned using any of the artificial intelligence or learning techniquesdescribed herein, such as on a training data set of historicaltransactions (e.g., by observing which type of asset objects are tradedin which environments), by training on human classification interactions(such as tagging of assets), and the like. Training may be seeded orassisted by a model, such as an asset classification model thatclassifies or clusters assets based on data object parameters. This mayinclude a hierarchical model or graph with classes and subclasses ofasset types.

In some embodiments, the EAL 23300 may also function as a type ofmonitoring system. For example, the EAL 23300 may be configured toautomatically monitor or mine for potential deals or transactions thatcould involve the enterprise assets that it manages and/or to monitor ormine for opportunities to acquire assets that it wishes to acquire. Insome configurations, the EAL 23300 monitors (e.g., via its interfacesystem 23310) a plurality of market participants 23110. While monitoringthe plurality of market participants 23110, the EAL 23300 may receive anindication that a monitored market participant 23110 requests or offersan asset candidate or type of asset. In the case of a request for anasset or type, the EAL 23300 determines (e.g., via using theintelligence system 23330) whether the asset candidate matches (or issimilar to) an asset available in the wallet system 23350 associatedwith the EAL 23300. If the asset candidate does not match any availableassets in the wallet system 23350, the EAL 23300 may continue to performmonitoring services for other asset candidates. In the case of offers,the EAL 23300 may receive an indication of the parameters of an offer ofa digital asset or type, compare the offer to a set of desiredtransaction parameters, and, if the parameters are satisfied, initiate atransaction to acquire the asset.

In response to a request matching an asset available in the walletsystem 23350, the EAL 23300 may be configured to perform a set ofoperations that further analyze whether to engage or to offer to engagein an asset transaction with the monitored market participant 23110.These operations may include identifying a set of asset controlconditions managed by the permissions system 23370 of the EAL 23300 anddetermining whether a transaction (e.g., a digital exchange) with themonitored market participant 23110 satisfies an asset control criterioncorresponding to the asset available in the wallet system 23350 (i.e.,the matching asset). For instance, the asset control criterion mayindicate that a threshold number has been exceeded. In response todetermining that the transaction with the monitored market participant23110 that involves the asset available in the wallet system 23350satisfies any asset control criteria (e.g., does not violate athreshold), the EAL 23300 may generate a message data packet thatproposes an actual transaction with the market participant 23110involving the asset available. In some examples, the interface system23310 communicates the message data packet on behalf of the EAL 23300 tothe market participant 23110.

In embodiments, an EAL 23300 may be configured as a multi-tenant EAL23300, where the functions and capabilities of the EAL 23300 are madeavailable to more than one enterprise (or to more than one business unitof an enterprise), such that processing resources and facilities (e.g.,data centers and network infrastructure), operating resources (such aspersonnel), and others are shared across tenants, while the functionsand capabilities of the EAL 23300 are governed and executed withawareness of the access rights and other attributes of each tenant. Forexample, two (or more) enterprises may share an EAL 23300, such as wherethe enterprises operate in a similar domain and/or undertake similartransactions, such that the marketplaces, exchanges, or othertransactions with which the EAL 23300 are similar for the twoenterprises. The EAL 23300 may monitor usage of each tenant, provisionresources (such as according to relative priorities), maintainseparation of enterprise-specific elements (e.g., wallets of eachenterprise), handle billing transactions for usage, and the like. Inembodiments, transactions across multiple tenants may be aggregated toachieve volume discounts, with discounts being automatically allocatedand applied according to a set of rules (such as based on proportionatecontribution to transactions, or the like). In embodiments, tenancy maybe managed in a set of tiers, such as with each tier having a set ofservice levels associated therewith, such as enabling usage of givensets of functions and capabilities of the EAL 23300, setting relativeprioritization (e.g., with higher tiers being given priority wheretransactions are limited, where resources are limited, or the like), andthe like.

In embodiments, the EAL 23300 may be configured for peer-to-peerconnectivity among a set of enterprises (e.g., bilateral connectivity ormultilateral connectivity), such that the functions and capabilities ofthe EAL 23300 are configured to handle the particular types of assets,resources, workflows and transactions that occur among the enterprises.For example, a bank and a manufacturing entity may establish apeer-to-peer EAL 23300 for a set of financial transactions, includingworking capital loans, trade credit lending, handling of deposits,payroll processing, payments processing, and others. In this example,the assets of the manufacturing enterprise may be presented in a walletin the EAL 23300 that is on accessible to the manufacturing entity andto lending officers of the bank, such that the lending assets can beconfigured to be used as collateral for lending transactions. Forexample, the EAL may facilitate automated generation of sets forcollateral for a set of loans among the manufacturing enterprise and thebank. In another example, a third entity, such as a secondary lender,underwriter, insurer, or the like may be added to the EAL 23300, such asto facilitate multi-party transactions. In other embodiments, amulti-party, peer-to-peer EAL 23300 may handle transactions among a setof parties participating in a supply chain, such as tiers of componentmanufacturers that provide components of systems manufactured by an OEM.A peer-to-peer EAL 23300 may be established between a manufacturer orretailer with a set of preferred customers, such as repeat customers,such that the EAL allows the preferred customers access to viewinventories (as presented in a wallet) in a manner that has priorityover the access by the general public. The peer-to-peer EAL 23300 mayinclude governing rules that are customized to each party (e.g., settingrules for what assets and transactions are presented or permitted), mayprovision and prioritize resources (e.g., for storage, processing,networking or the like) among parties, may allocate costs, and the like.The configured services of the EAL 23300 (of any of the types describedherein), may include ones that are configured for the needs of eachparty, such as by learning on historical transactions of that partyand/or on similarly situated other parties (such as ones from similardomains). In some embodiments, the peer-to-peer EAL 23300 may be amulti-tenant, peer-to-peer EAL 23300 having features described above.

Although the EAL 23300 has been generally described with respect todigital enterprise asset functionality, the EAL 23300 is not limited todigital assets, but may also perform its functionality for non-digitalassets. For example, for a non-digital enterprise asset, the EAL 23300may facilitate non-asset transactions by: managing transactionalparties, permissions, logistics, or recordation of a transaction in somemanner; providing intermediary services (e.g., escrow services for aphysical transaction, authentication services, etc.); generating adigital means (e.g., a token or a transactional record) to indicate thata non-digital asset transaction has occurred; or processing/storingdigital files related to a non-digital asset. As previously described, aphysical resource, which may be considered a non-digital enterpriseasset, may have associated documentation (e.g., certificate ofauthenticity, proof of purchase, deed, title, etc.). With associateddocumentation that can be generated, modified, transferred, processed,and/or stored in a digital context, the EAL 23300 can function torepresent and/or manage some of all of these transactional instances.

In some implementations, the EAL 23300 may be configured to perform thetransaction and/or to generate a record of the transaction for digitalstorage. For instance, the EAL 23300 generates a record of thetransaction and stores the record on one or more blockchains (e.g.,private blockchain associated with the enterprise and/or a publicblockchain). In some configurations, similar to a digital assettransaction, when the EAL 23300 is integrated with the performance of anon-digital asset transaction, the capabilities of the EAL 23300 maygenerate records that store detailed information regarding atransaction. This detailed information may be information such as theenterprise's agent who authorized the transaction, any permissionsrequired or satisfied to perform the transaction, any governanceinvolved to perform the transaction, any decision-making intelligencerequested/relied upon to perform the transaction, any dataprocessing/data retrieval involved to perform the transaction, etc. Inother words, the detailed information can log or record servicesperformed by EAL systems or entities in cooperation with EAL systems.

Intelligence Services System

FIG. 235 illustrates an example intelligence services system 23500 (alsoreferred to as “intelligence services”) according to some embodiments ofthe present disclosure. In embodiments, the intelligence services 23500provides a framework for providing intelligence services to one or moreintelligence service clients 23536. In some embodiments, theintelligence services 23500 framework may be adapted to be at leastpartially replicated in respective intelligence clients 23536 (e.g., anenterprise access layer, a wallet system, a market orchestration system,a digital lending system, an asset-backed tokenization system, and/orthe like). In these embodiments, an individual client 23536 may includesome or all of the capabilities of the intelligence services 23500,whereby the intelligence services 23500 is adapted for the specificfunctions performed by the subsystems of the intelligence client.Additionally or alternatively, in some embodiments, the intelligenceservices 23500 may be implemented as a set of microservices, such thatdifferent intelligence clients 23536 may leverage the intelligenceservices 23500 via one or more APIs exposed to the intelligence clients.In these embodiments, the intelligence services 23500 may be configuredto perform various types of intelligence services that may be adaptedfor different intelligence clients 23536. In either of theseconfigurations, an intelligence service client 23536 may provide anintelligence request to the intelligence services 23500, whereby therequest is to perform a specific intelligence task (e.g., a decision, arecommendation, a report, an instruction, a classification, aprediction, a training action, an NLP request, or the like). Inresponse, the intelligence services 23500 executes the requestedintelligence task and returns a response to the intelligence serviceclient 23536. Additionally or alternatively, in some embodiments, theintelligence services 23500 may be implemented using one or morespecialized chips that are configured to provide AI assistedmicroservices such as image processing, diagnostics, location andorientation, chemical analysis, data processing, and so forth. Examplesof AI-enabled chips are discussed elsewhere in the disclosure.

In embodiments, an intelligence services 23500 may include anintelligence service controller 23502 and artificial intelligence (AI)modules 23504. In embodiments, an artificial intelligence services 23500receives an intelligence request from an intelligence service client23536 and any required data to process the request from the intelligenceservice client 23536. In response to the request and the specific data,one or more implicated artificial intelligence modules 23504 perform theintelligence task and output an “intelligence response”. Examples ofintelligence modules 23504 responses may include a decision (e.g., acontrol instruction, a proposed action, machine-generated text, and/orthe like), a prediction (e.g., a predicted meaning of a text snippet, apredicted outcome associated with a proposed action, a predicted faultcondition, and/or the like), a classification (e.g., a classification ofan object in an image, a classification of a spoken utterance, aclassified fault condition based on sensor data, and/or the like),and/or other suitable outputs of an artificial intelligence system.

Artificial Intelligence Modules

In embodiments, artificial intelligence modules 23504 may include an MLmodule 23512, a rules-based module 23528, an analytics module 23518, anRPA module 23516, a digital twin module 23520, a machine vision module23522, an NLP module 23524, and/or a neural network module 23514. It isappreciated that the foregoing are non-limiting examples of artificialintelligence modules, and that some of the modules may be included orleveraged by other artificial intelligence modules. For example, the NLPmodule 23524 and the machine vision module 23522 may leverage differentneural networks that are part of the neural network module 23514 inperformance of their respective functions.

It is further noted that in some scenarios, artificial intelligencemodules 23504 themselves may also be intelligence clients 23536. Forexample, a rules-based intelligence module 23528 may request anintelligence task from an ML module 23512 or a neural networkF41 module23514, such as requesting a classification of an object appearing in avideo and/or a motion of the object. In this example, the rules-basedintelligence module 23528 may be an intelligence service client 23536that uses the classification to determine whether to take a specifiedaction. In another example, a machine vision module 23522 may request adigital twin of a specified environment from a digital twin module23520, such that the ML module 23512 may request specific data from thedigital twin as features to train a machine-learned model that istrained for a specific environment.

In embodiments, an intelligence task may require specific types of datato respond to the request. For example, a machine vision task requiresone or more images (and potentially other data) to classify objectsappearing in an image or set of images, to determine features within theset of images (such as locations of items, presence of faces, symbols orinstructions, expressions, parameters of motion, changes in status, andmany others), and the like. In another example, an NLP task requiresaudio of speech and/or text data (and potentially other data) todetermine a meaning or other element of the speech and/or text. In yetanother example, an AI-based control task (e.g., a decision on movementof a robot) may require environment data (e.g., maps, coordinates ofknown obstacles, images, and/or the like) and/or a motion plan to make adecision as to how to control the motion of a robot. In a platform-levelexample, an analytics-based reporting task may require data from anumber of different databases to generate a report. Thus, inembodiments, tasks that can be performed by an intelligence services23500 may require, or benefit from, specific intelligence service inputs23532. In some embodiments, an intelligence services 23500 may beconfigured to receive and/or request specific data from the intelligenceservice inputs 23532 to perform a respective intelligence task.Additionally or alternatively, the requesting intelligence serviceclient 23536 may provide the specific data in the request. For instance,the intelligence services 23500 may expose one or more APIs to theintelligence clients 23536, whereby a requesting client 23536 providesthe specific data in the request via the API. Examples of intelligenceservice inputs may include, but are not limited to, sensors that providesensor data, video streams, audio streams, databases, data feeds, humaninput, and/or other suitable data.

In embodiments, intelligence modules 23504 includes and provides accessto an ML module 23512 that may be integrated into or be accessed by oneor more intelligence clients 23536. In embodiments, the ML module 23512may provide machine-based learning capabilities, features, functions,and algorithms for use by an intelligence service client 23536 such astraining ML models, leveraging ML models, reinforcing ML models,performing various clustering techniques, feature extraction, and/or thelike. In an example, a machine learning module 23512 may provide machinelearning computing, data storage, and feedback infrastructure to asimulation system (e.g., as described above). The machine learningmodule 23512 may also operate cooperatively with other modules, such asthe rules-based module 23528, the machine vision module 23522, the RPAmodule 23516, and/or the like.

The machine learning module 23512 may define one or more machinelearning models for performing analytics, simulation, decision making,and predictive analytics related to data processing, data analysis,simulation creation, and simulation analysis of one or more componentsor subsystems of an intelligence service client 23536. In embodiments,the machine learning models are algorithms and/or statistical modelsthat perform specific tasks without using explicit instructions, relyinginstead on patterns and inference. The machine learning models build oneor more mathematical models based on training data to make predictionsand/or decisions without being explicitly programmed to perform thespecific tasks. In example implementations, machine learning models mayperform classification, prediction, regression, clustering, anomalydetection, recommendation generation, and/or other tasks.

In embodiments, the machine learning models may perform various types ofclassification based on the input data. Classification is a predictivemodeling problem where a class label is predicted for a given example ofinput data. For example, machine learning models can perform binaryclassification, multi-class or multi-label classification. Inembodiments, the machine-learning model may output “confidence scores”that are indicative of a respective confidence associated withclassification of the input into the respective class. In embodiments,the confidence scores can be compared to one or more thresholds torender a discrete categorical prediction. In embodiments, only a certainnumber of classes (e.g., one) with the relatively largest confidencescores can be selected to render a discrete categorical prediction.

In embodiments, machine learning models may output a probabilisticclassification. For example, machine learning models may predict, givena sample input, a probability distribution over a set of classes. Thus,rather than outputting only the most likely class to which the sampleinput should belong, machine learning models can output, for each class,a probability that the sample input belongs to such class. Inembodiments, the probability distribution over all possible classes cansum to one. In embodiments, a Softmax function, or other type offunction or layer can be used to turn a set of real values respectivelyassociated with the possible classes to a set of real values in therange (0, 1) that sum to one. In embodiments, the probabilities providedby the probability distribution can be compared to one or morethresholds to render a discrete categorical prediction. In embodiments,only a certain number of classes (e.g., one) with the relatively largestpredicted probability can be selected to render a discrete categoricalprediction.

In embodiments, machine learning models can perform regression toprovide output data in the form of a continuous numeric value. Asexamples, machine learning models can perform linear regression,polynomial regression, or nonlinear regression. As described, inembodiments, a Softmax function or other function or layer can be usedto squash a set of real values respectively associated with a two ormore possible classes to a set of real values in the range (0, 1) thatsum to one. For example, machine learning models can perform linearregression, polynomial regression, or nonlinear regression. As examples,machine learning models can perform simple regression or multipleregression. As described above, in some implementations, a Softmaxfunction or other function or layer can be used to squash a set of realvalues respectively associated with a two or more possible classes to aset of real values in the range (0, 1) that sum to one.

In embodiments, machine learning models may perform various types ofclustering. For example, machine learning models may identify one ormore previously-defined clusters to which the input data most likelycorresponds. In some implementations in which machine learning modelsperforms clustering, machine learning models can be trained usingunsupervised learning techniques.

In embodiments, machine learning models may perform anomaly detection oroutlier detection. For example, machine learning models can identifyinput data that does not conform to an expected pattern or othercharacteristic (e.g., as previously observed from previous input data).As examples, the anomaly detection can be used for fraud detection orsystem failure detection.

In some implementations, machine learning models can provide output datain the form of one or more recommendations. For example, machinelearning models can be included in a recommendation system or engine. Asan example, given input data that describes previous outcomes forcertain entities (e.g., a score, ranking, or rating indicative of anamount of success or enjoyment), machine learning models can output asuggestion or recommendation of one or more additional entities that,based on the previous outcomes, are expected to have a desired outcome

As described above, machine learning models can be or include one ormore of various different types of machine-learned models. Examples ofsuch different types of machine-learned models are provided below forillustration. One or more of the example models described below can beused (e.g., combined) to provide the output data in response to theinput data. Additional models beyond the example models provided belowcan be used as well.

In some implementations, machine learning models can be or include oneor more classifier models such as, for example, linear classificationmodels; quadratic classification models; etc. Machine learning modelsmay be or include one or more regression models such as, for example,simple linear regression models; multiple linear regression models;logistic regression models; stepwise regression models; multivariateadaptive regression splines; locally estimated scatterplot smoothingmodels; etc.

In some examples, machine learning models can be or include one or moredecision tree-based models such as, for example, classification and/orregression trees; chi-squared automatic interaction detection decisiontrees; decision stumps; conditional decision trees; etc.

Machine learning models may be or include one or more kernel machines.In some implementations, machine learning models can be or include oneor more support vector machines. Machine learning models may be orinclude one or more instance-based learning models such as, for example,learning vector quantization models; self-organizing map models; locallyweighted learning models; etc. In some implementations, machine learningmodels can be or include one or more nearest neighbor models such as,for example, k-nearest neighbor classifications models; k-nearestneighbors regression models; etc. Machine learning models can be orinclude one or more Bayesian models such as, for example, naïve Bayesmodels; Gaussian naïve Bayes models; multinomial naïve Bayes models;averaged one-dependence estimators; Bayesian networks; Bayesian beliefnetworks; hidden Markov models; etc.

Machine learning models may include one or more clustering models suchas, for example, k-means clustering models; k-medians clustering models;expectation maximization models; hierarchical clustering models; etc.

In some implementations, machine learning models can perform one or moredimensionality reduction techniques such as, for example, principalcomponent analysis; kernel principal component analysis; graph-basedkernel principal component analysis; principal component regression;partial least squares regression; Sammon mapping; multidimensionalscaling; projection pursuit; linear discriminant analysis; mixturediscriminant analysis; quadratic discriminant analysis; generalizeddiscriminant analysis; flexible discriminant analysis; autoencoding;etc.

In some implementations, machine learning models can perform or besubjected to one or more reinforcement learning techniques such asMarkov decision processes; dynamic programming; Q functions orQ-learning; value function approaches; deep Q-networks; differentiableneural computers; asynchronous advantage actor-critics; deterministicpolicy gradient; etc.

In embodiments, artificial intelligence modules 23504 may include and/orprovide access to a neural network module 23514. In embodiments, theneural network module 23514 is configured to train, deploy, and/orleverage artificial neural networks (or “neural networks”) on behalf ofan intelligence service client 23536. It is noted that in thedescription, the term machine learning model may include neuralnetworks, and as such, the neural network module 23514 may be part ofthe machine learning module 23512. In embodiments, the neural networkmodule 23514 may be configured to train neural networks that may be usedby the intelligence clients 23536. Non-limiting examples of differenttypes of neural networks may include any of the neural network typesdescribed throughout this disclosure and the documents incorporatedherein by reference, including without limitation convolutional neuralnetworks (CNN), deep convolutional neural networks (DCN), feed forwardneural networks (including deep feed forward neural networks), recurrentneural networks (RNN) (including without limitation gated RNNs),long/short term memory (LTSM) neural networks, and the like, as well ashybrids or combinations of the above, such as deployed in series, inparallel, in acyclic (e.g., directed graph-based) flows, and/or in morecomplex flows that may include intermediate decision nodes, recursiveloops, and the like, where a given type of neural network takes inputsfrom a data source or other neural network and provides outputs that areincluded within the input sets of another neural network until a flow iscompleted and a final output is provided. In embodiments, the neuralnetwork module 23514 may be leveraged by other artificial intelligencemodules 23504, such as the machine vision module 23522, the NLP module23524, the rules-based module 23528, the digital twin module 23526, andso on. Example applications of the neural network module 23514 aredescribed throughout the disclosure.

A neural network includes a group of connected nodes, which also can bereferred to as neurons or perceptrons. A neural network can be organizedinto one or more layers. Neural networks that include multiple layerscan be referred to as “deep” networks. A deep network can include aninput layer, an output layer, and one or more hidden layers positionedbetween the input layer and the output layer. The nodes of the neuralnetwork can be connected or non-fully connected.

In embodiments, the neural networks can be or include one or more feedforward neural networks. In feed forward networks, the connectionsbetween nodes do not form a cycle. For example, each connection canconnect a node from an earlier layer to a node from a later layer.

In embodiments, the neural networks can be or include one or morerecurrent neural networks. In some instances, at least some of the nodesof a recurrent neural network can form a cycle. Recurrent neuralnetworks can be especially useful for processing input data that issequential in nature. In particular, in some instances, a recurrentneural network can pass or retain information from a previous portion ofthe input data sequence to a subsequent portion of the input datasequence through the use of recurrent or directed cyclical nodeconnections.

In some examples, sequential input data can include time-series data(e.g., sensor data versus time or imagery captured at different times).For example, a recurrent neural network can analyze sensor data versustime to detect or predict a swipe direction, to perform handwritingrecognition, etc. Sequential input data may include words in a sentence(e.g., for natural language processing, speech detection or processing,etc.); notes in a musical composition; sequential actions taken by auser (e.g., to detect or predict sequential application usage);sequential object states; etc. In some example embodiments, recurrentneural networks include long short-term (LSTM) recurrent neuralnetworks; gated recurrent units; bi-direction recurrent neural networks;continuous time recurrent neural networks; neural history compressors;echo state networks; Elman networks; Jordan networks; recursive neuralnetworks; Hopfield networks; fully recurrent networks;sequence-to-sequence configurations; etc.

In some examples, neural networks can be or include one or morenon-recurrent sequence-to-sequence models based on self-attention, suchas Transformer networks. Details of an exemplary transformer network canbe found athttp://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf.

In embodiments, the neural networks can be or include one or moreconvolutional neural networks. In some instances, a convolutional neuralnetwork can include one or more convolutional layers that performconvolutions over input data using learned filters. Filters can also bereferred to as kernels. Convolutional neural networks can be especiallyuseful for vision problems such as when the input data includes imagerysuch as still images or video. However, convolutional neural networkscan also be applied for natural language processing.

In embodiments, the neural networks can be or include one or moregenerative networks such as, for example, generative adversarialnetworks. Generative networks can be used to generate new data such asnew images or other content.

In embodiments, the neural networks may be or include autoencoders. Insome instances, the aim of an autoencoder is to learn a representation(e.g., a lower-dimensional encoding) for a set of data, typically forthe purpose of dimensionality reduction. For example, in some instances,an autoencoder can seek to encode the input data and then provide outputdata that reconstructs the input data from the encoding. Recently, theautoencoder concept has become more widely used for learning generativemodels of data. In some instances, the autoencoder can includeadditional losses beyond reconstructing the input data.

In embodiments, the neural networks may be or include one or more otherforms of artificial neural networks such as, for example, deep Boltzmannmachines; deep belief networks; stacked autoencoders; etc. Any of theneural networks described herein can be combined (e.g., stacked) to formmore complex networks.

FIG. 236 illustrates an example neural network with multiple layers.Neural network 23540 may include an input layer, a hidden layer, and anoutput layer with each layer comprising a plurality of nodes or neuronsthat respond to different combinations of inputs from the previouslayers. The connections between the neurons have numeric weights thatdetermine how much relative effect an input has on the output value ofthe node in question. Input layer may include a plurality of input nodes23542, 23544, 23546, 23548 and 23550 that may provide information fromthe outside world or input data (e.g., sensor data, image data, textdata, audio data, etc.) to the neural network 23540. The input data maybe from different sources and may include library data x1, simulationdata x2, user input data x3, training data x4 and outcome data x5. Theinput nodes 23542, 23544, 23546, 23548 and 23550 may pass on theinformation to the next layer, and no computation may be performed bythe input nodes. Hidden layers may include a plurality of nodes, such asnodes 23552, 23554, and 23556. The nodes in the hidden layer 23552,23554, and 23556 may process the information from the input layer basedon the weights of the connections between the input layer and the hiddenlayer and transfer information to the output layer. Output layer mayinclude an output node 23558 which processes information based on theweights of the connections between the hidden layer and the output layerand is responsible for computing and transferring information from thenetwork to the outside world, such as recognizing certain objects oractivities, or predicting a condition or an action.

In embodiments, a neural network 23540 may include two or more hiddenlayers and may be referred to as a deep neural network. The layers areconstructed so that the first layer detects a set of primitive patternsin the input (e.g., image) data, the second layer detects patterns ofpatterns and the third layer detects patterns of those patterns. In someembodiments, a node in the neural network 23540 may have connections toall nodes in the immediately preceding layer and the immediate nextlayer. Thus, the layers may be referred to as fully-connected layers. Insome embodiments, a node in the neural network 23540 may haveconnections to only some of the nodes in the immediately preceding layerand the immediate next layer. Thus, the layers may be referred to assparsely-connected layers. Each neuron in the neural network consists ofa weighted linear combination of its inputs and the computation on eachneural network layer may be described as a multiplication of an inputmatrix and a weight matrix. A bias matrix is then added to the resultingproduct matrix to account for the threshold of each neuron in the nextlevel. Further, an activation function is applied to each resultantvalue, and the resulting values are placed in the matrix for the nextlayer. Thus, the output from a node i in the neural network may berepresented as:

yi=f(Σxiwi+bi)

where f is the activation function, Σxiwi is the weighted sum of inputmatrix and bi is the bias matrix.

The activation function determines the activity level or excitationlevel generated in the node as a result of an input signal of aparticular size. The purpose of the activation function is to introducenon-linearity into the output of a neural network node because mostreal-world functions are non-linear and it is desirable that the neuronscan learn these non-linear representations. Several activation functionsmay be used in an artificial neural network. One example activationfunction is the sigmoid function σ(x), which is a continuous S-shapedmonotonically increasing function that asymptotically approaches fixedvalues as the input approaches plus or minus infinity. The sigmoidfunction σ(x) takes a real-valued input and transforms it into a valuebetween 0 and 1:

σ(x)=1/(1+exp(−x)).

Another example activation function is the tan h function, which takes areal-valued input and transforms it into a value within the range of[−1, 1]:

tan h(x)=2σ(2x)−1

A third example activation function is the rectified linear unit (ReLU)function. The ReLU function takes a real-valued input and thresholds itabove zero (i.e., replacing negative values with zero):

f(x)=max(0,x).

It will be apparent that the above activation functions are provided asexamples and in various embodiments, neural network 23540 may utilize avariety of activation functions including (but not limited to) identity,binary step, logistic, soft step, tan h, arctan, softsign, rectifiedlinear unit (ReLU), leaky rectified linear unit, parameteric rectifiedlinear unit, randomized leaky rectified linear unit, exponential linearunit, s-shaped rectified linear activation unit, adaptive piecewiselinear, softplus, bent identity, softexponential, sinusoid, sinc,gaussian, softmax, maxout, and/or a combination of activation functions.

In the example shown in FIG. 236 , nodes 23542, 23544, 23546, 23548 and23550 in the input layer may take external inputs x1, x2, x3, x4 and x5which may be numerical values depending upon the input dataset. It willbe understood that even though only five inputs are shown in FIG. 236 ,in various implementations, a node may include tens, hundreds,thousands, or more inputs. As discussed above, no computation isperformed on the input layer and thus the outputs from nodes 23542,23544, 23546, 23548 and 23550 of input layer are x1, x2, x3, x4 and x5respectively, which are fed into hidden layer. The output of node 23552in the hidden layer may depend on the outputs from the input layer (x1,x2, x3, x4 and x5) and weights associated with connections (w1, w2, w3,w4 and w5). Thus, the output from node 23552 may be computed as:

Y ₂₃₅₅₂ =f(x1w1+x2w2+x3w3+x4w4+x5w5+b ₂₃₅₅₂).

The outputs from the nodes 23554 and 23556 in the hidden layer may alsobe computed in a similar manner and then be fed to the node 23558 in theoutput layer. Node 23558 in the output layer may perform similarcomputations (using weights v1, v2 and v3 associated with theconnections) as the nodes 23552, 23554 and 23556 in the hidden layers:

Y ₂₃₅₅₈ =f(y ₂₃₅₅₂ v1+y ₂₃₅₅₄ v2+y ₂₃₅₅₆ v3+b ₂₃₅₅₈);

where Y₂₃₅₄₀ is the output of the neural network 23540.

As mentioned, the connections between nodes in the neural network haveassociated weights, which determine how much relative effect an inputvalue has on the output value of the node in question. Before thenetwork is trained, random values are selected for each of the weights.The weights are adjusted during the training process and this adjustmentof weights to determine the best set of weights that maximize theaccuracy of the neural network is referred to as training. For everyinput in a training dataset, the output of the artificial neural networkmay be observed and compared with the expected output, and the errorbetween the expected output and the observed output may be propagatedback to the previous layer. The weights may be adjusted accordinglybased on the error. This process is repeated until the output error isbelow a predetermined threshold.

In embodiments, backpropagation (e.g., backward propagation of errors)is utilized with an optimization method such as gradient descent toadjust weights and update the neural network characteristics.Backpropagation may be a supervised training scheme that learns fromlabeled training data and errors at the nodes by changing parameters ofthe neural network to reduce the errors. For example, a result offorward propagation (e.g., output activation value(s)) determined usingtraining input data is compared against a corresponding known referenceoutput data to calculate a loss function gradient. The gradient may bethen utilized in an optimization method to determine new updated weightsin an attempt to minimize a loss function. For example, to measureerror, the mean square error is determined using the equation:

E=(target−output)2

To determine the gradient for a weight “w,” a partial derivative of theerror with respect to the weight may be determined, where:

gradient=∂E/∂w

The calculation of the partial derivative of the errors with respect tothe weights may flow backwards through the node levels of the neuralnetwork. Then a portion (e.g., ratio, percentage, etc.) of the gradientis subtracted from the weight to determine the updated weight. Theportion may be specified as a learning rate “a.” Thus an exampleequation of determining the updated weight is given by the formula:

w new=w old−α∂E/∂w

The learning rate must be selected such that it is not too small (e.g.,a rate that is too small may lead to a slow convergence to the desiredweights) and not too large (e.g., a rate that is too large may cause theweights to not converge to the desired weights).

After the weight adjustment, the network should perform better thanbefore for the same input because the weights have now been adjusted tominimize the errors.

As mentioned, neural networks may include convolutional neural networks(CNN). A CNN is a specialized neural network for processing data havinga known, grid-like topology, such as image data. Accordingly, CNNs arecommonly used for classification, object recognition and computer visionapplications, but they also may be used for other types of patternrecognition such as speech and language processing.

A convolutional neural network learns highly non-linear mappings byinterconnecting layers of artificial neurons arranged in many differentlayers with activation functions that make the layers dependent. Itincludes one or more convolutional layers, interspersed with one or moresub-sampling layers and non-linear layers, which are typically followedby one or more fully connected layers.

Referring to FIG. 237 , a CNN 23560 includes an input layer with aninput image 23562 to be classified by the CNN 23560, a hidden layerwhich in turn includes one or more convolutional layers, interspersedwith one or more activation or non-linear layers (e.g., ReLU) andpooling or sub-sampling layers and an output layer—typically includingone or more fully connected layers. Input image 23562 may be representedby a matrix of pixels and may have multiple channels. For example, acolored image may have a red, a green, and blue channels eachrepresenting red, green, and blue (RGB) components of the input image.Each channel may be represented by a 2-D matrix of pixels having pixelvalues in the range of 0 to 255. A gray-scale image on the other handmay have only one channel. The following section describes processing ofa single image channel using CNN 23560. It will be understood thatmultiple channels may be processed in a similar manner.

As shown, input image 23562 may be processed by the hidden layer, whichincludes sets of convolutional and activation layers 23564 and 23568,each followed by pooling layers 23566 and 23570.

The convolutional layers of the convolutional neural network serve asfeature extractors capable of learning and decomposing the input imageinto hierarchical features. The convolution layers may performconvolution operations on the input image where a filter (also referredas a kernel or feature detector) may slide over the input image at acertain step size (referred to as the stride). For every position (orstep), element-wise multiplications between the filter matrix and theoverlapped matrix in the input image may be calculated and summed to geta final value that represents a single element of an output matrixconstituting a feature map. The feature map refers to image data thatrepresents various features of the input image data and may have smallerdimensions as compared to the input image. The activation or non-linearlayers use different non-linear trigger functions to signal distinctidentification of likely features on each hidden layer. Non-linearlayers use a variety of specific functions to implement the non-lineartriggering, including the rectified linear units (ReLUs), hyperbolictangent, absolute of hyperbolic tangent and sigmoid functions. In oneimplementation, a ReLU activation implements the function y=max(x, 0)and keeps the input and output sizes of a layer the same. The advantageof using ReLU is that the convolutional neural network is trained manytimes faster. ReLU is a non-continuous, non-saturating activationfunction that is linear with respect to the input if the input valuesare larger than zero and zero otherwise.

As shown in FIG. 237 , the first convolution and activation layer 23564may perform convolutions on input image 23562 using multiple filtersfollowed by non-linearity operation (e.g., ReLU) to generate multipleoutput matrices (or feature maps) 23572. The number of filters used maybe referred to as the depth of the convolution layer. Thus, the firstconvolution and activation layer 23564 in the example of FIG. 237 has adepth of three and generates three feature maps using three filters.Feature maps 23572 may then be passed to the first pooling layer thatmay sub-sample or down-sample the feature maps using a pooling functionto generate output matrix 23574. The pooling function replaces thefeature map with a summary statistic to reduce the spatial dimensions ofthe extracted feature map thereby reducing the number of parameters andcomputations in the network. Thus, the pooling layer reduces thedimensionality of the feature maps while retaining the most importantinformation. The pooling function can also be used to introducetranslation invariance into the neural network, such that smalltranslations to the input do not change the pooled outputs. Differentpooling functions may be used in the pooling layer, including maxpooling, average pooling, and 12-norm pooling.

Output matrix 23574 may then be processed by a second convolution andactivation layer 23568 to perform convolutions and non-linear activationoperations (e.g., ReLU) as described above to generate feature maps23576. In the example shown in FIG. 237 , second convolution andactivation layer 23568 may have a depth of five. Feature maps 23576 maythen be passed to a pooling layer 23570, where feature maps 23576 may besubsampled or down-sampled to generate an output matrix 23578.

Output matrix 23578 generated by pooling layer 23570 is then processedby one or more fully connected layer 23580 that forms a part of theoutput layer of CNN 23560. The fully connected layer 23580 has a fullconnection with all the feature maps of the output matrix 23578 of thepooling layer 23570. In embodiments, the fully connected layer 23580 maytake the output matrix 23578 generated by the pooling layer 23570 as theinput in vector form, and perform high-level determination to output afeature vector containing information of the structures in the inputimage. In embodiments, the fully-connected layer 23580 may classify theobject in input image 23562 into one of several categories using aSoftmax function. The Softmax function may be used as the activationfunction in the output layer and takes a vector of real-valued scoresand maps it to a vector of values between zero and one that sum to one.In embodiments, other classifiers, such as a support vector machine(SVM) classifier, may be used.

In embodiments, one or more normalization layers may be added to the CNN23560 to normalize the output of the convolution filters. Thenormalization layer may provide whitening or lateral inhibition, avoidvanishing or exploding gradients, stabilize training, and enablelearning with higher rates and faster convergence. In embodiments, thenormalization layers are added after the convolution layer but beforethe activation layer.

CNN 23560 may thus be seen as multiple sets of convolution, activation,pooling, normalization and fully connected layers stacked together tolearn, enhance and extract implicit features and patterns in the inputimage 23562. A layer as used herein, can refer to one or more componentsthat operate with similar function by mathematical or other functionalmeans to process received inputs to generate/derive outputs for a nextlayer with one or more other components for further processing withinCNN 23560.

The initial layers of CNN 23560 e.g., convolution layers, may extractlow level features such as edges and/or gradients from the input image23562. Subsequent layers may extract or detect progressively morecomplex features and patterns such as presence of curvatures andtextures in image data and so on. The output of each layer may serve asan input of a succeeding layer in CNN 23560 to learn hierarchicalfeature representations from data in the input image 23562. This allowsconvolutional neural networks to efficiently learn increasingly complexand abstract visual concepts.

Although only two convolution layers are shown in the example, thepresent disclosure is not limited to the example architecture, and CNN23560 architecture may comprise any number of layers in total, and anynumber of layers for convolution, activation and pooling. For example,there have been many variations and improvements over the basic CNNmodel described above. Some examples include Alexnet, GoogLeNet, VGGNet(that stacks many layers containing narrow convolutional layers followedby max pooling layers), Residual network or ResNet (that uses residualblocks and skip connections to learn residual mapping), DenseNet (thatconnects each layer of CNN to every other layer in a feed-forwardfashion), Squeeze and excitation networks (that incorporate globalcontext into features) and AmobeaNet (that uses evolutionary algorithmsto search and find optimal architecture for image recognition).

Training of Convolutional Neural Network

The training process of a convolutional neural network, such as CNN23560, may be similar to the training process discussed in FIG. 236 withrespect to neural network 23540.

In embodiments, all parameters and weights (including the weights in thefilters and weights for the fully-connected layer are initially assigned(e.g., randomly assigned). Then, during training, a training image orimages, in which the objects have been detected and classified, areprovided as the input to the CNN 23560, which performs the forwardpropagation steps. In other words, CNN 23560 applies convolution,non-linear activation, and pooling layers to each training image todetermine the classification vectors (i.e., detect and classify eachtraining image). These classification vectors are compared with thepredetermined classification vectors. The error (e.g., the squared sumof differences, log loss, softmax log loss) between the classificationvectors of the CNN and the predetermined classification vectors isdetermined. This error is then employed to update the weights andparameters of the CNN in a backpropagation process which may usegradient descent and may include one or more iterations. The trainingprocess is repeated for each training image in the training set.

The training process and inference process described above may beperformed on hardware, software, or a combination of hardware andsoftware. However, training a convolutional neural network like CNN23560 or using the trained CNN for inference generally requiressignificant amounts of computation power to perform, for example, thematrix multiplications or convolutions. Thus, specialized hardwarecircuits, such as graphic processing units (GPUs), tensor processingunits (TPUs), neural network processing units (NPUs), FPGAs, ASICs, orother highly parallel processing circuits may be used for trainingand/or inference. Training and inference may be performed on a cloud, ona data center, or on a device.

Region Based CNNs (RCNNs) and Object Detection

In embodiments, an object detection model extends the functionality ofCNN based image classification neural network models by not onlyclassifying objects but also determining their locations in an image interms of bounding boxes. Region-based CNN (R-CNN) methods are used toextract regions of interest (ROI), where each ROI is a rectangle thatmay represent the boundary of an object in image. Conceptually, R-CNNoperates in two phases. In a first phase, region proposal methodsgenerate all potential bounding box candidates in the image. In a secondphase, for every proposal, a CNN classifier is applied to distinguishbetween objects. Alternatively, a fast R-CNN architecture can be used,which integrates the feature extractor and classifier into a unifiednetwork. Another faster R-CNN can be used, which incorporates a RegionProposal Network (RPN) and fast R-CNN into an end-to-end trainableframework. Mask R-CNN adds instance segmentation, while mesh R-CNN addsthe ability to generate a 3D mesh from a 2D image.

Referring back to FIG. 235 , in embodiments, the artificial intelligencemodules 23504 may provide access to and/or integrate a robotic processautomation (RPA) module 23516. The RPA module 23516 may facilitate,among other things, computer automation of producing and validatingworkflows. The RPA module 23516 provides automation of tasks performedby humans, such as receiving and reviewing written information, enteringdata into user interfaces, converting or otherwise processing data suchas files or records, recording observations, generating documents suchas reports, and communicating with other users by mechanisms such asemail. In some cases, the tasks involve a workflow that includes anumber of interrelated steps, contextual information that relates to thetask, and interactions with other applications and humans. The RPAmodule 23516 can be configured to receive or learn one or more suchworkflows on behalf of the human and in a manner similar to the actionsand logic of the human, and can thereafter perform such workflows inresponse to various triggers such as events. Examples of RPA modules23516 may encompass those in this disclosure and in the documentsincorporated by reference herein and may involve automation of any ofthe wide range of value chain network activities or entities describedtherein.

In embodiments, an RPA module 23516 is configured to receive or learn arobotic process automation workflow in a variety of ways. As a firstexample, in embodiments, the RPA module 23516 can include a graphicaluser interface (GUI) that enables a user to specify the details of therobotic process automation workflow. The GUI can include components thatrepresent different types of actions, such as an action of receivinginput from a user or application, an action of converting or otherwiseprocessing data, and an action of providing input to an application. TheGUI can receive, from the user, a selection of components representingactions that correspond to the steps of the workflow when performed by ahuman. The GUI can also receive, from the user, an interconnection ofthe selected components, such as a logical order in which thecorresponding actions are to be performed, or a dependency of onecomponent upon another component (e.g., a first component can outputdata that is received as input by another component). The GUI caninclude one or more templates, such as one or more sequences of actionsthat are performed together to complete a common workflow. The GUI canreceive, from the user, a selection of a template, optionally includingone or more details that adapt the selected template to a particularworkflow performed by the human. Based on the input received from theuser, the RPA module 23516 can generate a robotic process automationworkflow that can be executed to perform the workflow. The RPA module23516 can store the generated workflow for future use. For example, theRPA module 23516 can execute the compiled code or interpret thegenerated script to perform the workflow in a similar manner asperformed by the human.

As a second example, in embodiments, an RPA module 23516 is configuredto receive or learn a workflow based on a set of rules. For example, theRPA module 23516 can include a GUI that enables a user to specify thedetails of the robotic process automation workflow as a set ofconditions and responsive actions. The GUI includes a set of componentsthat respond to conditions to be monitored, such as a status of aresource or an occurrence of an event. The GUI for designing theworkflows can include a set of components that represent actions to betaken in response to an occurrence of one of the conditions. The GUI canreceive, from the user, a selection of components representing one ormore of the conditions of a workflow, and a selection of one or morecomponents representing the actions to be taken in response to theconditions. In some embodiments, the GUI can include one or moretemplates, such as one or more conditions associated with one or moreactions that correspond to a common workflow. The GUI can receive, fromthe user, a selection of one of the templates, including one or moredetails that adapt the selected template to a particular workflowperformed by the human. Based on the input received from the user, theRPA module 23516 can generate a robotic process automation workflow thatautomates a set of tasks in response to one or more detected events. TheRPA module 23516 can store the generated workflow for future use. Forexample, the RPA module 23516 can monitor the selected conditions andperform the selected actions in response to an occurrence of theselected actions, in a similar manner as performed by the human.

As a third example, in embodiments, an RPA module 23516 is configured tolearn a workflow by recording a set of actions performed by a human tocomplete the workflow. For example, the RPA module 23516 can receive,from the user, an indication of a start of the workflow involving adevice, such as a selection of a Start Recording button. The RPA module23516 can receive user input from the user, such as input to one or morehuman interaction devices (HIDs) such as a keyboard, a mouse, atouchscreen, a camera, or a microphone. Alternatively or additionally,the RPA module 23516 can receive user input as a series of humaninteraction events reported by a device, such as an input layer of anoperating system that receives and aggregates user input from one ormore human input devices. Alternatively or additionally, the RPA module23516 can receive user input as a series of events reported by one ormore applications, such as a web browser that reports a set of userinput events. The RPA module 23516 can record the user input as asequence of inputs. The RPA module 23516 can associate the recorded userinput with contextual information, such as an identification of theapplication to which the user input was directed. The RPA module 23516can associate the recorded user input with other events, such aspreceding events of an application that receives the user input (e.g.,an indication by a web browser that a web page has been rendered and isavailable to receive user input) and/or responsive events of theapplication in response to receiving the user input (e.g., an actionperformed by a web page in response to receiving user input). The RPAmodule 23516 can associate the recorded user input with other eventsoccurring within the device, such as an action performed by anotherapplication or an operating system of the device in response to the userinput. The RPA module 23516 can receive, from the user, an indication ofan end of the workflow, such as a selection of a Stop Recording button.The RPA module 23516 can generate a workflow that includes a record ofthe observed user input, optionally in association with other data. TheRPA module 23516 can store the generated workflow for future use. Forexample, the RPA module 23516 can replay the sequence of recorded userinput to perform the workflow in a similar manner as performed by thehuman.

As a fourth example, in embodiments, an RPA module 23516 is configuredto learn a workflow by watching an interaction between a human and adevice. For example, a human can perform a number of workflows using thedevice over a period of time, such as a business day. The RPA module23516 can monitor the user input of the human and can identify, in theuser input, one or more patterns of actions that are repeatedlyperformed by the human. The RPA module 23516 can determine that apattern of actions corresponds to a workflow performed by the human. Insome embodiments, the RPA module 23516 can identify variations amongvarious instances of the actions when performed by the human during theworkflow, such as different types of data entry that occur in differentinstances of the actions. The RPA module 23516 can associate an actionin the workflow with one or more parameters, wherein the parameterscorrespond to the different variations among the various instances ofthe action when performed by the human. In various embodiments, the RPAmodule 23516 can determine a basis of each of the variations of theaction that are associated with different variations of the action inthe workflow. For example, the RPA module 23516 can determine that whenthe workflow is performed by the human on behalf of a first user, theaction is to be performed with a first data entry value, such as dataentry including the name of the first user. When the workflow isperformed by the human on behalf of a second user, the action is to beperformed with a second data entry value, such as data entry includingthe name of the second user. The data entry can be represented in theworkflow as a data entry parameter (e.g., a name of a user on whosebehalf the workflow is performed), optionally with specific values thatcorrespond to a context of the workflow (e.g., the names of the users onwhose behalf the workflow can be performed). The RPA module 23516 cangenerate a workflow that includes a sequence of commands that correspondto the pattern of actions performed by the user during the workflow,and, optionally, the parameters and/or parameter values of variousactions of the workflow. The RPA module 23516 can store the generatedworkflow for future use. For example, the RPA module 23516 can replaythe sequence of commands to replicate the pattern of actions thatcorrespond to the workflow when performed in a similar manner as by thehuman.

In embodiments, the RPA module 23516 can be implemented in a variety ofarchitectures. As a first example, the RPA module 23516 can beimplemented on the same device as a human uses to perform a workflow,and/or that a user uses to specify the details of a workflow. The RPAmodule 23516 can store one or more generated workflows on the device,and can perform the workflow on the same device. As a second example,the RPA module 23516 can be implemented on a first device to replicate aworkflow performed by a human on a second device. The RPA module 23516can monitor the interaction of the human with the second device whileperforming a task, generate and store a workflow on the first device,and execute the workflow on the first device to perform the task on thefirst device in a similar manner as performed by the user on the seconddevice. As a third example, the RPA module 23516 can be implemented on afirst device to generate a workflow that corresponds to a task performedby the human on the first device, and can transmit the workflow to asecond device. The workflow can cause the second device to perform thetask on the second device in a similar manner as performed by the useron the first device. As a fourth example, the RPA module 23516 can beimplemented on a second device to receive a workflow that corresponds toa task performed by the human on a first device. The RPA module 23516workflow can execute the workflow on the second device to perform thetask on the second device in a similar manner as performed by the useron the first device. In some embodiments, the RPA module 23516 can bedistributed over a set of two or more devices, such as a first portionof the RPA module 23516 that executes on a first device to generate aworkflow based on an interaction between a human and the first device,and a second portion of the RPA module 23516 that executes on a seconddevice to perform the workflow on the second device. In someembodiments, at least a portion of the RPA module 23516 can bereplicated over a plurality of devices, such as two or more devices thateach perform (e.g., concurrently and/or consecutively) a workflow thatwas generated based on an interaction between a human and a firstdevice. In some embodiments, different RPA modules 23516 executing oneach of a plurality of devices can interact to execute one or moreworkflows (e.g., a first RPA module 23516 that executes on a firstdevice to perform a first portion of a workflow, and a second RPA module23516 that executes on a second device to perform a second portion ofthe same workflow). Each RPA module 23516 can operate in a particularrole while performing at least a portion of a workflow, such as a firstRPA module 23516 that executes on a cloud edge device to receive aninput of a workflow, a second RPA module 23516 that executes on a cloudserver to process the input of the workflow, and a third RPA module23516 that executes on another cloud edge device to present an output ofthe workflow.

In embodiments, an RPA module 23516 can perform a workflow in responseto a variety of triggers. The RPA module 23516 can perform a workflow inresponse to a request of a user, such as a request to execute code orrun a particular script in order to perform a learned workflow. The RPAmodule 23516 can perform a workflow in response to a detection of apattern of activity by a human (e.g., a second workflow that is to beperformed by the RPA module 23516 in response to a completion of a firstworkflow by a human). The RPA module 23516 can perform at least aportion of a workflow in lieu of a human performing at least a portionof the workflow. For example, the RPA module 23516 can detect a start ofa workflow by a human, and can suggest to the human that the RPA module23516 perform the rest of the workflow. Upon receiving an acceptance ofthe suggestion, the RPA module 23516 can perform the entire workflow inlieu of the human, and/or one or more remaining steps of the workflowfollowing the initial steps performed by the human. The RPA module 23516can perform a workflow in response to an occurrence of a type of data(e.g., the device receiving a file that includes particular data type,such as a particular type of document or a particular type of image).The RPA module 23516 can perform a workflow in response to receiving amessage through a communication channel such as email, telephone, textmessage, gesture input received by a camera or haptic input device, orvoice input received by a microphone. The RPA module 23516 can perform aworkflow in response to receiving a request from an operating system oran application executing on the device (e.g., a request from aspreadsheet application in response to a user entering a certain type ofdata). The RPA module 23516 can perform a workflow in response to adetected event. For example, when a device recognizes a presence of aparticular human (e.g., when a camera of a device recognizes a face ofthe human), the RPA module 23516 can perform a workflow that involvesdisplaying a report for the human. The RPA module 23516 can perform aworkflow at a scheduled interval, such as once per hour or once per day.The RPA module 23516 can perform a workflow in response to a requestreceived from another workflow executed on the same device or anotherdevice (e.g., a second workflow that is to be performed upon completionof a first workflow).

In embodiments, an RPA module 23516 can perform a workflow based on avariety of inputs. The RPA module 23516 can perform a workflow based onone or more details of a trigger of the workflow. For example, if theworkflow is being performed in response to a request of a user toperform the workflow, the RPA module 23516 can perform the workflowbased on one or more details of the request. For example, if theworkflow was triggered by a request of a user to process a particulardocument, the RPA module 23516 can perform the workflow based on one ormore details of the document. If the workflow is being performed inresponse to a message or telephone call, the RPA module 23516 canperform the workflow based on an identity of the sender of the messageor the identity of the caller. If the workflow is being performed as adaily instance based on a schedule, the RPA module 23516 can perform theworkflow based on the day of the week on which the workflow is beingperformed. If a workflow is being performed in response to a detectionof a condition, the RPA module 23516 can perform the workflow based onone or more details of the condition. For example, if the condition is astorage capacity of a device that exceeds a storage capacity threshold,the RPA module 23516 can perform the workflow based on a severity of thestorage capacity condition (e.g., a remaining storage capacity of thedevice). The RPA module 23516 can perform a workflow based on a datasource, such as one or more files of a file system, one or more rows orrecords of a database, or one or more messages received by a networkinterface. If the RPA module 23516 is performing a workflow in responseto one or more events, the RPA Module 23516 can perform the workflowbased on one or more details of the event. For example, if the RPAmodule 23516 is performing a second workflow in response to a completionof a first workflow on the same device or another device, the RPA module23516 can perform the workflow based on a date or time of the completionof the first workflow, a result of the first workflow, and/or an outputof the first workflow. The RPA module 23516 can perform a workflow basedon one or more contextual details. For example, the RPA module 23516 canperform a workflow based on a detected number and identities of humanswho are present in the proximity of a device. The RPA module 23516 canperform a workflow based on data associated with an applicationexecuting on the device. For example, if the RPA module 23516 performsthe workflow based on a loading of a web page, the RPA module 23516 canperform the workflow based on data scraped from the contents of the webpage. The RPA module 23516 can perform the workflow based on observationof human actions that involve interactions with hardware elements, withsoftware interfaces, and with other elements. Observations may includefield observations as humans perform real tasks, as well as observationsof simulations or other activities in which a human performs an actionwith the explicit intent to provide a training data set or input for theRPA module 23516, such as where a human tags or labels a training dataset with features that assist the RPA module 23516 in learning torecognize or classify features or objects, among many other examples.

In embodiments, an RPA module 23516 can interact with one or moreapplications while performing the workflow. For example, the RPA module23516 can extract data from a variable or an object of an application,such as text content of a textbox in a web form or the contents of cellsin a spreadsheet. The RPA module 23516 can extract data stored within anapplication (e.g., by inspecting a memory space of the application). TheRPA module 23516 can analyze data generated as output by the application(e.g., one or more files generated by the application, one or more rowsor records of a spreadsheet generated by the application, or one or morenetwork communication messages received and/or transmitted by theapplication over a network). The RPA module 23516 can invoke anapplication programming interface (API) of the application to requestdata from the application, and can receive and analyze data provided bythe application in response to the invocation of the API. The RPA module23516 can examine one or more properties of the device on which theapplication is executing (e.g., a portion of a display of the devicesthat includes a graphical user interface of the application) to extractdata from the application. Alternatively or additionally, the RPA module23516 can provide data to an application and/or modify a behavior of anapplication while performing the workflow. For example, the RPA module23516 can generate user input that is directed to an application (e.g.,simulating a human interaction device (HID), such as a keyboard, togenerate keystrokes that are delivered to the application as userinput). The RPA module 23516 can directly transmit and/or modify data ofthe application (e.g., altering HTML, data stored in a rendered web pageto modifying the contents of the textbox, or directly modifying data inthe memory space of an application). The RPA module 23516 can requestthe operating system to interact with and/or modify the behavior of anapplication (e.g., requesting that the device start, activate, suspend,resume, close, or terminate an application). The RPA module 23516 caninvoke an API of the application to provide data to the application(e.g., invoking an API of a spreadsheet to request the entry of datainto a particular cell). The RPA module 23516 can invoke code associatedwith an application to provide data and/or modify the behavior of theapplication (e.g., executing code that is encoded in anapplication-specific programming language and embedded in a documentused by an application or invoking a stored procedure of a databaseassociated with the application). The RPA module 23516 can cause orallow an interaction with an application to be visible to a human (e.g.,the RPA module 23516 can provide user input that simulates a uservisually activating a spreadsheet application and visually typing datainto various cells of the spreadsheet application). The RPA module 23516can hide an interaction with an application from a human (e.g., visuallyhiding a window of an application while entering data into one or moretextboxes of the window of the application).

In embodiments, an RPA module 23516 can utilize a variety of logicalprocesses while performing a workflow. The RPA module 23516 canretrieve, interpret, analyze, convert, validate, aggregate, partition,render, store, and/or otherwise process data that was received and/or isassociated with the workflow. The RPA module 23516 can transmit the datato another workflow, application, or device for processing or storage,and/or can query or receive the data from another workflow, application,or device. The RPA module 23516 can apply an optical characterrecognition (OCR) process to an image (e.g., a picture of a form or adocument) to determine and extract text content from the image. The RPAmodule 23516 can apply a computer vision process to an image (e.g., aphotograph captured by a camera) to determine and extract image datafrom the image, such as detecting, recognizing, classifying, and/orlocalizing one or more objects. The RPA module 23516 can apply a speechrecognition process to a sound input (e.g., a voice input from atelephone call or a microphone) to determine and extract voice contentfrom the image, such as one or more voice commands. The RPA module 23516can apply a gesture recognition process to an input device (e.g., acamera, proximity sensor, or inertial measurement unit that detectsmovement of a hand) to determine one or more gestures performed by ahuman. The RPA module 23516 can apply a pattern recognition process todata to detect one or more patterns in the data (e.g., analyzing sensordata from a machine to detect one or more occurrences of an eventassociated with the machine, such as a movement of a moving part of themachine).

In embodiments, the RPA module 23516 performs a workflow in cooperationwith a human or another workflow. For example, a workflow can includeone or more human portions to be performed by a human and one or moreautomated portions to be performed by the RPA module 23516. The RPAmodule 23516 can first perform an automated portion and deliver a resultof the automated portion to the human so that the human can perform ahuman portion based on the result. The RPA module 23516 can receive aresult of a human portion of the workflow and can perform an automatedportion of the workflow on the result of the human portion of theworkflow. The RPA module 23516 can perform the automated portion of theworkflow concurrently with a human performing a human portion of theworkflow, and can then combine a result of the automated portion of theworkflow with a result of the human portion of the workflow. The RPAmodule 23516 can perform a first automated portion of the workflow,present a result of the first automated portion to a human for reviewand validation, and can perform a second automated portion of theworkflow based on the review and validation of the result of the firstautomated portion based on a result of the review and validation by thehuman.

In embodiments, an RPA module 23516 may learn to perform certain tasksbased on the learned patterns and processes. The RPA module 23516 canuse one or more artificial intelligence modules 23504 to perform one ormore steps of a workflow. For example, an RPA module 23516 can perform adata classification step on input data by applying a classificationneural network to the input data. An RPA module 23516 can perform apattern recognition step on input data by applying a pattern recognitionneural network to the input data. An RPA module 23516 can perform acomputer vision processing step and/or an optical character recognitionstep of a workflow by applying one or CNNs 23560 to an image. An RPAmodule 23516 can perform a sequential analysis step involving timeseries data by applying one or more recurrent neural networks (RNNs) tothe time series data. An RPA module 23516 can perform one or morenatural language processing steps on a natural-language expression(e.g., a natural-language document or a natural-language voice input) byapplying one or more transformer-based neural networks to thenatural-language expression.

In various embodiments, the RPA module 23516 uses one or more artificialintelligence modules 23504 that are untrained. For example, the one ormore artificial intelligence modules 23504 can include ak-nearest-neighbor model that determines a classification of a receivedinput based on a proximity of the received input to a collection ofother inputs with known classifications. The k-nearest-neighbor modelthen classifies the received input according to a majority of the knownclassifications of the determined k inputs that are closest to thereceived input.

In various embodiments, the RPA module 23516 uses one or more artificialintelligence modules 23504 that are trained in an unsupervised manner.For example, the workflow can include an anomaly detection step, such asdetermining a portion of a form that includes handwritten text. Ananomaly detection algorithm can partition the form into a collection ofsymbols, and can compare the symbols to distinguish between symbols thatoccur with a high frequency (e.g., machine-printed characters in a font)from symbols that occur with a low frequency (e.g., hand-printedcharacters that are unique or at least highly variable). The anomalydetection algorithm can therefore partition the form into regions thatinclude machine-printed characters and regions that include hand-printedcharacters. The RPA module 23516 can then process each region of thedocument with either an OCR module that is configured to recognizemachine-printed characters in a font or an OCR module that is configuredto recognize hand-printed characters.

In various embodiments, the RPA module 23516 uses one or more artificialintelligence modules 23504 that are specifically designed and/or trainedfor the workflow. For example, the workflow can be associated with atraining data set, and the RPA module 23516 can train one or moremachine learning models to perform the processing of the workflow basedon the training data set. In various embodiments, the RPA module 23516uses one or more pretrained artificial intelligence modules 23504 toperform the processing of the workflow. For example, the RPA module23516 can receive a partially pretrained natural language processing(NLP) machine learning model that is generally trained to recognizesentence structure and word meaning. The RPA module 23516 can adapt thepartially pretrained NLP machine learning model based onnatural-language expressions that are more specifically associated withthe workflow. The adaptation can involve applying transfer learning toan artificial intelligence module 23504 (e.g., more specificallytraining one or more classification layers in a classification portionof the NLP machine learning model while holding other portions of theNLP machine learning model constant). The adaptation can involveretraining an artificial intelligence module 23504 (e.g., retraining anentirety of an NLP machine learning model based on natural-languageexpressions that are associated with a workflow). The adaptation caninvolve generating an ensemble of artificial intelligence modules 23504to perform the workflow (e.g., two or more artificial intelligencemodules 23504, each of which performs classification of data in adifferent way, wherein an output classification of the workflow is basedon a consensus of the two or more artificial intelligence modules23504). The artificial intelligence modules 23504 can include a randomforest, in which each of one or more decision trees analyses an inputdata according to different criteria, and an output of the random forestis based on a consensus of the decision trees. The artificialintelligence modules 23504 can include a stacking ensemble, in whicheach of two or more machine learning models processes data to generatean output, and another machine learning model determines which output,among the outputs of the two or more machine learning models, is to beused as the output of processing the data.

In embodiments, the RPA module 23516 generates one or more outputs orresults of a workflow. The RPA module 23516 can generate, as output,data that can be stored by the device (e.g., as a file in a file systemor as a row or record in a database). The RPA module 23516 can generate,as output, data that is included in another data set (e.g., text enteredinto fields of a form, numbers entered into cells of a spreadsheet, ortext entered into textboxes of a web page). The RPA module 23516 cangenerate, as output, data that is transmitted to another device (e.g., asubmission of form data of a web page to a webserver). The RPA module23516 can generate, as output, data that is communicated to one or moreusers (e.g., a visual notification of a result displayed for a user ofthe device, or a message that is transmitted to a user by acommunication channel such as email, text message, or voice output). TheRPA module 23516 can generate, as output, data that modifies a behaviorof an application (e.g., a command to start, activate, suspend, resume,close, or terminate an application). The RPA module 23516 can generate,as output, data that modifies a behavior of the device or another device(e.g., a command that controls a machine, such as a printer, a camera, adevice, or an industrial manufacturing device). The RPA module 23516 cangenerate, as output, data that reflects an initial, current, or finalstatus of the workflow (e.g., a dashboard that shows a progress of theworkflow to completion, or a result of the workflow in combination withthe results of other workflows). The RPA module 23516 can generate, asoutput, one or more events (e.g., notifications to a human, anapplication, an operating system of the device, or another device as tothe progression, completion, and/or results of the workflow). The eventscan be received and further processed by the RPA module 23516 or anotherRPA module executing on the same device or another device. For example,upon completion of a first workflow, the RPA module 23516 can initiate asecond workflow based on a result and/or output of the first workflow.The RPA module 23516 can generate, as output, documentation of one ormore results of the workflow. For example, the RPA module 23516 canupdate a log to document the results and/or output of the workflow,including one or more errors, exceptions, validation failures thatoccurred during the workflow.

In embodiments, the RPA module 23516 modifies a workflow based on aperformance of the workflow. For example, the RPA module 23516 canrequest review, by a user, of one or more results of the workflow,including one or more errors, exceptions, validation failures thatoccurred during the workflow. The RPA module 23516 can deactivate one ormore steps or modules of the workflow that resulted in an error,exception, or validation failure. The RPA module 23516 can automaticallyadjust the workflow to perform future instances of the workflow based onthe completed instance of the workflow. For example, the RPA module23516 can update the workflow to improve an efficiency of the workflow,to add or remove functions to the workflow, to adjust functions of theworkflow to perform differently, to log one or more instances and/orparameters of the workflow, and/or to eliminate or reduce one or morelogical faults in the workflow. The RPA module 23516 can update one ormore artificial intelligence modules 23504 associated with the workflow.For example, the RPA module 23516 can generate or add one or moremachine learning models to the workflow to improve processing of theworkflow. The RPA module 23516 can remove one or more machine learningmodels to improve efficiency of the workflow. The RPA module 23516 canredesign and/or retrain one or more machine learning models based on aresult of the workflow. The RPA module 23516 can add one or more machinelearning models to an existing ensemble of machine learning models.

Analytics Module

In embodiments, the artificial intelligence modules 23504 may includeand/or provide access to an analytics module 23518. In embodiments, ananalytics module 23518 is configured to perform various analyticalprocesses on data output from value chain entities or other datasources. In example embodiments, analytics produced by the analyticsmodule 23518 may facilitate quantification of system performance ascompared to a set of goals and/or metrics. The goals and/or metrics maybe preconfigured, determined dynamically from operating results, and thelike. Examples of analytics processes that can be performed by ananalytics module 23518 are discussed below and in the documentincorporated herein by reference. In some example implementations,analytics processes may include tracking goals and/or specific metricsthat involve coordination of value chain activities and demandintelligence, such as involving forecasting demand for a set of relevantitems by location and time (among many others).

Digital Twin Module

In embodiments, artificial intelligence modules 23504 may include and/orprovide access to a digital twin module 23520. The digital twin module23520 may encompass any of a wide range of features and capabilitiesdescribed herein In embodiments, a digital twin module 23520 may beconfigured to provide, among other things, execution environments forand different types of digital twins, such as twins of physicalenvironments, twins of robot operating units, logistics twins, executivedigital twins, organizational digital twins, role-based digital twins,and the like. In embodiments, the digital twin module 23520 may beconfigured in accordance with digital twin systems and/or modulesdescribed elsewhere throughout the disclosure. In example embodiments, adigital twin module 23520 may be configured to generate digital twinsthat are requested by intelligence clients 23536. Further, the digitaltwin module 23520 may be configured with interfaces, such as APIs andthe like for receiving information from external data sources. Forinstance, the digital twin module 23520 may receive real-time data fromsensor systems of a machinery, vehicle, robot, or other device, and/orsensor systems of the physical environment in which a device operates.In embodiments, the digital twin module 23520 may receive digital twindata from other suitable data sources, such as third-party services(e.g., weather services, traffic data services, logistics systems anddatabases, and the like. In embodiments, the digital twin module 23520may include digital twin data representing features, states, or the likeof value chain network entities, such as supply chain infrastructureentities, transportation or logistic entities, containers, goods, or thelike, as well as demand entities, such as customers, merchants, stores,points-of-sale, points-of-use, and the like. The digital twin module23520 may be integrated with or into, link to, or otherwise interactwith an interface (e.g., a control tower or dashboard), for coordinationof supply and demand, including coordination of automation within supplychain activities and demand management activities.

In embodiments, a digital twin module 23520 may provide access to andmanage a library of digital twins. Artificial intelligence modules 23504may access the library to perform functions, such as a simulation ofactions in a given environment in response to certain stimuli.

Machine Vision Module

In embodiments, artificial intelligence modules 23504 may include and/orprovide access to a machine vision module 23522. In embodiments, amachine vision module 23522 is configured to process images (e.g.,captured by a camera) to detect and classify objects in the image. Inembodiments, the machine vision module 23522 receives one or more images(which may be frames of a video feed or single still shot images) andidentifies “blobs” in an image (e.g., using edge detection techniques orthe like). The machine vision module 23522 may then classify the blobs.In some embodiments, the machine vision module 23522 leverages one ormore machine-learned image classification models and/or neural networks(e.g., convolutional neural networks) to classify the blobs in theimage. In some embodiments, the machine vision module 23522 may performfeature extraction on the images and/or the respective blobs in theimage prior to classification. In some embodiments, the machine visionmodule 23522 may leverage classification made in a previous image toaffirm or update classification(s) from the previous image. For example,if an object that was detected in a previous frame was classified with alower confidence score (e.g., the object was partially occluded or outof focus), the machine vision module 23522 may affirm or update theclassification if the machine vision module 23522 is able to determine aclassification of the object with a higher degree of confidence. Inembodiments, the machine vision module 23522 is configured to detectocclusions, such as objects that may be occluded by another object. Inembodiments, the machine vision module 23522 receives additional inputto assist in image classification tasks, such as from a radar, a sonar,a digital twin of an environment (which may show locations of knownobjects), and/or the like. In some embodiments, a machine-vision module23522 may include or interface with a liquid lens. In these embodiments,the liquid lens may facilitate improved machine vision (e.g., whenfocusing at multiple distances is necessitated by the environment andjob of a robot) and/or other machine vision tasks that are enabled by aliquid lens.

Natural Language Processing Module

In embodiments, the artificial intelligence modules 23504 may includeand/or provide access to a natural language processing (NLP) module23524. In embodiments, an NLP module 23524 performs natural languagetasks on behalf of an intelligence service client 23536. Examples ofnatural language processing techniques may include, but are not limitedto, speech recognition, speech segmentation, speaker diarization,text-to-speech, lemmatization, morphological segmentation,parts-of-speech tagging, stemming, syntactic analysis, lexical analysis,and the like. In embodiments, the NLP module 23524 may enable voicecommands that are received from a human. In embodiments, the NLP module23524 receives an audio stream (e.g., from a microphone) and may performvoice-to-text conversion on the audio stream to obtain a transcriptionof the audio stream. The NLP module 23524 may process text (e.g., atranscription of the audio stream) to determine a meaning of the textusing various NLP techniques (e.g., NLP models, neural networks, and/orthe like). In embodiments, the NLP module 23524 may determine an actionor command that was spoken in the audio stream based on the results ofthe NLP. In embodiments, the NLP module 23524 may output the results ofthe NLP to an intelligence service client 23536.

In embodiments, the NLP module 23524 provides an intelligence serviceclient 23536 with the ability to parse one or more conversational voiceinstructions provided by a human user to perform one or more tasks aswell as communicate with the human user. The NLP module 23524 mayperform speech recognition to recognize the voice instructions, naturallanguage understanding to parse and derive meaning from theinstructions, and natural language generation to generate a voiceresponse for the user upon processing of the user instructions. In someembodiments, the NLP module 23524 enables an intelligence service client23536 to understand the instructions and, upon successful completion ofthe task by the intelligence service client 23536, provide a response tothe user. In embodiments, the NLP module 23524 may formulate and askquestions to a user if the context of the user request is not completelyclear. In embodiments, the NLP module 23524 may utilize inputs receivedfrom one or more sensors including vision sensors, location-based data(e.g., GPS data) to determine context information associated withprocessed speech or text data.

In embodiments, the NLP module 23524 uses neural networks whenperforming NLP tasks, such as recurrent neural networks, long short termmemory (LSTMs), gated recurrent unit (GRUs), transformer neuralnetworks, convolutional neural networks and/or the like.

FIG. 238 illustrates an example neural network 23500 for implementingNLP module 23524. In the illustrated example, the example neural networkis a transformer neural network. In the example, the transformer neuralnetwork 23500 includes three input stages and five output stages totransform an input sequence into an output sequence. The exampletransformer includes an encoder 23502 and a decoder 23504. The encoder23502 processes input, and the decoder 23504 generates outputprobabilities, for example. The encoder 23502 includes three stages, andthe decoder 23504 includes five stages. Encoder 23502 stage 1 representsan input as a sequence of positional encodings added to embedded inputs.Encoder 23502 stages 2 and 3 include N layers (e.g., N=6, etc.) in whicheach layer includes a position-wise feedforward neural network (FNN) andan attention-based sublayer. Each attention-based sublayer of encoder23502 stage 2 includes four linear projections and multi-head attentionlogic to be added and normalized to be provided to the position-wise FNNof encoder 23502 stage 3. Encoder 23502 stages 2 and 3 employ a residualconnection followed by a normalization layer at their output.

The example decoder 23504 processes an output embedding as its inputwith the output embedding shifted right by one position to help ensurethat a prediction for position i is dependent on positions previousto/less than i. In stage 2 of the decoder 23504, masked multi-headattention is modified to prevent positions from attending to subsequentpositions. Stages 3-4 of the decoder 23504 include N layers (e.g., N=6,etc.) in which each layer includes a position-wise FNN and twoattention-based sublayers. Each attention-based sublayer of decoder23504 stage 3 includes four linear projections and multi-head attentionlogic to be added and normalized to be provided to the position-wise FNNof decoder 23504 stage 4. Decoder 23504 stages 2-4 employ a residualconnection followed by a normalization layer at their output. Decoder23504 stage 5 provides a linear transformation followed by a softmaxfunction to normalize a resulting vector of K numbers into a probabilitydistribution 23506 including K probabilities proportional toexponentials of the K input numbers.

Additional examples of neural networks may be found elsewhere in thedisclosure.

Rules-Based Module

Referring back to FIG. 235 , in embodiments, artificial intelligencemodules 23504 may also include and/or provide access to a rules-basedmodule 23528 that may be integrated into or be accessed by anintelligence service client 23536. In some embodiments, a rules-basedmodule 23528 may be configured with programmatic logic that defines aset of rules and other conditions that trigger certain actions that maybe performed in connection with an intelligence client. In embodiments,the rule-based module 23528 may be configured with programmatic logicthat receives input and determines whether one or more rules are metbased on the input. If a condition is met, the rules-based module 23528determines an action to perform, which may be output to a requestingintelligence service client 23536. The data received by the rules-basedengine may be received from an intelligence service input source 23532and/or may be requested from another module in artificial intelligencemodules 23504, such as the machine vision module 23522, the neuralnetwork module 23514, the ML module 23512, and/or the like. For example,a rule-based module 23528 may receive classifications of objects in afield of view of a mobile system (e.g., robot, autonomous vehicle, orthe like) from a machine vision system and/or sensor data from a lidarsensor of the mobile system and, in response, may determine whether themobile system should continue in its path, change its course, or stop.In embodiments, the rules-based module 23528 may be configured to makeother suitable rules-based decisions on behalf of a respective client23536, examples of which are discussed throughout the disclosure. Insome embodiments, the rules-based engine may apply governance standardsand/or analysis modules, which are described in greater detail below.

Intelligence Services Controller and Analysis Management Module

In embodiments, artificial intelligence modules 23504 interface with anintelligence service controller 23502, which is configured to determinea type of request issued by an intelligence service client 23536 and, inresponse, may determine a set of governance standards and/or analysesthat are to be applied by the artificial intelligence modules 23504 whenresponding to the request. In embodiments, the intelligence servicecontroller 23502 may include an analysis management module 23506, a setof analysis modules 23508, and a governance library 23510.

In embodiments, an intelligence service controller 23502 is configuredto determine a type of request issued by an intelligence service client23536 and, in response, may determine a set of governance standardsand/or analyses that are to be applied by the artificial intelligencemodules 23504 when responding to the request. In embodiments, theintelligence service controller 23502 may include an analysis managementmodule 23506, a set of analysis modules 23508, and a governance library23510. In embodiments, the analysis management module 23506 receives anartificial intelligence module 23504 request and determines thegovernance standards and/or analyses implicated by the request. Inembodiments, the analysis management module 23506 may determine thegovernance standards that apply to the request based on the type ofdecision that was requested and/or whether certain analyses are to beperformed with respect to the requested decision. For example, a requestfor a control decision that results in an intelligence service client23536 performing an action may implicate a certain set of governancestandards that apply, such as safety standards, legal standards, qualitystandards, or the like, and/or may implicate one or more analysesregarding the control decision, such as a risk analysis, a safetyanalysis, an engineering analysis, or the like.

In some embodiments, the analysis management module 23506 may determinethe governance standards that apply to a decision request based on oneor more conditions. Non-limiting examples of such conditions may includethe type of decision that is requested, a geolocation in which adecision is being made, an environment that the decision will affect,current or predicted environment conditions of the environment and/orthe like. In embodiments, the governance standards may be defined as aset of standards libraries stored in a governance library 23510. Inembodiments, standards libraries may define conditions, thresholds,rules, recommendations, or other suitable parameters by which a decisionmay be analyzed. Examples of standards libraries may include, legalstandards library, a regulatory standards library, a quality standardslibrary, an engineering standards library, a safety standards library, afinancial standards library, and/or other suitable types of standardslibraries. In embodiments, the governance library 23510 may include anindex that indexes certain standards defined in the respective standardslibrary based on different conditions. Examples of conditions may be ajurisdiction or geographic areas to which certain standards apply,environmental conditions to which certain standards apply, device typesto which certain standards apply, materials or products to which certainstandards apply, and/or the like.

In some embodiments, the analysis management module 23506 may determinethe appropriate set of standards that must be applied with respect to aparticular decision and may provide the appropriate set of standards tothe artificial intelligence modules 23504, such that the artificialintelligence modules 23504 leverages the implicated governance standardswhen determining a decision. In these embodiments, the artificialintelligence modules 23504 may be configured to apply the standards inthe decision-making process, such that a decision output by theartificial intelligence modules 23504 is consistent with the implicatedgovernance standards. It is appreciated that the standards libraries inthe governance library may be defined by the platform provider,customers, and/or third parties. The standards may be governmentstandards, industry standards, customer standards, or other suitablesources. In embodiments, each set of standards may include a set ofconditions that implicate the respective set of standards, such that theconditions may be used to determine which standards to apply given asituation.

In some embodiments, the analysis management module 23506 may determineone or more analyses that are to be performed with respect to aparticular decision and may provide corresponding analysis modules 23508that perform those analyses to the artificial intelligence modules23504, such that the artificial intelligence modules 23504 leverage thecorresponding analysis modules 23508 to analyze a decision beforeoutputting the decision to the requesting client. In embodiments, theanalysis modules 23508 may include modules that are configured toperform specific analyses with respect to certain types of decisions,whereby the respective modules are executed by a processing system thathosts the instance of the intelligence services 23500. Non-limitingexamples of analysis modules 23508 may include risk analysis module(s),security analysis module(s), decision tree analysis module(s), ethicsanalysis module(s), failure mode and effects (FMEA) analysis module(s),hazard analysis module(s), quality analysis module(s), safety analysismodule(s), regulatory analysis module(s), legal analysis module(s),and/or other suitable analysis modules.

In some embodiments, the analysis management module 23506 is configuredto determine which types of analyses to perform based on the type ofdecision that was requested by an intelligence service client 23536. Insome of these embodiments, the analysis management module 23506 mayinclude an index or other suitable mechanism that identifies a set ofanalysis modules 23508 based on a requested decision type. In theseembodiments, the analysis management module 23506 may receive thedecision type and may determine a set of analysis modules 23508 that areto be executed based on the decision type. Additionally oralternatively, one or more governance standards may define when aparticular analysis is to be performed. For example, the engineeringstandards may define what scenarios necessitate a FMEA analysis. In thisexample, the engineering standards may have been implicated by a requestfor a particular type of decision and the engineering standards maydefine scenarios when an FMEA analysis is to be performed. In thisexample, artificial intelligence modules 23504 may execute a safetyanalysis module and/or a risk analysis module and may determine analternative decision if the action would violate a legal standard or asafety standard. In response to analyzing a proposed decision,artificial intelligence modules 23504 may selectively output theproposed condition based on the results of the executed analyses. If adecision is allowed, artificial intelligence modules 23504 may outputthe decision to the requesting intelligence service client 23536. If theproposed configuration is flagged by one or more of the analyses,artificial intelligence modules 23504 may determine an alternativedecision and execute the analyses with respect to the alternate proposeddecision until a conforming decision is obtained.

It is noted here that in some embodiments, one or more analysis modules23508 may themselves be defined in a standard, and one or more relevantstandards used together may comprise a particular analysis. For example,the applicable safety standard may call for a risk analysis that can useor more allowable methods. In this example, an ISO standard for overallprocess and documentation, and an ASTM standard for a narrowly definedprocedure may be employed to complete the risk analysis required by thesafety governance standard.

As mentioned, the foregoing framework of an intelligence services 23500may be applied in and/or leveraged by various entities of a value chain.For example, in some embodiments, a platform-level intelligence systemmay be configured with the entire capabilities of the intelligenceservices 23500, and certain configurations of the intelligence services23500 may be provisioned for respective value chain entities.Furthermore, in some embodiments, an intelligence service client 23536may be configured to escalate an intelligence system task to ahigher-level value chain entity (e.g., edge-level or the platform-level)when the intelligence service client 23536 cannot perform the taskautonomously. It is noted that in some embodiments, an intelligenceservice controller 23502 may direct intelligence tasks to a lower-levelcomponent. Furthermore, in some implementations, an intelligenceservices 23500 may be configured to output default actions when adecision cannot be reached by the intelligence services 23500 and/or ahigher or lower-level intelligence system. In some of theseimplementations, the default decisions may be defined in a rule and/orin a standards library.

Reinforcement Learning to Determine Optimal Policy

Reinforcement learning (RL), is a machine learning technique where anagent iteratively learns optimal policy through interactions with theenvironment. In RL, the agent must discover correct actions bytrial-and-error so as to maximize some notion of long-term reward.Specifically, in a system employing RL, there exist two entities: (1) anenvironment and (2) an agent. The agent is a computer program componentthat is connected to its environment such that it can sense the state ofthe environment as well as execute actions on the environment. On eachstep of interaction, the agent senses the current state of theenvironment, s, and chooses an action to take, a. The action changes thestate of the environment, and the value of this state transition iscommunicated to the agent by a reward signal, r, where the magnitude ofr indicates the desirability of an action. Over time, the agent builds apolicy, π, which specifies the action the agent will take for each stateof the environment.

Formally, in reinforcement learning, there exists a discrete set ofenvironment states, S; a discrete set of agent actions, A; and a set ofscalar reinforcement signals, R. After learning, the system creates apolicy, π, that defines the value of taking action aεA in state sεS. Thepolicy defines Qπ(s, a) as the expected return value for starting froms, taking action a, and following policy π.

The reinforcement learning agent is trained in a policy throughiterative exposure to various states, having the agent select an actionas per the policy and providing a reward based on a function designed toreward desirable behavior. Based on the reward feedback, the system may“learn” the policy and becomes trained in producing desirable actions.For example, for navigation policy, RL agent may evaluate its staterepeatedly (e.g., location, distance from a target object), select anaction (e.g., provide input to the motors for movement towards thetarget object), evaluate the action using a reward signal, whichprovides an indication of the of the success of the action. (e.g., areward of +10 if movement reduces the distance between a mobile systemand a target object and −10 if the movement increases the distance).Similarly, the RL agent may be trained in grasping policy by iterativelyobtaining images of a target object to be grasped, attempt to grasp theobject, evaluate the attempt, and then execute the subsequent iterationusing the evaluation of the attempt of the preceding iteration(s) toassist in determining the next attempt.

There may be several approaches for training the RL agent in a policy.Imitation learning is a key approach in which the agent learns fromstate/action pairs where the actions are those that would be chosen byan expert (e.g., a human) in response to an observed state. Imitationlearning not just solves sample-inefficiency or computationalfeasibility problems, but also makes the training process safer. The RLagent may derive multiple examples of the state/action pairs byobserving a human (e.g., navigating towards and grasping a targetobject), and uses them as a basis for training the policy. Behaviorcloning (BC), that focuses on learning the expert's policy usingsupervised learning is an example of imitation learning approach.

Value based learning approach aims to find a policy comprising asequence of actions that maximizes the expectation value of futurereward (or minimizes the expected cost). The RL agent may learn thevalue/cost function and then derives a policy with respect to the same.Two different expectation values are often referred to: the state valueV(s) and the action value Q (s,a) respectively. The state value functionV(s) represents the value associated with the agent at each statewhereas the action value function Q(s,a) represents the value associatedwith the agent at state s and performing action a. The value-basedlearning approach works by approximating optimal value (V* or Q*) andthen deriving an optimal policy. For example, the optimal value functionQ*(s, a) may be identified by finding the sequence of actions whichmaximize the state-action value function Q (s, a). The optimal policyfor each state can be derived by identifying the highest valued actionthat can be taken from each state.

π*(s)=argmaxQ*(s,a)

To iteratively calculate the value function as actions within thesequence are executed and the mobile system transitions from one stateto another, the Bellman Optimality equation may be applied. The optimalvalue function Q*(s,a) obeys Bellman Optimality equation and can beexpressed as:

Q*(s _(t) ,a _(t))=E[r _(t+1)+γmaxQ*(s _(t+1) ,a _(t+1))]

Policy based learning approach directly optimizes the policy function πusing a suitable optimization technique (e.g., stochastic gradientdescent) to fine tune a vector of parameters without calculating a valuefunction. The policy-based learning approach is typically effective inhigh-dimensional or continuous action spaces.

FIG. 239 illustrates an approach based on reinforcement learning andincluding evaluation of various states, actions and rewards indetermining optimal policy for executing one or more tasks by a mobilesystem.

At 23602, a reinforcement learning agent (e.g., of the intelligenceservices system 23600) receives sensor information including a pluralityof images captured by the mobile system in the environment. The analysisof one or more of these images may enable the agent to determine a firststate associated with the mobile system at 23604. The data representingthe first state may include information about the environment, such asimages, sounds, temperature or time and information about the mobilesystem, including its position, speed, internal state (e.g., batterylife, clock setting) etc.

At 23606, 23608, and 23610, various potential actions responsive to thestate may be determined. Some examples of potential actions includeproviding control instructions to actuators, motors, wheels, wingsflaps, or other components that controls the agent's speed,acceleration, orientation, or position; changing the agent's internalsettings, such as putting certain components into a sleep mode toconserve battery life; changing the direction if the agent is in dangerof colliding with an obstacle object; acquiring or transmitting data;attempting to grasp a target object and the like.

At 23612, 23614 and 23616, expected rewards may be determined for eachof the potential actions based on a reward function. For each of thedetermined potential actions, an expected reward may be determined basedon a reward function. The reward may be predicated on a desired outcome,such as avoiding an obstacle, conserving power, or acquiring data. Ifthe action yields the desired outcome (e.g., avoiding the obstacle), thereward is high; otherwise, the reward may be low.

The agent may also look to the future to analyze whether there may beopportunities for realizing higher rewards in the future. At 23618,23620, and 23622, the agent may determine future states resulting frompotential actions respectively at 23606, 23608, and 23610.

For each of the future states predicted at 23618, 23620, and 23622, oneor more future actions may be determined and evaluated. At steps 23624,23626, and 23628, for example, values or other indicators of expectedrewards associated with one or more of the future actions may bedeveloped. The expected rewards associated with the one or more futureactions may be evaluated by comparing values of reward functionsassociated with each future action

At 23630, an action may be selected based on a comparison of expectedcurrent and future rewards.

In embodiments, the reinforcement learning agent may be pre-trainedthrough simulations in a digital twin system. In embodiments, thereinforcement agent may be pre-trained using behavior cloning. Inembodiments, the reinforcement agent may be trained using a deepreinforcement learning algorithm selected from Deep Q-Network (DQN),double deep Q-Network (DDQN), Deep Deterministic Policy Gradient (DDPG),soft actor critic (SAC), advantage actor critic (A2C), asynchronousadvantage actor critic (A3C), proximal policy optimization (PPO), trustregion policy optimization (TRPO).

In embodiments, the reinforcement learning agent may look to balanceexploitation (of current knowledge) with exploration (of unchartedterritory) while traversing the action space. For example, the agent mayfollow an ε-greedy policy by randomly selecting exploration occasionallywith probability ε while taking the optimal action most of the time withprobability 1−ε, where ε is a parameter satisfying 0<ε<1.

Market Orchestration Architecture

Referring to FIG. 240 , a block diagram of exemplary features,capabilities, and interfaces of an exemplary deployment environment24100 of a set of cross market interaction and exchange methods andsystems 24110 is depicted. Cross market interaction and exchange methodsand systems may be configured as a portion (or portions) of one or moretransaction platforms. The exemplary embodiment in FIG. 240 depicts across market interaction and exchange method and system 24110 includingcurrency-based value normalization, cross-exchange item valuetranslation, item token generation, rights token generation and thelike. Exemplary embodiments of cross market interaction and exchangemethods and systems 24110 are depicted and described elsewhere herein.Assets (e.g., items) 24102 may represent one or more sources of assetinformation, such as item value, item characteristics, item rights, itemsmart contracts, and the like. In an exemplary transaction platformdeployment of the cross market interaction and exchange methods andsystems 24110, which is described elsewhere herein in greater detail,asset data may be processed, such as through use of robotic processautomation (R P A) 24104, to generate, for example a normalized assetvalue, a translated asset value, an asset token, an asset right token,and the like.

In embodiments, cross market interaction and exchange methods andsystems 24110 may be configured with or operationally connected to a setof application programming interfaces (APIs) through which, among otherthings, asset data may be retrieved and/or received. In exemplaryembodiments, an API may be an open/standardized API (e.g.,banking/financial institution open APIs) that, among other things, mayfacilitate integration with and into current and emerging ecosystems.Use of open/standardized APIs, while optional, may further enable crossmarket interaction and exchange methods and systems 24110 to beintegrated into a wide range of transaction workflows, such as corporateinternal workflows, inter-jurisdiction transaction workflows, and thelike.

In example embodiments, market orchestration elements 24108 mayfacilitate use of cross market interaction and exchange methods andsystems 24110 for various aspects of market orchestration, including,without limitations, software orchestrated transactions, softwareorchestrated marketplaces, and the like. Market orchestration elements24108 may facilitate deployment of cross market interaction and exchangemethods and systems 24110, such as in a web service embodiment, as anintegrated function of a market orchestration platform, such as anautomated market orchestration system of systems as described herein. Inembodiments, cross market interaction and exchange methods and systems24110 may provide cross market interaction and exchange capabilities formarket orchestration when configured as a portion of marketorchestration elements 24108 and the like.

The example deployment environment 24100 may include, reference and/orprovide cross market interaction capabilities 24110 that may enableleveraging cross market interaction and exchange principals, computationcapabilities, storage and data sourcing capabilities, as well asintelligence capabilities for cross market interactions. Cross marketinteraction capabilities 24110 may include interfaces to one or moremarketplaces, transaction environments, and the like, so that, amongother things, a data network and infrastructure pipeline 24106 may beconfigured with assets from one market in a cross market integrationdeployment as a source of data and with another market in the crossmarket integration deployment as a target recipient of the data networkand infrastructure pipeline services. In embodiments, a similararrangement may be constructed between two or more markets so that assetdata in either market can be used as a data source and can be influencedby asset data from another market. Cross market interactions 24110 maybe accomplished through one or more market-to-market data network andinfrastructure pipelines for intelligent exchange of asset data amongmarkets, such as data about assets of buyers in one market and aboutassets of sellers in another.

In the exemplary deployment environment 24100, functions and processes24112, for an exemplary market-oriented deployment may include softwareoriented transaction functions and processes, automatic transactiontransactions and processes, and the like. Functions and processes 24112for cross market interaction and exchange methods and systems 24110 mayinclude signaling availability of data (e.g., emergence of an occurrenceof asset data) that impacts data provided to a transaction operator from(for example). Other exemplary functions and processes 24112 may includeembedding cross market interaction and exchange capabilities into smartcontracts, tokens, publishing data on a schedule, or other occurrences(e.g., an initiation of a smart contract and the like). Yet otherfunctions and processes may include payments between/among machines andthe like.

In embodiments, cross market interaction and exchange methods andsystems may include and/or be associated with cross market interactionand exchange technology enablers 24114, such as 5G networking,artificial intelligence, visualization technology (e.g., VR/AR/XR),distributed ledger, and the like.

In embodiments, cross market interaction and exchange methods andsystems 24110 may include and/or leverage cloud-based virtualizedcontainerization capabilities and services 24116, such as withoutlimitation a container deployment and operation controller, such asKubernetes 24118 and the like. Cloud-based virtualized containers mayfacilitate cross market interaction and exchange resources beingdeployed close to asset data, thereby potentially reducing networkbandwidth consumption or the potential for network disturbances in adata workflow and without substantive investment in infrastructure by anoperator and/or consumer.

The exemplary deployment 24100 may further include business systeminterfaces 24120, such as APIs and the like that facilitate adoption ofcross market interaction and exchange methods and systems by enterprisesfor development, among other things of a data-centric business workflowenvironment that enables cross-functional data use, seamlessaggregation, and immediate contextualization of corporate data forindividual departments, enterprise, subsidiary, and the like. Furtherintegration of aspects of the cross market interaction and exchangemethods and systems into enterprise systems may include integration withone or more enterprise databases and the like.

Cross market interaction and exchange enabled markets 24122 may beenabled by and/or enhanced through the adoption of cross marketinteraction and exchange technology. Markets, such as markets at anintersection of financial service and physical product offerings may berevealed and/or enabled through application of this technology to helpparse, analyze, and provide intelligence for a wide range ofmarket-impacting and/or related assets. These emergent markets may besubstantively constructed as a result of application of cross marketinteraction and exchange techniques within or in association withindividual markets, and the like.

Technologies that may be provided by and/or enabled by cross marketinteraction and exchange methods and systems 24110 may includeintelligence services 24124, such as artificial intelligence, machinelearning and the like. These intelligence services 24124 may be providedin the environment 24100, or accessed (e.g., as third-party services)via one or more interfaces of the environment 24100. The cross marketinteraction and exchange methods and systems may be provided access tothese intelligence services 24124. One or more cross market interactionand exchange methods and systems 24110 may bring to the platform its ownset of intelligence services, which may be dedicated to a host crossmarket interaction and exchange system, or may be shareable among linkedsystems, for example.

In the exemplary embodiment of FIG. 240 , transaction/market orientedcapabilities, services, and deployment may include market platforms24126, transaction flows 24128, buyers 24132, sellers 24131, andtransaction/marketplace specific data network and infrastructurepipelines that enrich transactions, transaction services and the like24130. For multi-party transaction environments, a plurality of crossmarket interaction and exchange methods and systems 24110 may beconfigured and operated to satisfy a range of consumer needs for marketanalysis, transaction efficiencies, cost containment, buy/sell decisionsand the like.

Normalization within a Set of Items

Referring to FIG. 241 , computer-implemented methods and systems forautomated orchestration of one or more marketplaces may include a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges. A robotic processautomation-enabled platform for item value normalization 24200 asdepicted in FIG. 241 may receive information for a plurality of items24202 that may be available for transaction in a plurality of exchanges.The plurality of items may include sets of items. Items within a set maybe available for transaction in one or more of the plurality ofexchanges. Each set of items may be available for transactions in onemore of the plurality of exchanges as a set, such as a complete set (allitems in a set), a partial set (a subset of items from the set), ahybrid set (combinations of items from two or more sets), and the like.Transactions of the one or more sets (and/or items in a set) may bebound by aspects of exchanges in which a transaction occurs. In anexample, two exchanges may conduct transactions in two distinctcurrencies. While jurisdiction-specific currencies are contemplated,different currencies within and/or across jurisdictions are alsocontemplated, such as various types of cryptocurrencies, conventionalcurrencies, and the like. As an example, a first exchange may valueitems in a currency, such as USD. Transactions for items in the firstexchange may occur using USD. In addition to item values being based onan exchange-specific currency, fees (optional and/or mandatory) of anexchange (e.g., any of a range of fees that an exchange may charge inassociation with a transaction, some of which are described herein) maybe based on the exchange-specific currency. In example embodiments,exchange fees may be based on an item value (e.g., a sales fee of x % ofa transaction amount is charged to a transaction participant of thetransaction). A second exchange (optionally within the jurisdiction ofthe first exchange) may value items in a virtual currency, such as aform of cryptocurrency and the like. Items, services, transaction fees,and the like may also be based on one or more cryptocurrencies of thesecond exchange.

A transaction orchestration platform, such as a may be described hereinmay include capabilities for orchestrating transactions, such asanalysis, prediction, contracting services, and the like. Fees for thesepossibly optional services may also be aligned with an exchange-specificcurrency.

Transaction participants (e.g., item buyers and sellers and the like),exchange operators, transaction orchestrators, and other transactionfacilitators may benefit from normalizing item values for sets of itemsthat may be transacted through exchanges with distinct currencies. Whilecross-currency exchange rates may help participants determine some costsfor exchanges with different currencies, normalizing item values mayallow participants in a plurality of exchange-currency specificexchanges to determine aspects of item value, such as relative valuesand the like. Examples of a form of item value normalization across aplurality of exchanges is depicted in FIG. 241 . The item valuenormalization platform 24200 may process item values for a plurality ofsets of items 24202 to deliver a normalized value for items within theset. The item value normalization platform 24200 may deliver anormalized value for items within the set that are further normalizedfor a plurality of currencies of the exchanges. In an example, value ofitems in a first set of items maybe normalized within the set for aplurality of exchange currencies, such as normalized values of items ofSET A may be normalized for a currency of exchange X 24204 and for acurrency of exchange Y 24206. A range of normalization approaches may beapplied. An exemplary approach involves normalizing values for items ina set against one of the items in the set (e.g., a reference item), sothat a value of each item is represented relative to reference itemvalue. Item value normalization within a set may further includenormalization for item value for a given currency. In an example, valuesof items in the SET A may be based on a first currency (a referencecurrency). The normalized value of items in SET A for the referencecurrency may be processed by the platform 24200 for a second currency(e.g., exchange Y currency) to produce normalized values of items in SETA against the reference item for exchange Y currency.

An item normalization system 24208 may process item values (e.g.,reference item values and the like) for a plurality of currencies;thereby producing a plurality of currency-specific normalized itemvalues for items in a set against a reference item value. In the exampleof the embodiments depicted by FIG. 241 , a plurality of sets of items24202 (e.g., item set A represented by items A1, A2, and A3, item set Brepresented by items B1, B2, and B3, and item set C represented by itemsC1, C2, and C3) may be processed by the item in a set normalization fora currency system 24208. Representative items in one or more of theexemplary plurality of sets of items (Sets A, B, and C) may be processedfor normalization of values within a set, thereby producing, forexample, a plurality of item value normalized currency-specific itemsets. In the embodiments of FIG. 241 , exemplary sets A, B, and C may beprocessed for normalization for exchange X currency 24204 to produceitem sets (item value sets) AX, BX, and CX 24212. Likewise, exemplaryitem sets A, B, and C may be processed for normalization within exchangeY currency 24206 to product items sets AY, BY, and CY 24214.

Normalization of item values within an item set, and optionally within aplurality of item sets, may include identifying one of the items in eachset to be normalized as a reference item. In example embodiments, itemA1 may be identified as a reference item for item value normalization ofitems within item set A. Item B2 may be selected for set B, and so forthso that at least one item is selected as a reference item for item valuenormalization in each set of items to be processed for item valuenormalization.

Determination of a reference item in a set (or a plurality of item sets)may be based on factors, such as item value in a given currency (e.g.,the lowest valued item in the given currency). A reference itemdetermination factor may include an item transaction history. Items withmeasurable transaction history may be valued based on marketplacefactors, such as an exchange participant's perception of item value.Therefore, use of item transaction history may provide a value basisthat may align well with a likely exchange value for the item, which maysuggest exchange value for other items in the set. A reference itemdetermination factor may include a degree of commonality of an item toother items in a set. As an example, if an item shares features,physical aspects, capabilities, and the like with a majority of otheritems in a set, designating it as a reference item may facilitatedetermining relative values for other items in the set based on, forexample, differences, such as more or few features than such a referenceitem. Yet another reference item determination factor may be a degree ofinterest in the item, such as by exchange participants or by othermeasures (e.g., social media expressed interest and the like). Selectinga popular item as a reference item may enable value normalization ofother, potentially less popular items in a set.

Further, an item selected as a reference item in a first set for a firstcurrency may be different than an item selected as a reference item inthe first set for a second currency. In an example, due at least in partto currency exchange rates, an item in a first currency with a valuethat is below a minimum monetary unit in a second currency may bepreferred as a reference item in the first currency but not for thesecond currency, due at least in part to avoiding fractional valueditems as reference items. Likewise, an item selected as a reference itemfor an item set in a first exchange may be different than an itemselected as a reference item in the item set in a second exchange.Exchange factors that may impact selection of a reference item mayinclude regional/jurisdictional differences, exchange participantpreferences and the like.

The example embodiments depicted in FIG. 241 may include automation ofitem value normalization actions, such as those described herein asbeing performed by and/or enabled by the item normalization system 24208and the like. Automation may be provided by and/or enabled by roboticprocess automation techniques as may be performed by a robotic processautomation system 24210. The robotic process automation system 24210 mayinclude capabilities, features, structures, methods, algorithms,techniques for learning, human activity emulation, and the like that maybe similar to other robotic process automation systems described herein.In the embodiments of FIG. 241 , the robotic process automation system24210 may preform normalization of values of items in a plurality ofsets of items, including, for example, selection of a reference item inan item set, normalization of values of other items in the item set,selection of a reference item in a plurality of item sets, normalizationof values of other items in the plurality of items sets, and the like.As an example, normalization of items B1, B2, and B3 of item set B (inthe plurality of item sets 24202) for exchange currency Y 24206 may beperformed through application of automation capabilities of the roboticprocess automation system 24210, optionally without human intervention.In example embodiments, the robotic process automation system 24210 mayautonomously perform item value normalization for one or more items in aset of items for one or more currencies, such as currencies associatedwith one or more exchanges (e.g., exchange X currency 24204 and/orexchange Y currency 24206).

Normalization Across Sets of Items

Referring to FIG. 242 , computer-implemented methods and systems forautomated orchestration of one or more marketplaces may include a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges. A robotic processautomation-enabled platform for item value normalization 24300 asdepicted in FIG. 242 may receive information for a plurality of items24302 that may be available for transaction in a plurality of exchanges.The plurality of items may include sets of items. Items within a set maybe available for transaction in one or more of the plurality ofexchanges. Each set of items may be available for transactions in onemore of the plurality of exchanges as a set, such as a complete set (allitems in a set), a partial set (a subset of items from the set), ahybrid set (combinations of items from two or more sets), and the like.Transactions of the one or more sets (and/or items in a set) may bebound by aspects of exchanges in which a transaction occurs. In anexample, two exchanges may conduct transactions in two distinctcurrencies. While jurisdiction-specific currencies are contemplated,different currencies within and/or across jurisdictions are alsocontemplated, such as various types of cryptocurrencies, conventionalcurrencies, and the like. As an example, a first exchange may valueitems in a currency, such as USD. Transactions for items in the firstexchange may occur using USD. In addition to item values being based onan exchange-specific currency, fees (optional and/or mandatory) of anexchange (e.g., any of a range of fees that an exchange may charge inassociation with a transaction, some of which are described herein) maybe based on the exchange-specific currency. In example embodiments,exchange fees may be based on an item value (e.g., a sales fee of x % ofa transaction amount is charged to a transaction participant of thetransaction). A second exchange (optionally within the jurisdiction ofthe first exchange) may value items in a virtual currency, such as aform of cryptocurrency and the like. Items, services, transaction fees,and the like may also be based on one or more cryptocurrencies of thesecond exchange.

Transaction participants (e.g., item buyers and sellers and the like),exchange operators, transaction orchestrators, and other transactionfacilitators may benefit from normalizing item values for sets of itemsthat may be transacted through exchanges with distinct currencies. Whilecross-currency exchange rates may help participants determine some costsfor exchanges with different currencies, normalizing item values mayallow participants in a plurality of exchange-currency specificexchanges to determine aspects of item value, such as relative valuesand the like. Examples of a form of item value normalization across aplurality of exchanges is depicted in FIG. 242 . The item valuenormalization platform 24300 may process item values for a plurality ofitem sets 24302 to deliver a normalized value for items across theplurality of item sets. The item value normalization platform 24300 maydeliver a normalized value for items across the plurality of item setsthat are further normalized for a plurality of currencies of exchangesfor which item value normalization is requested. In an example, value ofitems in a first set of items maybe normalized across a plurality ofitem sets for a plurality of exchange currencies, such as normalizedvalues of items across SETs A, B, and C may be normalized for a currencyof exchange X 24304 and for a currency of exchange Y 24306. A range ofnormalization approaches may be applied for both cross-set item valuenormalization for a single currency as well as for cross-set item valuenormalization for multiple currencies. An exemplary approach involvesnormalizing values for items in a first set against one or more of itemsin a second set (e.g., a reference set), so that a value of each item inthe first set is represented relative to the one or more items in thereference set. Item value normalization across item sets may furtherinclude normalization for item value for a given currency. In anexample, values of items in the SET A may be based on a first currency(a reference currency). The normalized value of items in SET A for thereference currency may be processed by the platform 24300 for a secondcurrency (e.g., exchange Y currency) to produce normalized values of SETA items against the reference set for exchange Y currency.

An item normalization system 24308 may process item values (e.g.,reference set item values and the like) for a plurality of currencies;thereby producing a plurality of currency-specific normalized itemvalues for items in a set against a reference set. In the example of theembodiments depicted by FIG. 242 , a plurality of sets of items 24302(e.g., item set A represented by items A1, A2, and A3, item set Brepresented by items B1, B2, and B3, and item set C represented by itemsC1, C2, and C3) may be processed by cross-set item value normalizationsystem 24308. Representative items in one or more of the exemplaryplurality of sets of items (Sets A, B, and C) may be processed fornormalization of values across the item sets, thereby producing, forexample, a plurality of item value normalized currency-specific itemsets. In the embodiments of FIG. 242 , exemplary sets A, B, and C may beprocessed for normalization for exchange X currency 24304 to produceitem sets (item value sets) AX, BX, and CX 24312. In this example,values of items in sets B and C are normalized for currency X againstvalues of items in reference set A. Likewise, exemplary item sets A, B,and C may be processed for normalization within exchange Y currency24306 to product items sets AY, BY, and CY 24314. In this example,values of items in sets A and C are normalized, for currency Y againstvalues of items in reference set B.

Normalization of item values across item sets may include identifyingone of the item sets in the plurality of item sets to be normalized as areference item set. In example embodiments, one or more items in itemset A, including any or all of the items in item set A, may beidentified as a member of a reference set for item value normalizationof items across a plurality of sets. Items in set B may alternatively beselected as items in a reference, and so forth.

Determination of a reference set may be based on factors, such as itemvalues for a set in a given currency (e.g., the lowest valued set in thegiven currency). A reference set determination factor may include anitem set transaction history. Item sets with a measurable transactionhistory may be valued based on marketplace factors, such as an exchangeparticipant's perception of item set value. Therefore, use of item settransaction history may provide a value basis that may align well with alikely exchange value for item set, which may suggest an exchange valuefor other item sets in the plurality of item sets. A reference setdetermination factor may include a degree of commonality of a items in aset to items in other sets. As an example, if items in a first set sharefeatures, physical aspects, capabilities, and the like with a majorityof other items in other item sets, designating the first set as areference set may facilitate determining relative values for other itemsin other sets based on, for example, differences, such as more or fewfeatures than items in the reference set. Yet another reference setdetermination factor may be a degree of interest in one or more of theitems in the set (and/or the set as a whole), such as by exchangeparticipants or by other measures (e.g., social media expressed interestand the like). Selecting a popular item set as a reference set mayenable value normalization of other, potentially less popular item sets.

Further, an item set selected as a reference set for a first currencymay be different than a set selected as a reference set for a secondcurrency. In an example, due at least in part to currency exchangerates, an item set in a first currency with a value that is below aminimum monetary unit in a second currency may be preferred as areference set in the first currency but not for the second currency, dueat least in part to avoiding fractional valued item sets as referencesets. Likewise, an item set selected as a reference set in a firstexchange may be different than an item set selected as a reference setin a second exchange. Exchange factors that may impact selection of areference set may include regional/jurisdictional differences, exchangeparticipant preferences and the like.

The example embodiments depicted in FIG. 242 may include automation ofitem value normalization actions, such as those described herein asbeing performed by and/or enabled by the item normalization system 24308and the like. Automation may be provided by and/or enabled by roboticprocess automation techniques as may be performed by a robotic processautomation system 24310. The robotic process automation system 24310 mayinclude capabilities, features, structures, methods, algorithms,techniques for learning, human activity emulation, and the like that maybe similar to other robotic process automation systems described herein.In the embodiments of FIG. 242 , the robotic process automation system24310 may preform normalization of values of items across a plurality ofsets of items, including, for example, selection of a reference set,normalization of values of other items in the plurality of item sets,and the like. As an example, normalization of items B1, B2, and B3 ofitem set B (in the plurality of item sets 24302) for exchange currency Y24306 may be performed through application of automation capabilities ofthe robotic process automation system 24310, optionally without humanintervention. In example embodiments, the robotic process automationsystem 24310 may autonomously perform item value normalization for oneor more items across a plurality of item sets for one or morecurrencies, such as currencies associated with one or more exchanges(e.g., exchange X currency 24304 and/or exchange Y currency 24306).

Normalization Across Currencies

Referring to FIG. 243 , computer-implemented methods and systems forautomated orchestration of one or more marketplaces may include a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on andoptionally across the native currencies of the respective exchanges. Arobotic process automation-enabled platform for item value normalization24400 as depicted in FIG. 243 may receive information for a plurality ofsets of items 24402 and 24403 that may be available for transaction in aplurality of exchanges. The information may include exchangecurrency-specific item values and the like. Items within a set may beavailable for transaction in one or more of the plurality of exchanges.Each set of items may be available for transactions in one more of theplurality of exchanges as a set, such as a complete set (all items in aset), a partial set (a subset of items from the set), a hybrid set(combinations of items from two or more sets), and the like.Transactions of the one or more sets (and/or items in a set) may bebound by aspects of exchanges in which a transaction occurs. In anexample, two exchanges may conduct transactions in two distinctcurrencies. While jurisdiction-specific currencies are contemplated,different currencies within and/or across jurisdictions are alsocontemplated, such as various types of cryptocurrencies, conventionalcurrencies, and the like. As an example, a first exchange may valueitems in a first currency, such as USD. Further, transactions for itemsin the first exchange may occur using USD. A second exchange may supporttransactions, item values, and the like in both USD andcryptocurrencies. In addition to item values being based on anexchange-specific currency, fees (optional and/or mandatory) of anexchange (e.g., any of a range of fees that an exchange may charge inassociation with a transaction, some of which are described herein) maybe based on the exchange-specific currency. In example embodiments,exchange fees may be based on an item value (e.g., a sales fee of x % ofa transaction amount is charged to a transaction participant of thetransaction). A second exchange (optionally within the jurisdiction ofthe first exchange) may value items in a virtual currency, such as aform of cryptocurrency and the like. Items, services, transaction fees,and the like may also be based on one or more cryptocurrencies of thesecond exchange. In example embodiments, item values for transactions inan exchange may be expressed for a plurality of currencies when morethan one currency is supported by the exchange. Likewise, values foreach item and/or each set of items that are transactable through aplurality of exchanges may be expressed in a plurality of exchangecurrencies. In the embodiments of FIG. 243 , values for item sets 24402may be expressed in exchange X currency units. In this example, valuesfor items in set AX in the plurality of sets 24402 may be stated as AX1for item A1, AX2 for item A2, AX3 for item A3, and the like. Likewise,values for item sets 24403 may be expressed in exchange Y currency units(e.g., a value for item A1 of set AY may be stated as AY1, etc.).

Transaction participants (e.g., item buyers and sellers and the like),exchange operators, transaction orchestrators, and other transactionfacilitators may benefit from normalizing item values for items in setsof items that may be transacted through exchanges with distinctcurrencies. While cross-currency exchange rates may help participantsdetermine some costs for exchanges with different currencies,normalizing item values in and across exchange currencies may allowparticipants in a plurality of exchange-currency specific exchanges todetermine aspects of item value, such as normalized item relative valueswithin and across exchanges and the like. Examples of a form of itemvalue normalization across a plurality of exchanges is depicted in FIG.243 . The item value normalization platform 24400 may process itemvalues for a plurality of item sets 24402 and 24403 to deliver anormalized value for items across a plurality of currencies (andoptionally within an item set and/or across item sets. The item valuenormalization platform 24400 may deliver a normalized value for itemsacross the plurality of currencies that may be further normalized withcurrencies of exchanges for which item value normalization is requested.In an example, currency-specific (e.g., exchange currency-specific)value of one or more items in one or more item sets of items maybenormalized across a plurality a plurality of other exchange currencies.In the example, exchange X currency-specific values of items in one ormore of SETs AX, BX, and CX 24402 may be normalized for a currency ofexchange Y 24406; thereby producing cross-currency normalized item setsAX(Y), BX(Y), and CX(Y) 24412. Similarly, exchange Y currency-specificvalue of items in one more of sets AY, BY, and CY 24403 may benormalized for currency of exchange X 24404; thereby producingcross-currency normalized item sets AY(X), BY(X), and CY(X) 24414.Specifically, value of item A1 of set A may be stated as AX1 forcurrency X and may be stated as AY1 for currency Y. When cross-currencynormalization is performed by item normalization across currenciessystem 24408, item A1 X currency value AX1 can be normalized based oncurrency Y to product item value AX(Y)1. In example embodiments, any ofitem values in the set of items 24402 may be normalized for currency X.Likewise, any of item values in the set of items 24403 may be normalizedfor currency Y. Therefore, item normalization across currencies system24408 may process normalized item values and/or non-normalized itemvalues when generating cross-currency normalized item values stated initem value sets 24412 and/or 24414.

A range of normalization approaches may be applied for item valuenormalization for and/or across multiple currencies. An exemplaryapproach involves normalizing values for items in a one or more itemsets for a given currency (e.g., a reference currency) so that a valueof each item is represented relative to the reference currency. In theexample of FIG. 243 , values of items in the SET A may be stated for aplurality of reference currencies. Value of items in item sets 24402 maybe stated for currency X, which may be a first reference currency.Likewise values of items in item set 24403 may be stated for currency Y,which may be a second reference currency. The value of items in SET Afor the first reference currency (X) may be processed by the platform24400 for a second currency (e.g., exchange Y currency) to producenormalized values of SET A items against the reference currency forexchange Y currency 24412. Similarly the value of items in SET A for thesecond reference currency (Y) may be processed by the platform 24400 fora second currency (e.g., exchange X currency) to produce normalizedvalues of SET A items against the reference currency for exchange Xcurrency 24414.

Normalization of item values across currencies may include identifyingone of the currencies as a reference currency. The example embodimentsof FIG. 243 suggests either currency X or currency Y as a referencecurrency. However, a currency other than a currency in which item valuesare stated and other than a currency in which item values are to benormalized may be identified as a reference currency. In an example,currency X and currency Y may be currencies used to state item valuesand for use in an exchange for transacting items. A reference currencymay be selected (e.g., currency Z). A statement of relationship amongthe currencies, or at least between reference currency Z and each ofcurrencies X and Y may be applied when performing cross-currencynormalization to achieve currency-specific normalization for use in oneor more exchanges. A statement of relationship may include a currencyexchange rate, and the like.

Determination of a reference currency may be based on factors, such asstability of a currency that may be determined from currency exchangehistory, futures values, volatility score, geopolitical factors, and thelike. Determination of a reference currency may be based exchange ratesand/or costs for exchanging currencies. As an example, if an exchangerate for currency X into a plurality of currencies, is favorable over anexchange rate for currency Y for the plurality of currencies, currency Xmay be selected as a reference currency. Another example ofdetermination of a reference currency may be based on support for thecurrency by a given exchange. In this example, referring to FIG. 243 ,currency X 24404 may be selected as a reference currency for normalizingitem values for exchange X and currency Y 24406 may be selected as areference currency for normalizing item values for exchange Y. In thisway, the item value normalization across currencies system 24408 mayprocess item values differently for different exchanges, due at least inpart to currency support of each different exchange.

Further, currency may be selected as a reference currency due at leastin part to relative currency valuation. If, a minimum monetary unit of afirst currencies is greater than a minimum monetary unit of a secondcurrency, the second currency may be selected as the reference currencyso that normalized values may be expressed in terms of multiples of thereference currency (rather than fractions of the reference currency).

The example embodiments depicted in FIG. 243 may include automation ofitem value normalization actions, such as those described herein asbeing performed by and/or enabled by the item normalization system 24408and the like. Automation may be provided by and/or enabled by roboticprocess automation techniques as may be performed by a robotic processautomation system 24410. The robotic process automation system 24410 mayinclude capabilities, features, structures, methods, algorithms,techniques for learning, human activity emulation, and the like that maybe similar to other robotic process automation systems described herein.In the embodiments of FIG. 243 , the robotic process automation system24410 may preform normalization of values of items across currencies,including, for example, selection of a reference currency, normalizationof values of items relative to the reference currency, and the like. Asan example, normalization of values for items in set 24402 acrossexchange X currency 24404 and/or exchange Y currency 24406 may beperformed through application of automation capabilities of the roboticprocess automation system 24410, optionally without human intervention.In example embodiments, the robotic process automation system 24410 mayautonomously perform item value normalization for one or more itemsacross a plurality of currencies, such as currencies associated with oneor more exchanges (e.g., exchange X currency 24404 and/or exchange Ycurrency 24406).

Value Translation

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems are provided having a set of robotic processautomation services that are configured to automatically translate thevalue of an item represented in a first exchange into a value of theitem for representation in a second exchange. Item value representationmay include a range of aspects of value, such as an exchange currencyvalue (e.g., an amount of a currency of an exchange in which the itemsis being represented that a participant in the exchange may ascribe toan item). This aspect of value may be represented as a sale price, apurchase price, or generally an amount for changing (temporarily,conditionally, permanently, or otherwise) one or more rights (e.g.,ownership rights) of the item. Such an aspect of value may berepresented as a plurality of aspect-related values. IN an example, avalue for taking ownership of an item may be different (e.g., involve adifferent amount of an exchange currency) than a value for a limited useof the item, which may be different than other aspects of value of anitem. Further, a value, such as an amount of an exchange currency toparticipate in a transaction of/for the item (e.g., purchase the item)may vary relative to a currency of a different exchange. Therefore valuetranslation for representation of a value in a first exchange of an iteminto a value in a second exchange of an item may involve more thanmerely converting from a first exchange currency to a second exchangecurrency.

Further, a value of an item may be based on a wide range of factors andtherefore may be impacted by more than just currency exchange rates. Inexample embodiments, a value of an item may be a multi-dimensionalcurrency-based value, such as a transaction cost plus an ongoing cost,such as to operate/use/consume the item over time. An automobile may bean example of such an item for which a value includes both a purchasevalue and an operating value. Ongoing value of an item may includefinancing terms (e.g., if a buyer finances a transaction for an item,fees of financing may impact the value). Ongoing costs may vary from oneexchange jurisdiction to another, thereby impacting translation of avalue of an item from one exchange to another. In an example,fuel-related costs in a first jurisdiction may be higher or lower thanthose in a second jurisdiction. Therefore, translating value for an itemin an exchange of a low fuel cost jurisdiction to be represented in anexchange of a high fuel cost jurisdiction may take into considerationdifferences in fuel costs. Many other ongoing costs may factor intotranslation of a value of an item.

Robotic process automation-based value translation may includetranslation of aspects of value of an item that may be time-dependent,such as an expiration date, a production sequence (e.g., serial) numberof the item, and the like. In example embodiments, time-dependent valueaspects may be determined from one or more algorithms that take intoaccount time variation when determining value of an item. An example oftime-dependent value translation may include market-based changes incurrency exchange between the currencies of two exchanges. Representinga value of an item in a target exchange (e.g., that supports a targetcurrency), where the item value is translated from an originatingexchange (e.g., that supports an originating currency) may requiredynamic representation based on ongoing currency exchange variation.Further, independent of currency variation, an originating exchangevalue for an item may vary due to a range of factors that may be outsideof direct control of the exchange, such as quality reports forcomparable items transacted in the originating exchange. Therefore, as arepresentation of originating exchange item value varies, translation ofthe item value from the originating exchange item value for presentationin a target exchange may dynamically make a corresponding change in atarget exchange value.

In example embodiments, translation of item value from a first exchangefor representation of item value in a second exchange may includedependencies, such as volume dependencies, jurisdiction ruledependencies, market-specific (e.g., costs) dependencies (e.g., tariffs,and the like). Volume, such as a relative volume of an item available inan exchange may impact translation of value from a first exchange to asecond exchange. As an example, a volume of available product in firstexchange (from which item value is being translated) may be limited. Avolume of available product in a second exchange (to which item value isbeing translated) may not be limited, at least not comparably to volumelimits of the first exchange. Translation of item value in such asituation may result in a volume-based adjustment in item value duringtranslation. In this example, a value in a first exchange of the itemmay, based on supply and demand pricing principles generally, notdirectly translate based on currency exchange rates. Rather, due togreater availability of the item in the second exchange, a translatedvalue for the item may result in a value that is lower (potentiallysubstantively lower) might be suggested based on currency exchangerates.

In example embodiments, tariffs may impact item value translation.Tariffs imposed on items being moved from a first jurisdiction (e.g.,where an item is available for transaction in a first exchange) to asecond jurisdiction (e.g., where the item is desired to be transacted)may impact translation of value of the item. One such impact may beadding the value of the tariff to the value of the item as part of valuetranslation. Generally, a nature of tariffs is that they are no evenlyapplied across all types of items even from a single jurisdiction.Therefore, translation of value of an item may include determining if atariff applies, the amount of the tariff, and conditions for imposingthe tariff (e.g., a non-profit buyer may not pay the full tariff).

In embodiments, translation of value of an item from a first exchange torepresent a value of the item in a second exchange may includeconditional value factors. In example embodiments, conditional valuefactors may be outside of a control sphere of the second exchange.Aspects, such as seasonal factors, weather factors, geopoliticalstability, and the like may impact a representation of value of an item.In an example for an impact of seasonal factors on value, an item thatprovides essential features for one season (e.g., a thermally insulatedcoat for a Winter season) may have a representation of value in a firstexchange based on the seasonal conditions locally for the first exchange(e.g., northern climates during Winter). Translating a value for thisitem to a second exchange (e.g., where the local season is Summer) mayimpact (e.g., reduce) a represented value for the item in the secondexchange more substantively than may occur due to currency exchangesrates, for example. Geopolitical factors may impact item valuetranslation. In example embodiments, risks of timely delivery of an itemrepresented in a first jurisdiction that lacks political stability to arecipient in a second jurisdiction where a transaction for the itemoccurs at least in part in an exchange in the second jurisdiction mayimpact a translation of value of an item. In an example, an operator ofthe exchange in the second jurisdiction may require imposing additionalfees in such cases (e.g., to secure timely transfer, and the like),thereby impacting the translation of value of the item.

In example embodiments, translation of value of an item from arepresentation of value of an item in a first exchange to represent avalue for an item in a second exchange may include exchange-basedfactors. Exchange-based factors may include, exchange fees (e.g.,overhead associated with transactions for items in an exchange),value-impacting conditions (e.g., compulsory insurance), and the like.Translation of value of an item represented in a high-overhead exchangemay factor differences in exchange overhead when representing a value ina low-overhead exchange. Translation of value may, in this regard,consider and evaluate contributors to item value differently based onexchange. Whereas a core value of an item may be one contributor to arepresentation of item value in a first exchange, exchange overhead forthe item in the first exchange may be separated from the representationof value during value translation. In an example, a currency exchangerate-based factor may be applied to the core value, and a targetexchange overhead may supplement the translated core value to arrive ata representation of value of the item in the target exchange, leavingthe first exchange high overhead out of the final representation ofvalue translation.

For translation of value of multiple similar items, aspects thatdifferentiate the otherwise similar items may impact value translation.In an example, translation of value of bottles of wine may varysubstantively based on, for example, vintage of individual bottles.Value for a case of wine with mixed vintage bottles (or a collection ofitems grouped into a set of mixed vintage bottles of wine) may betranslated differently than for single vintage bottles (e.g., where asingle translation factor may be applied to each of the bottles). Inanother example, translating a value of identical items from aproduction line may be impacted by aspects of the items, such as aserial number of the item. A first serial number, a milestone-typeserial number (e.g., 1,000,000), a final production serial number, andthe like may be valued differently than other items from the productionrun. In example embodiments, determining such aspects of items for valuetranslation may provide improved value translation for representation ofan item value in a target exchange.

Item value may further be based on a perception of a participant in atransaction for the item. In a simple example, a value of an item to abuyer may be different than a value to a seller of the item. In exampleembodiments, taxation may impact an exchange participant differently indifferent exchanges. A value to an exchange participant in a firstexchange may include no sales tax, whereas a sales tax may be imposed ontransactions conducted in a target exchange. Therefore, valuetranslation may factor tax treatment of transactions in the twoexchanges into a representation of value. Another miscellaneous valuethat may impact an exchange participant's perception of value mayinclude access to credit/funding, such as with favorable borrower termsfor conducting transactions in an exchange. Translating a representationof value of an item in a first exchange that includes one or more ofthese factors that are different from value-impacting factors of targetexchange may include adaptation of a representation of a value of theitem in the target exchange to include such supplemental value aspects.

Item value for translation among exchanges may include post-transactionvalue. An example of such value differentiation may includepost-transaction value. Post-transaction value for items transacted in afirst exchange may include residual income based on use of the item. Ina second exchange, post-transaction value may be derived from accrual ofenergy/carbon credits based on item use. Yet another example ofpost-transaction value that may impact value translation may includebenefits to, for example a seller for use of an exchange. A value to aseller in a first exchange may include advertising/promotional creditsfor future use with the exchange. One such example credit may includeaccess to social media influencers associated with the exchange. Apost-transaction value to a seller in a second exchange may includeaccrual of credits toward exchange fees that may be used by the seller,transferred to other sellers, and the like. Yet another example ofpost-transaction factors that may impact value translation may includetransaction fee basis differences. A transaction fee may be applied to atransaction at the successful conclusion of the transaction (e.g., thebuyer releases funds to the seller). In a first exchange the fee may betransaction amount-based; in a second exchange the may be fixed or atleast partially independent of the transaction amount.

Item Value Translation

Referring to FIG. 244 , embodiments are depicted of a set of roboticprocess automation services that are configured to automaticallytranslate the value of an item represented in a first exchange into avalue of the item for representation in a second exchange. Left side ofthe figure generally depicts a first exchange value representation; theright side generally depicts a second or target exchange item valuerepresentation. For discussion of the embodiments of FIG. 244 , a firstexchange may include Exchange X 24602; a second/target exchange mayinclude Exchange Y 24504. Value translation may be performed by a valuetranslation system 24510. The value translation system 24510 may accessexchange X metadata 24506 that may include one or more of the factorsdescribed above, such as transaction fees, access to funding, meaning ofterminology, historical treatment of some factors, supported currencies,and the like that are associated with item value representationassociated with Exchange X 24502. The value translation system 24510 mayaccess a comparable data store of exchange item value representationassociated metadata 24508 for exchange Y 24504. The value translationsystem 24510 may, among other things, process the exchange metadata(e.g., X exchange metadata 24506 and/or Y exchange metadata 24508, orportions thereof) to develop an understanding of how X exchange itemvalue representation compares and/or relates to Y exchange item valuerepresentation. In an example, the value translation system 24510 maydetermine from such processing that exchange overhead that may berepresented in item values for exchange Y may be a multiple of exchangeoverhead for exchange X. This value translation-impacting determinationmay be based using a comparative process for overhead values stated foreach exchange, (e.g., 1% for exchange X and 1.2% for exchange Y).Determining one or more relationships of exchange overhead to item valuerepresentation for translating value my include parsing descriptiveinformation (e.g., Exchange X overhead description: “An exchangetransaction fee of one percent of transaction amount is included in eachtransaction”; exchange Y overhead description: “Participants arerequired to pay a monthly exchange participant fee of one percent ofaverage trailing month transaction amount”) and making an adjustment toa representation of exchange overhead value impact based on, for exampleinformation captured from the metadata datasets 24506 and/or 24508.

As variously described herein, a representation of an item value may bemultidimensional. Item value representation for translation from a firstexchange may include value dimension associated with an intrinsic valueof an item 24512. This may, for example, be an amount that a currentowner of the item paid for the item (e.g., in an earlier transaction inthe first or another exchange). An intrinsic amount for an item ofcurrency (e.g., digital/crypto currency) may be a derived from aquantity of the item of currency and a currently discernable value of aunit of the corresponding currency. In example embodiments, an intrinsicvalue 24512 dimension of a representation of value of an item in a firstexchange may be market-based, such as based on intrinsic valuedimensions of comparable items. In this example, transaction history mayplay a role in determining an intrinsic value dimension of arepresentation of item value. Comparable item sales may generallysuggest an intrinsic value dimension. A relevance and impact of anintrinsic value dimension of item value representation may, as describedherein in examples and for embodiments depicted in the figures, beimpacted by a range of factors, including, without limitation a quantityof the items available in the exchange (supply) and a degree of demandfor the items. Similarly, an intrinsic value dimension of valuerepresentation may be influenced by similarity of an item to other itemsin the source exchange, the target exchange, or generally known by thevalue translation system 24510. Intrinsic differences in items (e.g.,expected service life, damage history, specific features/upgrades, andthe like) may determine, at least in part, how an intrinsic valuedimension of an item in a first exchange is translated and impacts arepresentation of value of the item in a second exchange. The valuetranslation system 24510 may process intrinsic value dimension 24512information, along with intrinsic value-impacting metadata whenproducing a representation of value of an item including a targetexchange intrinsic value dimension 24522.

In example embodiments, other value determining dimensions that mayimpact translation and representation of item value across exchanges mayinclude, for a first exchange (e.g., exchange X 24502) a first exchangeseller value dimension 24514, a first exchange buyer value dimension24516, a first exchange platform value dimension 24518, and the like.

The value translation system 24510 may be operated, such as by anautonomous robotic process automation system 24509, to translate eachdimension of value in a representation of value of an item in a firstexchange into a corresponding dimension of value for item valuerepresentation in a second/target exchange. Translation of one or morefirst exchange seller value dimensions 24514 into corresponding targetexchange seller value dimension(s) 24524 may include determination offirst exchange seller value dimension 24514 factors and a contributionof at least a portion of such factors on a first exchange representationof item value. In a non-limiting example, a seller of an item in a firstexchange for which item value is to be translated by the valuetranslation system 24510, may have to pay a listing fee for the firstexchange. This item of seller value dimension 24514 may not be presentin the target exchange. Therefore translation of a first exchange sellervalue dimension 24514 to a corresponding second exchange seller valuedimension 24524 may include elimination of a listing fee factor impacton the item value representation in the second exchange. In anotherexample of translation of item value that may be impacted by sellervalue factors, currency preferences of the seller may be such a factor.In this example, a seller (e.g., the same seller/item owner in each ofthe first and target exchanges) may prefer to receive payments incryptocurrency. The first exchange may not support cryptocurrencytransactions, so the seller of the item in the first exchange mustinclude in a seller value dimension transaction value in the form of anon-preferred currency. The implications of performing transactions inthe first exchange may result in the seller setting a value (e.g., ahigher sale price) that takes into consideration a measure of onus tothe owner. Translation of item value may take into consideration thisimpact on a seller value dimension of item value. In this example, arobotic process automation-based autonomous item value translation maydetermine that a seller would adapt his/her perspective on item valuebased on the second/target exchange supporting a seller-preferredcurrency; in this example, a cryptocurrency.

Translating item value for representation from a first exchange valuerepresentation to a second exchange item value representation mayinclude translating a first exchange buyer value dimension 24516 todetermine a corresponding second/target exchange buyer value dimension24526. While generally, buyers and sellers may express different valuesfor an item, such as for the purposes of each of the buyer and sellermaximizing, for example, value of a transaction, which may include ahigher sale price from the seller's perspective and a lower price fromthe buyer's perspective. Further when translating a value of an item forrepresentation in a second exchange, second exchange buyer valuedimension factors may impact a buyer's perspective value differentlythan another buyer's perspective of item value in a first exchange. Arange of buyer value dimension 24526 impacting factors are describedherein. In example, a potential buyer in a first exchange may have readyaccess to multiples of an item in a first exchange. A potential buyer ina second exchange may have limited access to the item. In this simpleexample, a first exchange buyer value dimension 24516 may have anegligible impact on item value representation. However, a secondexchange buyer value dimension 24526 may have a substantive impact onitem value in the second exchange. The translation system 24510 maydetect/determine these two different buyer value dimension factors andadjust a representation of value during translation accordingly. Otherbuyer value dimensions (first exchange 24514, second exchange 24524)dimensions may include tax differences, and the like.

Another candidate dimension of value that contributes to arepresentation of item value in a first exchange may include a firstexchange value dimension 24518. Exchange value dimension 24518 mayinclude exchange-based impacts on item value, such as support inoperations of the first exchange for promoting, transacting, financing,and delivery of an item. If, for example, support for promoting andfinancing transactions for the item is available in a first exchange andis not available in a second exchange, translation of item value fromthe first exchange to the second exchange may be influenced by thisdifference in exchange value. In this example, a representation of valueof an item in the first exchange may include a representation of theexchange support factors; however, a corresponding representation ofitem value in the second exchange may not. For autonomous valuetranslation, such as by a robotic process automation system 24509,detecting and adjusting value of an item in a second exchange that doesnot include comparable exchange value 24528 may result in arepresentation of value that expresses these differences in exchangevalue.

In example embodiments, value translation, such as through a roboticprocess automation system 24509 operating a value translation system24510 may rely on, among other things, artificial intelligence-basedvalue translation algorithms 24520. These algorithms 24520 may beconfigured, adapted, and maintained through use of machinelearning-based feedback, such as from a feedback system 24530. As anexample, a translation algorithm that facilitates conversion of aplurality of dimensions of item value from a representation of itemvalue in first exchange for generating a representation of value of theitem in a second exchange may benefit from feedback, such as relevanceof a result of translation of one or more of the value dimensions, suchas intrinsic, seller, buyer, and exchange. A feedback system 24530 maygather information from a second exchange, such as information thatfacilitates validation of translation algorithms that validatetranslation actions, such as translating one or more aspects ofintrinsic value. Techniques for gathering feedback may include capturingdata from a plurality of sensors disposed logically and/or physicallythroughout aspects of a second exchange, such as sensors that detectbidding by buyers for an item for which value was translated. Feedbackthat suggests that, for example, buyers generally place bids that areconsistent with a representation of value of the item, reinforces valuetranslation algorithms 24520 used to generate translated value. Feedbackfrom, for example, smart contract configured for automating transactionsof items in a second exchange (e.g., terms associated with exchangefees) may validate one or more translation actions, such as translatinga first exchange exchange-value 24518 to a second exchangeexchange-value 24528. Disclosed herein are a range of feedback-basedmachine learning techniques that may be applied to the methods andsystems of value translation of the embodiments of FIG. 244 .

Value Translation and Conditional External Factors

Referring to FIG. 245 , embodiments are depicted of a set of roboticprocess automation services that are configured to automaticallytranslate the value of an item represented in a first exchange into avalue of the item for representation in a second exchange while takinginto consideration external factors that may result in a translation ofvalue that is conditional based on the external factors. For discussionof the embodiments of FIG. 245 , a first exchange may include Exchange X24602; a second/target exchange may include Exchange Y 24604. Valuetranslation, including producing a conditional value based on externalfactors, may be performed by a value translation system 24610. The valuetranslation system 24610 may access exchange X metadata 24606 that mayinclude one or more of the factors described above, such as transactionfees, access to funding, meaning of terminology, historical treatment ofsome factors, supported currencies, and the like that are associatedwith item value representation associated with Exchange X 24602. Thevalue translation system 24610 may access a comparable data store ofexchange item value representation associated metadata 24608 forexchange Y 24604. The value translation system 24610 may, among otherthings, process the exchange metadata (e.g., X exchange metadata 24606and/or Y exchange metadata 24608, or portions thereof) to develop anunderstanding of how X exchange item value representation comparesand/or relates to Y exchange item value representation. In an example,the value translation system 24610 may determine from such processingthat exchange overhead that may be represented in item values forexchange Y may be a multiple of exchange overhead for exchange X.Further exchange overhead may be influenced by external factors that maysuggest translation of value may be conditional. This valuetranslation-impacting determination may be based using a comparativeprocess for overhead values stated for each exchange, (e.g., 1% forexchange X and 1.2% for exchange Y). Determining one or morerelationships of exchange overhead to item value representation fortranslating value my include parsing descriptive information (e.g.,Exchange X overhead description: “An exchange transaction fee of onepercent of transaction amount is included in each transaction”; exchangeY overhead description: “Participants are required to pay a monthlyexchange participant fee of one percent of average trailing monthtransaction amount”) and making an adjustment to a representation ofexchange overhead value impact based on, for example informationcaptured from the metadata datasets 24606 and/or 24608.

As variously described herein, a representation of an item value may bemultidimensional. Further, external value factors 24632, such as factorsoutside of at least direct control of an exchange (and/or an operator orparticipant of an exchange) may influence one or more dimensions of itemvalue. Item value representation for translation from a first exchangemay include value dimension associated with an intrinsic value of anitem 24612. This may, for example, be an amount that a current owner ofthe item paid for the item (e.g., in an earlier transaction in the firstor another exchange). An intrinsic amount for an item of currency (e.g.,digital/crypto currency) may be a derived from a quantity of the item ofcurrency and a currently discernable value of a unit of thecorresponding currency. In example embodiments, an intrinsic value 24612dimension of a representation of value of an item in a first exchangemay be based on one or more external factors, such as after-markettrading activity (e.g., private sales) of the item, comparable items andthe like. An external value factor 24632 that may impact intrinsic valuedifferently for different exchanges may include weather-baseddifferences between typical use environments of the exchanges. An itemthat has a high intrinsic value in an exchange that is experiencingwinter conditions may have a low intrinsic value for an exchangeexperiencing mild weather.

A relevance and impact of an intrinsic value dimension of item valuerepresentation may be impacted by a range of external factors,including, without limitation a quantity of the items available tobuyers in a jurisdiction of an exchange (supply) and a degree of demandfor the items. Similarly, an intrinsic value dimension 24612 of valuerepresentation may be influenced by similarity of an item to other itemsin the source exchange, the target exchange, external to either or bothexchanges, and otherwise generally known by the value translation system24610. Intrinsic differences in items (e.g., expected service life,damage history, specific features/upgrades, after-sale offers that areoutside of the exchange, and the like) may determine, at least in part,how an intrinsic value dimension of an item in a first exchange istranslated and impacts a representation of value of the item in a secondexchange. External value factors 24632, such as a likelihood of anenvironmental event/weather conditions and the like, particularly whenthose external factors differ among exchanges, may impact translation ofan intrinsic dimension of value. The value translation system 24610 mayprocess intrinsic value dimension 24612 information, along withintrinsic value-impacting metadata, and optionally with intrinsicvalue-impacting external value factors 24632 when producing arepresentation of value of an item including a target exchange intrinsicvalue dimension 24522.

In example embodiments, other value determining dimensions that mayimpact translation and representation of item value across exchanges mayinclude, for a first exchange (e.g., exchange X 24602) a first exchangeseller value dimension 24614, a first exchange buyer value dimension24616, a first exchange platform value dimension 24618, and the like.Factors that may impact translation of one or more of the first exchangedimensions of value may include external value factors 24632 and/orresults of processing the external factors 24632, such as with aconditional value processing system 24634.

The value translation system 24610 may be operated, such as by anautonomous robotic process automation system 24609, to translate eachdimension of value in a representation of value of an item in a firstexchange into a corresponding dimension of value for item valuerepresentation in a second/target exchange. Robotic processautomation-based translation may also include generating a conditionalvalue factor 24634 and applying that factor to the translation of one ormore first exchange seller value dimensions 24614 into correspondingtarget exchange seller value dimension(s) 24624 may includedetermination of first exchange seller value dimension 24614 factors anda contribution of at least a portion of such factors on a first exchangerepresentation of item value. In a non-limiting example, a seller of anitem in a first exchange for which item value is to be translated by thevalue translation system 24610, may have to pay a listing fee to alisting service that is external to the first exchange. This externalfactor of seller value dimension 24624 may not be present in the targetexchange. Therefore translation of a first exchange seller valuedimension 24614 to a corresponding second exchange seller valuedimension 24624 may include elimination of an external service listingfee factor impact on the item value representation in the secondexchange.

Translating item value for representation from a first exchange valuerepresentation to a second exchange item value representation mayinclude translating a first exchange buyer value dimension 24616 todetermine a corresponding second/target exchange buyer value dimension24626. Further when translating a value of an item for representation ina second exchange, second exchange buyer value dimension factors mayimpact a buyer's perspective value differently than another buyer'sperspective of item value in a first exchange. A range of buyer valuedimension 24626 impacting factors, at least some of which may beinfluenced by external value factors 24632 are described herein. Inexample, a quantity of items available to a potential buyer in a firstexchange may be limited by external factors, such as rules that limit anumber of items purchased based on an economic classification of thebuyer (e.g., quantities allowed for foreign nationals may be limited). Apotential buyer in a second exchange may not have such limits on accessto the item. In this simple example, a first exchange buyer valuedimension 24616 may have a conditional impact on item valuerepresentation (e.g., economic classification). However, a secondexchange buyer value dimension 24626 may have a no such impact on itemvalue in the second exchange. The translation system 24610 maydetect/determine these two different buyer value dimension factors andadjust a representation of value during translation accordingly. Otherexternal factor influenced buyer value dimensions (first exchange 24614,second exchange 24624) dimensions may include tax differences, and thelike.

Another candidate dimension of value that contributes to arepresentation of item value in a first exchange may include a firstexchange value dimension 24618. Exchange value dimension 24618 mayinclude exchange-based impacts on item value, such as support inoperations of the first exchange for promoting, transacting, financing,and delivery of an item. If, for example, support for promoting andfinancing transactions for the item is available in a first exchange andis not available in a second exchange, external value factors (e.g.,third-parties providing these services) may influence translation ofitem value from the first exchange to the second exchange.

In example embodiments, value translation, such as through a roboticprocess automation system 24609 operating a value translation system24610 may rely on, among other things, artificial intelligence-basedvalue translation algorithms 24620. These algorithms 24620 may beconfigured, adapted, and maintained through use of machinelearning-based feedback, such as from a feedback system 24630. Thesealgorithms 24630 may further be adapted based on external value factors24632 that may be structured as a conditional value impact 24634.

In an example of feedback of an impact of value 24630, a translationalgorithm that facilitates conversion of a plurality of dimensions ofitem value from a representation of item value in first exchange forgenerating a representation of value of the item in a second exchangemay benefit from feedback, such as relevance of a result of translationof one or more of the value dimensions, such as intrinsic, seller,buyer, and exchange. A feedback system 24630 may gather information froma second exchange, such as information that facilitates validation oftranslation algorithms that validate translation actions, such astranslating one or more aspects of intrinsic value. Techniques forgathering feedback may include capturing data from a plurality ofsensors disposed logically and/or physically throughout aspects of asecond exchange, such as sensors that detect bidding by buyers for anitem for which value was translated. Feedback that suggests that, forexample, buyers generally place bids that are consistent with arepresentation of value of the item, reinforces value translationalgorithms 24620 used to generate translated value. Feedback from, forexample, smart contract configured for automating transactions of itemsin a second exchange (e.g., terms associated with exchange fees) mayvalidate one or more translation actions, such as translating a firstexchange exchange-value 24618 to a second exchange exchange-value 24628.Disclosed herein are a range of feedback-based machine learningtechniques that may be applied to the methods and systems of valuetranslation of the embodiments of FIG. 244 .

In an example of external value factor 24632 impact on valuetranslation, such as an impact of a conditional value factor 24634, animpending weather event in a target use market of items purchased in asecond exchange may increase an impact of one or more of the dimensionsof value. Further as a risk of the impending weather event increases orabates (which may be expressed as an external value factor conditionalvalue 24634, a translation algorithm 24620 may be adapted accordingly.

Token Generation from Item Characteristics

In example embodiments, provided herein are computer-implemented methodsand systems for automated orchestration of one or more marketplaces,such methods and systems are provided having a set of robotic processautomation services that are configured to generate a token thatrepresents an item in an exchange based on characteristics of the itemdetermined from data from a different exchange. An item may be containand/or be associated with and/or be represented by a plurality ofcharacteristics. Characteristics of an item that may be used to generatea token representing the item in an exchange may include a range oftypes of characteristics. Just a few examples include: physicalcharacteristics (e.g., size, weight, volume, quantity, material, etc.),value-based characteristics (e.g., cost to purchase, cost to operate,tax value, energy value, collectability, etc.), accessibilitycharacteristics (e.g., when an item is accessible, for now long, underwhat conditions can the item be accessed, and the like). In exampleembodiments, employing robotic process automation services toautonomously generate a token that represents an item in an exchange maybenefit from an understanding of the various types of characteristics,their relative importance (e.g., weight) for a first/source exchange andfor a second/target exchange.

Further, token generation may be performed for one or more purposes, atleast a few of which may impact a token generation process, optionallyincluding determining which characteristics of the item to rely uponwhen generating a token for representing the item. As an example, if anobjective of token generation is to maximize transaction value of theitem, characteristics that are associated with enhanced valuation of anitem may be usefully processed to generate the token. If, for example,an object of generating a token is to achieve a very quick transaction(e.g., sale, rental, etc.) of the item, characteristics that engendersuch interest may be a focus of a representative token generationprocess. In yet another example, if an objective of generating a tokenthat is representative of an item is to attract certain candidatebuyers, corresponding characteristics may be a substantive part ofrepresentative token generation.

Referring to FIG. 246 , embodiments of methods and systems forgenerating tokens that are representative of one or items based at leastin part on characteristics of the items are depicted. More specifically,embodiments for methods and systems of robotic process automation-basedtoken generation are depicted. In example embodiments, token generationfor representation of an item in a second exchange may be based oncharacteristics of the item determined from data from a first (e.g.,different) exchange. The embodiments depicted in FIG. 246 may include afirst exchange X 24702 from which item characteristics 24706 areretrieved for token generation, e.g., via token generation system 24712that may produce item token 24714 for exchange y 24704. At least oneobjective of such a system may be to configure a robotic processautomation system to capture item characteristics 24706 of an item thatis available in a first exchange; process those characteristics; andproduce an item token 24714 that is useful in representing the item in atarget exchange, such as exchange Y 24704.

In example embodiments, an item of a source exchange 24702 isidentified, such as by designation through a user interface,automatically, semi-automatically, and the like. Generally independentof a means by which an item is identified for representative tokengeneration, given that an item is identified, a robotic processautomation process may automate collection of characteristics of theitem 24706 from the first exchange 24702. Characteristics collection maybe based on one or more mechanisms by which a first exchange makesinformation that is descriptive and/or contains characteristics of anitem. In example, a first exchange 24702 may provide and/or makeavailable a directory (e.g., list and the like) of items in theexchange. The list may include a set of characteristics for the item,such as identifying characteristics, physical characteristics, ownershipcharacteristics, performance/service characteristics, pricing and salesterms characteristics, and the like. Therefore, in an example ofrepresentative item token generation, a method of token generation,optionally through operation of a robotic process automation system24710, may retrieve item characteristics 24706 of an identified item(not shown) and deliver the same to a token generation system 24712whereat one or more item representative tokens 24714 may be generated.

The token generation system 24712 may process one or more retrieved itemcharacteristics (e.g., characteristics of an item that is represented ina first exchange 24702) for generating an item token 24714 along withcharacteristics rules 24708 of a target exchange (e.g., exchange Y 24704in the embodiments of FIG. 246 ). Target exchanges may maintain the setof characteristics rules 24708 to facilitate, among other things,representing an item based on item characteristics, and optionally foruse and/or understanding of item characteristics. The token generationsystem 24712 may query one or more control facilities, such as anexchange control tower of a target exchange to retrieve characteristicsrules that are pertinent to token generation. In an example ofcharacteristic rule querying, an item characteristic retrieved for anitem for which a representative token is being generated may indicate adimension of the item in imperial units. The token generation system24712 may generate a query (e.g., a lookup type data structure) thatindicates information about the characteristic, such as the type ofcharacteristic, data of the instance of the type of characteristic, andthe like. In this example the token generation system may present aquery data structure that includes at least a type attribute (physicaldimension), and optionally a value attribute (3.1), and a unit attribute(e.g., imperial inches). The token generation system 24712 may access atarget exchange characteristics rules data structure 24708, such as bycomparing one or more of the query data structure elements to entries inthe rules data structure 24708. Alternatively, the query data structuremay be presented to the exchange control tower or other exchangemanagement facility whereat a search or other action is taken of a rulesdata structure to provide a corresponding characteristic rule for use ingenerating a representative token based at least in part on the specificitem characteristic. Further in this example, a correspondingcharacteristics rule may indicate that for compliance with physicaldimension characteristics of items in the target exchange, units forsuch values are to be metric. When this rule is applied, the tokengeneration system 24712 may convert the item dimension of 3.1 inches toa corresponding quantity of metric dimension (e.g., 78.74 mm); therebycausing the physical dimension characteristic to be represented inand/or by the generated item token 24714 in metric units.

Another example of use of characteristics rules by a token generationsystem 24712 may include minimum quantity characteristics. An itemcharacteristic may indicate that a minimum transaction quantity of theitem in a transaction is based on item count (e.g., a single item). Acharacteristic rule may require that a minimum transaction quantity ofan item in the target exchange be based on transaction amount.Therefore, based on item cost, when applying the minimum transactionquantity rule, a minimum item count for a transaction to be representedby the generated item token 24714 may be greater than a single item.

A generated item token 24714 that is representative of the item may beencoded with the item characteristics. Characteristics of an item thatindicate physical aspects of the item may be encoded as displayableelements in the token (e.g., color, general shape, relative size, andthe like). Characteristics that are time-related (e.g., expiration date,automatic renewal date, best-by date, offer validity timeframe, changesin characteristics over time, and the like) may be encoded as termsand/or conditions of a corresponding smart contract. Characteristicsthat are value related (e.g., purchase costs, operating costs, residualfees, return fees, finance fees, and the like) may be encoded in thetoken. In example embodiments, a generated item token 24714 mayreference a companion set of item characteristics 24716 that areassociated with token. The item characteristics 24706, processed andadjusted as needed based on exchange rules 24708 by the token generationsystem 24712 may be output to a characteristics data structure 24716that is linked to a generated token 24714.

In example embodiments, the methods and systems of characteristics-basedtoken generation may be performed by a robotic process automationservice 24710 that may be trained on a set of token generation actions,such as actions taken by humans, the token generation system 24712 andthe like. Robotic process automation services 24710 may facilitateautonomous generation of representative tokens based on itemcharacteristics of an item of a source exchange for use in a targetexchange. Robotic process automation services 24710, when combined withthe token generation system 24712 capabilities and processing systemsmay automate conversion of a plurality of sets of item characteristicsin a first exchange to item-representative tokens in a second exchange.

Token Generation from Item Token with Characteristic HarvestingAlgorithm

Referring to FIG. 247 , embodiments of methods and systems forgenerating tokens that are representative of one or items based at leastin part on characteristics of the items are depicted. More specifically,embodiments for methods and systems of robotic process automation-basedtoken generation are depicted. In example embodiments, token generationfor representation of an item in a second exchange may be based oncharacteristics of the item determined from data extracted from a token(e.g., a digital representation) of the item from a first (e.g.,different) exchange. The embodiments depicted in FIG. 247 may include afirst exchange X 24802 that includes and/or makes use of item tokens24806 from which item characteristics are determined for tokengeneration, e.g., via token generation system 24812 that may produce atarget exchange item token 24814 for exchange y 24804. At least oneobjective of such a system may be to configure a robotic processautomation system to harvest item characteristics from a token 24806 ofan item that is available in a first exchange; process thosecharacteristics; and produce a target exchange item token 24814 (andoptionally a companion set of item characteristics 24816) that is usefulin representing the item in a target exchange, such as exchange Y 24804.

In example embodiments, an item of a source exchange 24802 isidentified, such as by designation through a user interface,automatically, semi-automatically, and the like. Generally independentof a means by which an item is identified for representative tokengeneration, given that an item is identified, a robotic processautomation process may automate harvesting of characteristics of theitem from an item token 24806 of the first exchange 24802.Characteristics harvesting may be based on one or more characteristicsharvesting algorithms 24818 that may be used to process an itemrepresentative token 24806 of a first exchange to determine informationthat is descriptive and/or contains characteristics of an item. In anexample, a first exchange 24802 may provide and/or make available adirectory (e.g., list and the like) of items and corresponding itemtokens 24806 in the exchange. The list may include and/or reference aset of characteristics represented by the item token 24806 for eachitem, such as identifying characteristics, physical characteristics,ownership characteristics, performance/service characteristics, pricingand sales terms characteristics, and the like. Therefore, in an exampleof representative item token generation, a token generation system24812, optionally through operation of a robotic process automationsystem 24810, may process an item-representative token 24806 withcharacteristics harvesting algorithms 24818 to retrieve itemcharacteristics of an identified item (not shown) and generate one ormore target item representative tokens 24814 for a second/targetexchange.

The characteristic harvesting algorithms 24818 may facilitate conversionof item-representative token content, optionally a digitalrepresentation of the item including characteristics of the item intoitem characteristics suitable for use by the token generation system24812 to produce at least a corresponding target exchange itemrepresentative token. A digital representation of an item 24806 maycontain and/or reference a wide range of aspects associated with an itemof an exchange. Some of the aspects may characterize the item; some maycharacterize a context of the exchange that is pertinent to the item,others may be indicative of a seller, a broker, use and otherconditions, and the like. The characteristics harvesting algorithms24818 may facilitate parsing item token 24806 content to distinguishamong the potentially many types of content, only some of which may bepertinent to target exchange item representative token generation. Inexample embodiments, digital representation of source exchange aspectsin an item token 24806 may not be pertinent to generating a targetexchange item-representative token 24814 and therefore may beeffectively filtered by application of one or more of the characteristicharvesting algorithms 24818. The characteristics harvesting algorithms24818 may reference a set of characteristics types that may beassociated with different target exchanges. The token generation system24812 may, through the algorithms, identify for retrieval and forprocessing a portion of the set of characteristics types that areassociated with the target exchange (exchange Y 24804 in the embodimentsof FIG. 247 ). In example embodiments, robotic process automationservices 24810 may facilitate automated execution of this aspect oftoken generation for configuring an instance of the token generationsystem 24812 for generating a target exchange item-representative tokenfor a target exchange 24804 from an item-representative token 24806 of asource exchange 24802.

The token generation system 24812 may process one or more harvested itemcharacteristics (e.g., characteristics of an item retrieved from an itemtoken 24806 of a first exchange 24802) for generating a target exchangeitem token 24814 along with characteristics rules 24808 of a targetexchange (e.g., exchange Y 24804 in the embodiments of FIG. 247 ).Target exchanges may maintain the set of characteristics rules 24808 tofacilitate, among other things, representing an item based on itemcharacteristics, and optionally for use and/or understanding of itemcharacteristics. The token generation system 24812 may query one or morecontrol facilities, such as an exchange control tower of a targetexchange to retrieve characteristics rules that are pertinent to tokengeneration. In an example of characteristic rule querying, an itemcharacteristic retrieved for an item for which a representative token isbeing generated may indicate a dimension of the item in imperial units.The token generation system 24812 may generate a query (e.g., a lookuptype data structure) that indicates information about thecharacteristic, such as the type of characteristic, data of the instanceof the type of characteristic, and the like. In this example the tokengeneration system may present a query data structure that includes atleast a type attribute (physical dimension), and optionally a valueattribute (3.1), and a unit attribute (e.g., imperial inches). The tokengeneration system 24812 may access a target exchange characteristicsrules data structure 24808, such as by comparing one or more of thequery data structure elements to entries in the rules data structure24808. Alternatively, the query data structure may be presented to theexchange control tower or other exchange management facility whereat asearch or other action is taken of a rules data structure to provide acorresponding characteristic rule for use in generating a representativetoken based at least in part on the specific item characteristic.Further in this example, a corresponding characteristics rule mayindicate that for compliance with physical dimension characteristics ofitems in the target exchange, units for such values are to be metric.When this rule is applied, the token generation system 24812 may convertthe item dimension of 3.1 inches to a corresponding quantity of metricdimension (e.g., 78.74 mm); thereby causing the physical dimensioncharacteristic to be represented in and/or by the generated targetexchange item token 24814 in metric units.

Another example of use of characteristics rules by a token generationsystem 24812 may include minimum quantity characteristics. An itemcharacteristic harvested from or based on a first exchange itemrepresentative token 24806 may indicate that a minimum transactionquantity of the item in a transaction is based on item count (e.g., asingle item). A minimum transaction quantity-associated characteristicrule for a target exchange may require that a minimum transactionquantity of an item in the target exchange be based on transactionamount. Therefore, taking into consideration item unit cost, whenapplying this example target exchange minimum transaction quantity rule,a minimum item count for a transaction to be represented by thegenerated target exchange item token 24814 may be greater than a singleitem.

A generated target exchange item token 24814 that is representative ofthe item may be encoded with the item characteristics. Characteristicsof an item that indicate physical aspects of the item may be encoded asdisplayable elements in the token (e.g., color, general shape, relativesize, and the like). Characteristics that are time-related (e.g.,expiration date, automatic renewal date, best-by date, offer validitytimeframe, changes in characteristics over time, and the like) may beencoded as terms and/or conditions of a corresponding smart contract.Characteristics that are value related (e.g., purchase costs, operatingcosts, residual fees, return fees, finance fees, and the like) may beencoded in the token. In example embodiments, a generated item token24814 may reference a companion set of item characteristics 24816 thatare associated with token. The item characteristics 24806, processed andadjusted as needed based on exchange rules 24808 by the token generationsystem 24812 may be output to a characteristics data structure 24816that is linked to a generated token 24814.

In example embodiments, the methods and systems of FIG. 247 forcharacteristics harvesting and target exchange token generation may beperformed by a robotic process automation service 24810 that may betrained on a set of token generation actions, such as actions taken byhumans, the token generation system 24812 and the like. Robotic processautomation services 24810 may facilitate autonomous generation ofrepresentative tokens based on item characteristics of an item of asource exchange for use in a target exchange. Robotic process automationservices 24810, when combined with the token generation system 24812capabilities and processing systems may automate conversion of aplurality of sets of item characteristics in a first exchange toitem-representative tokens in a second exchange.

In example embodiments, the methods and systems for item characteristicharvesting and target exchange token (and optional smart contract)generation may include converting a token from a first exchange to atoken for the target exchange. Such token conversion may includederiving item characteristics from the first exchange token, such as byuse of item characteristic harvesting algorithms 24818 and the like andproducing an item token for the item for a target exchange that isconsistent with governing rules of the target exchange. A non-limitinggoverning rule of a target exchange may include use of anexchange-preferred currency, which may be different than a currency ofthe exchange from which the item characteristics in a correspondingtoken are harvested. An example of achieving compliance with targetexchange governing rules may include conversion from the first exchangecurrency to the target exchange preferred currency. In another exampleof achieving compliance with currency-related target exchange governingrules, a rate of exchange, a process for currency exchange (e.g.,conversion through an intermediate currency), and the like may beperformed during token conversion.

Conversion to a target exchange token may include generating one or moresmart contracts based on a combination of source exchange tokencharacteristics (as may be harvested and/or derived as describedherein), such as for presentation of the item in the second exchange.

Conversion to a target exchange token may include execution of a smartcontract associated with at least one of the item, the source/firstexchange, and the target exchange. Such a token conversion smartcontract may be configured with terms that provide a degree of controlon a token conversion process. An example token conversion smartcontract term may be for facilitating harvesting of item characteristicsfrom a source token, such as limiting access to item characteristicsbased on legal jurisdiction of on one or more of the source and targetexchanges. In this example, information that may describecharacteristics of an item in a first exchange may require exportclearance before being presented in an exchange outside of ajurisdiction of the first exchange. A smart contract that may facilitateand/or control aspects of token item characteristic harvesting and/orconversion may dictate which information requires export clearance, andone or more ways by which such clearance is to be obtained. A smartcontract may identify how such information is to be handled, processed,stored, transferred, and the like, including without limitationpreventing access to the relevant characteristics information forservices and the like that enable export of information, such asservices that may facilitate conversion of item characteristics from afirst jurisdiction to a second restricted jurisdiction, and the like.

Token Generation from Items and Smart Contract

Referring to FIG. 248 , embodiments of methods and systems forgenerating tokens (and optional corresponding smart contracts) that arerepresentative of one or items based at least in part on characteristicsof the items and one or more corresponding smart contracts are depicted.More specifically, embodiments for methods and systems of roboticprocess automation-based token generation are depicted. In exampleembodiments, token (and optionally item smart contract) generation forrepresentation of an item in a second exchange may be based oncharacteristics of the item determined from data extracted from an item24905 and optionally from a smart contract 24806 of an item from a first(e.g., different) exchange. The embodiments depicted in FIG. 248 mayinclude a first exchange X 24902 that includes and/or makes use of item24905 and item smart contracts 24906 from which at least itemcharacteristics are determined for token generation, e.g., via tokengeneration system 24912 that may produce a target exchange item token24914 and optional target exchange item smart contract 24916 forexchange y 24904. At least one objective of such a system may be toconfigure a robotic process automation system to harvest itemcharacteristics from an item and contract terms from a smart contractfor the item that is available in a first exchange; process thosecharacteristics and terms; and produce a target exchange item token24914 (and optionally a companion target exchange item smart contract24916) that is useful in representing the item in a target exchange,such as exchange Y 24904.

In example embodiments, an item of a source exchange 24902 isidentified, such as by designation through a user interface,automatically, semi-automatically, and the like. Generally independentof a means by which an item is identified for representative tokengeneration, given that an item is identified, a robotic processautomation process may automate collection of characteristics of theitem from the item 24905 of the first exchange 24902. Further a smartcontact 24906 associated with the item 24905 may be identified andprocessed (e.g., through a smart contract parsing system 24918) toproduce one or more smart contract terms associated with the item 24905.The token generation system 24912 may process the item 24905 todetermine information that is descriptive and/or containscharacteristics to be used at least for generating a target exchangeitem-representative token 24914.

In an example, a first exchange 24902 may provide and/or make availablea directory (e.g., list and the like) of items 24905 and correspondingsmart contracts 24906 for items in the exchange. The list may includeand/or reference a set of characteristics of the item 24905, such asidentifying characteristics, physical characteristics, ownershipcharacteristics, performance/service characteristics, pricing and salesterms characteristics, and the like. Therefore, in an example ofrepresentative item token generation, a token generation system 24912,optionally through operation of a robotic process automation system24910, may process characteristics of an identified item 24905 andgenerate one or more target item representative tokens 24914 for asecond/target exchange.

The smart contract parsing system 24918 may facilitate conversion ofitem-specific contract terms into a set of smart contract terms suitablefor use by the token generation system 24912 to produce at least acorresponding target exchange item-specific smart contract 24916. Anitem smart contract 24906 may contain and/or reference a wide range ofcontract terms associated with an item of an exchange. Some of the termsmay characterize the item; some may characterize a context of theexchange that is pertinent to the item, others may be indicative ofseller terms, broker terms, use and other conditions, and the like. Thesmart contract parsing system 24918 may facilitate parsing a sourcesmart contract 24906 content to distinguish among the potentially manytypes of content and/or terms, only some of which may be pertinent totarget exchange item representative token and companion smart contractgeneration. In example embodiments, smart contract terms of a sourceexchange in an item smart contract 24906 may not be pertinent togenerating a target exchange smart contract for the item and thereforemay be effectively filtered by use of the smart contract parsing system24918. The smart contract parsing system 24918 may reference a set oftypes of contract terms that may be associated with different targetexchanges. The smart contract parsing system 24918 may identify forretrieval and for processing a portion of the set of contract term typesthat are associated with the target exchange (exchange Y 24904 in theembodiments of FIG. 248 ). In example embodiments, robotic processautomation services 24910 may facilitate automated execution of thisaspect of token and contract generation for configuring an instance ofthe token generation system 24912 for generating a target exchangeitem-representative token (and optional smart contract) for a targetexchange 24904 from an item-associated smart contract 24906 of a sourceexchange 24902.

The token generation system 24912 may process one or more itemcharacteristics (e.g., characteristics of an item retrieved from an item24905 of a first exchange 24902) along with contract terms of thecorresponding item smart contract 24906 for generating a target exchangeitem token 24914 and smart contract. This processing may further rely oncharacteristics rules 24908 of a target exchange (e.g., exchange Y 24904in the embodiments of FIG. 248 ). Target exchanges may maintain the setof characteristics rules 24908 to facilitate, among other things,representing an item based on item characteristics, and optionally foruse and/or understanding of item characteristics. The token generationsystem 24912 may query one or more control facilities, such as anexchange control tower of a target exchange to retrieve characteristicsrules that are pertinent to token generation.

An example of use of characteristics rules by a token generation system24912 may include minimum quantity characteristics. A smart contractterm harvested from or based on a first exchange item smart contract24906 may indicate that a minimum transaction quantity of the item in atransaction is based on item count (e.g., a single item). A minimumtransaction quantity-associated characteristic rule for a targetexchange may require that a minimum transaction quantity of an item inthe target exchange be based on transaction amount. Therefore, takinginto consideration item unit cost, when applying this example targetexchange minimum transaction quantity rule, a contract term of targetexchange item smart contract 24916 may indicate that a minimum itemcount for a transaction to be represented by the generated targetexchange item token 24914 be based on transaction amount, which may begreater than a single item.

The token generation system 24912 may rely on (e.g., interact with) asmart contract engine 24920 when processing at least contract terms ofthe source item smart contract 24906. The smart contract engine 24920may generate and/or validate (e.g., through simulation and the like) atterms for the target exchange smart contract 24916 that comply withsmart contract best practices and with target exchange characteristicsrules 24908. As an example, if physical units designated in a targetexchange characteristics set of rules 24908 indicate such units are tobe metric units, the smart contract engine 24920 may create terms and/orconditions associated with the target exchange generateditem-representative token 24914 that are expressed in metric terms. Theresulting smart contract 24916 may indicate that a deployment of theitem must be on a surface that stably supports the weight of the item,which would require use of metric units (e.g., metric tons) as acriteria for meeting smart contract compliance of this iteminstallation-related term. IN this way the target exchange smartcontract 24916 term directly relates to a characteristic of the item(e.g., a metric weight of the item) included in the item-representativetarget exchange token 24914.

A generated target exchange item token 24914 that is representative ofthe item may be encoded with the item characteristics. Characteristicsof an item that indicate physical aspects of the item may be encoded asdisplayable elements in the token (e.g., color, general shape, relativesize, and the like). Characteristics that are time-related (e.g.,expiration date, automatic renewal date, best-by date, offer validitytimeframe, changes in characteristics over time, and the like) may beencoded as terms and/or conditions of a corresponding smart contract.Characteristics that are value related (e.g., purchase costs, operatingcosts, residual fees, return fees, finance fees, and the like) may beencoded in the token. In example embodiments, a generated item token24914 may reference a companion smart contract 24916. The contract termsof the smart contract 24906, processed (e.g., by the smart contractparsing system 24918) and adjusted as needed based on exchange rules24908 (e.g., by the smart contract engine 24920) may be output to atarget exchange item-specific smart contract 24916 that is linked to agenerated token 24914.

In example embodiments, the methods and systems of FIG. 248 for contractterm harvesting and target exchange token (and optional smart contract)generation may be performed by a robotic process automation service24910 that may be trained on a set of token and smart contractgeneration actions, such as actions taken by humans, the tokengeneration system 24912, the smart contract engine 24920, the smartcontract parsing system 24918, and the like. Robotic process automationservices 24910 may facilitate autonomous generation of representativetokens based on item characteristics of an item of a source exchange foruse in a target exchange. Robotic process automation services 24910,when combined with the token generation system 24912 capabilities andprocessing systems may automate generation of a plurality of targetexchange item-representative tokens 24914 and target exchangeitem-specific smart contracts 24916.

Rights Token Generation

Referring to FIG. 249 , embodiments of methods and systems forgenerating rights tokens (and optional corresponding smart contracts)that are representative of a set of rights relating to an item based atleast in part on one or more of a smart contract of an item, or a set ofterms and conditions of the item are depicted.

In example embodiments, rights token generation system 24064 may receiveitem-related smart contract information (e.g., via a smart contractprocessing system 24058 and the like that may generate and/or provide aset of smart contract parameters) and/or item-related terms andconditions (e.g., via a terms and condition analysis system 24066 andthe like that may generate and/or provide a set of item-related termsand conditions) and produce, among other things, an item rights token24068 that may be based at least in part on a target exchange set ofgoverning rules 24062.

In example embodiments, rights related to an item 24052 that may beencoded into a generated item rights token 24068 may include, withoutlimitation ownership rights, transaction dispositive (e.g., last rightof refusal) rights, item use rights, item naming rights, itemcommercialization rights and the like. Ownership rights may includerights provided to an owner(s) of the item (e.g., a party who owns atleast a portion of the item). Ownership rights may include rightsprovided with the item (e.g., rights to adapt the item, rights toreproduce the item, rights to an easement such as access to a parkingspace, and the like). Item related rights that may be transactiondispositive may include rights to set a minimum sale price, rights toset restrictions on buyer financial risk, and the like. Item related userights may include, without limitation, rights to use an item for alimited duration, during a specific time frame, rights to cause normalwear and tear on the item, jurisdictional restrictions of use, and thelike. Item commercialization rights may include rights to rent, lease,or otherwise license access to, use of, and resale of the item, use ofthe item for promotional purposes, and the like. Item naming rights mayinclude rights to determine, such as for publication, promotion, andidentification purposes a name of the item (e.g., rights to make asports arena/stadium for a duration of time, such as for a calendar yearand the like.

The rights token generation system 24064 may rely on a smart contractprocessing system 24058 to parse, decode, process (e.g., execute), orotherwise identify rights granted through and/or controlled by the smartcontract for parties related to the item, such as a buyer, seller,curator, and the like. As an example, an item smart contract 24054 mayindicate that a party may sell his/her share of the item; however aprocess for selling called out by the smart contract may indicate thatthe owned portion shall first be offered to one or more of the otherowners/partial owners, who may have a first right of refusal, and thelike. The smart contract processing system 24058 may determine if anitem is controlled by a smart contract and may automatically process thesmart contract to identify rights associated therewith. The smartcontract processing system 24058 may also determine if an item has areporting relationship with the smart contract, such as if the item isrequired to report into the smart contract. In a non-limiting examplethe item may be an electronic item (and/or may be monitored by anelectronic monitoring device) that may be required to report activity toa smart contract associated with monetization by an exchange in which itis listed, and the like.

In example embodiments, an item for which a rights token may begenerated, such as item 24052, may be associated with (e.g., mayinclude) a set of terms and/or conditions 24056 that may identify,influence, or otherwise impact generation of a rights token for theitem. Although these terms and conditions 24056 may not include explicitterms and conditions for rights, they may indicate certain factors thatmay impact generating an item rights token 24068. In an example, acondition associated with the item 24052 may include limits on operatingthe item in a residential neighborhood after dark. Such a condition maybe analyzed by the terms and conditions analysis system 24066 that mayproduce a set of data regarding this condition that the right tokengeneration system 24064 may convert into a limit on a set of use rights,such as rights to use (e.g., operate) the item include only daytime use.

In example embodiments, the rights token generation system 24064 mayconsult a set of target exchange governing rules 24062 when generatingan item rights token 24068 for the target exchange. Sample targetexchange governing rules are now described; however a more detaileddescription of target exchange governing rules and their impact onrights token generation may be found in the description associated withFIG. 250 . Governing rules of the exchange may include exchange-specificrules, such as exchange transaction timing (e.g., settlement dwell timeand the like), record keeping (e.g., use of a distributed ledger), rulesassociated with a platform through which the target exchange operates,and the like. Other sample target exchange governing rules 24062 mayinclude transaction-specific rules, such as exchange of physical itemsmay provide rights to a smart contract of the target exchange to monitorconditions of sale of items transacted through the exchange. A targetexchange governing rule that may relate to item rights token generationmay include minimum use durations (e.g., a minimum calendar time mustlapse before the item can be re-transacted). Commercialization rightsmay be impacted by a set of target exchange governing rules 24062, suchas requiring that resale of the item occur through the target exchangeunless a fee (e.g., buyer resale release fee) is paid to the exchange,and the like.

In example embodiments, other example target exchange governing rules24062 may include transactor and/or transactor-type rules. As anexample, a set of item rights for an item rights token 24068 may beimpacted by a liquidity of a transactor, such as a buyer and/or sellerin a transaction for an item. A target exchange may retain a degree ofownership rights when a liquidity of a buyer is below a threshold. Insuch a case a smart contract may optionally be configured to update thedegree of exchange ownership throughout a probation period aftercompletion of a transaction for the item by the buyer.

In example embodiments, the methods and systems of FIG. 249 for rightstoken (and optional smart contract) generation may be performed by arobotic process automation service 24060 that may be trained on a set ofrights token generation actions, such as actions taken by humans, therights token generation system 24064, the smart contract processingsystem 24058, the terms and conditions analysis system 24066, and thelike. Robotic process automation services 24060 may facilitateautonomous generation of rights tokens based on rights of an item in asource exchange for use in a target exchange. Robotic process automationservices 24060, when combined with the rights token generation system24064 capabilities may automate generation of a plurality of item rightstokens 24068 based on item terms and conditions 24056, item smartcontract 24054, and target exchange governing rules 24062.

Rights Token Generation with Target Exchange Rules Details

Referring to FIG. 250 , embodiments of methods and systems forgenerating item rights tokens that are representative of a set of rightsrelating to an item based at least in part on one or more of a smartcontract of an item, a set of terms and conditions of the item, and amulti-dimensional set of target exchange governing rules are depicted.

In example embodiments, rights token generation system 24164 may receiveitem-related smart contract information (e.g., via a smart contractprocessing system 24158 and the like that may generate and/or provide aset of smart contract parameters) and/or item-related terms andconditions (e.g., via a terms and condition analysis system 24166 andthe like that may generate and/or provide a set of item-related termsand conditions) and produce, among other things, an item rights token24168 that may be based at least in part on a target exchange set ofmulti-dimensional governing rules 24162.

In example embodiments, rights related to an item 24152 that may beencoded into a generated item rights token 24168 may include, withoutlimitation ownership rights, transaction dispositive (e.g., last rightof refusal) rights, item use rights, item naming rights, itemcommercialization rights and the like. Ownership rights may includerights provided to an owner(s) of the item (e.g., a party who owns atleast a portion of the item). Ownership rights may include rightsprovided with the item (e.g., rights to adapt the item, rights toreproduce the item, rights to an easement such as access to a parkingspace, and the like). Item related rights that may be transactiondispositive may include rights to set a minimum sale price, rights toset restrictions on buyer financial risk, and the like. Item related userights may include, without limitation, rights to use an item for alimited duration, during a specific time frame, rights to cause normalwear and tear on the item, jurisdictional restrictions of use, and thelike. Item commercialization rights may include rights to rent, lease,or otherwise license access to, use of, and resale of the item, use ofthe item for promotional purposes, and the like. Item naming rights mayinclude rights to determine, such as for publication, promotion, andidentification purposes a name of the item (e.g., rights to make asports arena/stadium for a duration of time, such as for a calendar yearand the like.

The rights token generation system 24164 may rely on a smart contractprocessing system 24158 to parse, decode, process (e.g., execute), orotherwise identify rights granted through and/or controlled by the smartcontract for parties related to the item, such as a buyer, seller,curator, and the like. As an example, an item smart contract 24154 mayindicate that a party may sell his/her share of the item; however aprocess for selling called out by the smart contract may indicate thatthe owned portion shall first be offered to one or more of the otherowners/partial owners, who may have a first right of refusal, and thelike. The smart contract processing system 24158 may determine if anitem is controlled by a smart contract and may automatically process thesmart contract to identify rights associated therewith. The smartcontract processing system 24158 may also determine if an item has areporting relationship with the smart contract, such as if the item isrequired to report into the smart contract. In a non-limiting examplethe item may be an electronic item (and/or may be monitored by anelectronic monitoring device) that may be required to report activity toa smart contract associated with monetization by an exchange in which itis listed, and the like.

In example embodiments, an item for which a rights token may begenerated, such as item 24152, may be associated with (e.g., mayinclude) a set of terms and/or conditions 24156 that may identify,influence, or otherwise impact generation of a rights token for theitem. Although these terms and conditions 24156 may not include explicitterms and conditions for rights, they may indicate certain factors thatmay impact generating an item rights token 24168. In an example, acondition associated with the item 24152 may include limits on operatingthe item in a residential neighborhood after dark. Such a condition maybe analyzed by the terms and conditions analysis system 24166 that mayproduce a set of data regarding this condition that the right tokengeneration system 24164 may convert into a limit on a set of use rights,such as rights to use (e.g., operate) the item include only daytime use.

In example embodiments, the rights token generation system 24164 mayconsult a multi-dimensional set of target exchange governing rules 24162when generating an item rights token 24168 for use in a target exchange.Target exchange governing rules 24162 may include a plurality ofdimensions and/or type of governing rules including, without limitation,jurisdiction rules 24170, item industry rights standards 24172, rightstoken templates 24174 and the like.

Jurisdiction governing rules 24170 may impact right token generationsystem 24164, such as by setting constraints on one or more rightsassociated with the item. As an example, an item may be permitted to beowned and transacted by a non-citizen in a first jurisdiction; however,the item may not be permitted to be owned by a non-citizen in ajurisdiction of a target exchange in which the item rights token 24168is to be used. For a non-citizen to acquire the item in a jurisdictionof the target exchange, ownership rights may have to be shared with alegal entity formed in the target exchange jurisdiction, and the like.Jurisdiction rules 24170 that may be consulted by the rights tokengeneration system 24164 may include age limits for items that may bedifferent than those found in a set of item terms and conditions 24156,for example. The rights token generation system 24164 may receive itemterms and conditions 24156 that may indicate only users above age 18 canoperate the item. However, when this age-related condition is evaluatedagainst a target jurisdiction age rule for the item, the resulting itemrights token 24168 may limit operation to users above age 20 or maypermit operation by users as young as 16 years.

Another type of target exchange governing rule 24162 may include itemindustry rights standards 24172 that may include a set of standardsdeveloped for an industry of the item for guiding selection and/orgeneration of item rights. In an example of a livestock industry, a setof industry rights standards 24172 may include guidelines for rights ofthird-parties to perform inspection, use rights for different classes oflivestock (e.g., goats versus Clydesdales), and the like. The industryrights standards 24172 may be relied up by the rights token generationsystem 24164 to facilitate adapting rights detected through smartcontract processing 24158 and/or through terms and condition analysis24166. If a right determined by the smart contract processing system24158 presents a conflict with an industry rights standard 24172, therights token generation system 24164 may adapt the incomingsmart-contract determined right to be compliant with the industrystandard rights 24172.

Another type or dimension of target exchange governing rules 24162 mayinclude rights token templates 24174. A target exchange may publish aset of rights token templates for ensuring compliance with anyapplicable governing rules, such as jurisdiction rules 24170, industryrights standards 24172, and the like. A set of rights templates 24174may include exchange-based rights templates that may include a minimumset of rights for items transacted in the exchange. A set of rightstemplates 24174 may include templates and/or configure one or moretemplates to include industry group rights standards, regulatory-basedrights, and the like. In example embodiments, exchanges may establishrights token templates to facilitate differentiation from otherexchanges, such as to provide a way for item owners to identify benefitsof engaging with one exchange or another, for example.

In example embodiments, the methods and systems of FIG. 250 for rightstoken (and optional smart contract) generation may be performed by arobotic process automation service 24160 that may be trained on a set ofrights token generation actions, such as actions taken by humans, therights token generation system 24164, the smart contract processingsystem 24158, the terms and conditions analysis system 24166, and thelike. Robotic process automation services 24160 may facilitateautonomous generation of rights tokens based on rights of an item in asource exchange for use in a target exchange. Robotic process automationservices 24160, when combined with the rights token generation system24164 capabilities may automate generation of a plurality of item rightstokens 24168 based on item terms and conditions 24156, item smartcontract 24154, and target exchange governing rules 24162.

Rights Token Generation with Rights Conformance Evaluation

Referring to FIG. 251 , embodiments of methods and systems forgenerating item rights tokens that are representative of a set of rightsrelating to an item based at least in part on one or more of a smartcontract of an item, a set of terms and conditions of the item, and thatare compliant with one or more target exchange governing rules aredepicted.

In example embodiments, rights conformance evaluation system 24270 mayreceive item-related smart contract information (e.g., via a smartcontract processing system 24258 and the like that may generate and/orprovide a set of smart contract parameters) and/or item-related termsand conditions (e.g., via a terms and condition analysis system 24266and the like that may generate and/or provide a set of item-relatedterms and conditions) and produce, among other things, a set ofconforming rights 24274 and optionally a set of non-conforming rights24272 based at least in part on a target exchange set of governing rules24262. In example embodiments, the rights conformance evaluation system24270 may determine, for one or more rights received if it conflictswith a corresponding target exchange governing rule 24262. The rightsconformance evaluation system 24270 may determine that a right receivedis a non-confirming right 24272, a conforming right 24274, or thatconformance is not dispositive through the evaluation. In exampleembodiments, rights that determined to be other than non-conforming maybe deemed to be conforming.

The rights conformance evaluation system 24270 may rely on a smartcontract processing system 24258 to parse, decode, process (e.g.,execute), or otherwise identify rights granted through and/or controlledby the smart contract for parties related to the item, such as a buyer,seller, curator, and the like. As an example, an item smart contract24254 may indicate that a party may sell his/her share of the item;however a process for selling called out by the smart contract mayindicate that the owned portion shall first be offered to one or more ofthe other owners/partial owners, who may have a first right of refusal,and the like. The smart contract processing system 24258 may determineif an item is controlled by a smart contract and may automaticallyprocess the smart contract to identify rights associated therewith. Thesmart contract processing system 24258 may also determine if an item hasa reporting relationship with the smart contract, such as if the item isrequired to report into the smart contract. In a non-limiting examplethe item may be an electronic item (and/or may be monitored by anelectronic monitoring device) that may be required to report activity toa smart contract associated with monetization by an exchange in which itis listed, and the like. Item smart contract-derived rights mayestablish ownership rights that may conflict with target exchangegoverning rules 24262. An example ownership right conflict may include asmart contract assigning rights to revenues resulting from the use ofthe item to a third party, whereas the target exchange governing rules24262 require such rights to revenues be determined by the acquiringowner of the item.

In example embodiments, an item for which a rights token may begenerated, such as item 24252, may be associated with (e.g., mayinclude) a set of terms and/or conditions 24256 that may identify,influence, or otherwise impact generation of a rights token for theitem. Although these terms and conditions 24256 may not include explicitterms and conditions for rights, they may indicate certain factors thatmay impact generating an item rights token 24268. In an example, acondition associated with the item 24252 may include limits on operatingthe item in a residential neighborhood after dark. Such a condition maybe analyzed by the terms and conditions analysis system 24266 that mayproduce a set of data regarding this condition that the rightsconformance evaluation system 24270 may evaluate against a correspondingtarget exchange governing rule 24262. If this set of data is indicativeof a conforming right 24274, the right token generation system 24264 mayconvert it into a limit on a set of use rights, such as rights to use(e.g., operate) the item include only daytime use.

The rights conformance evaluation system 24270 may rely on an item termsand condition processing system 24266 to parse, decode, analyze, orotherwise identify rights associated with the item for parties relatedto the item, such as a buyer, seller, curator, and the like. An exampleof conflicting rights determined from analysis of item terms andconditions 24256 may include a right to own the item without formalregistration of the item with a regulatory party (e.g., a firearm) thatmay conflict with a set of target exchange governing rules 24262 thatrequire registration. The rights conformance evaluation system 24270 mayflag this item right as a non-conforming right 24272.

In example embodiments, rights related to an item 24252 that are deemedto be conforming rights 24274 may be encoded into a generated itemrights token 24268 and may include, without limitation ownership rights,transaction dispositive (e.g., last right of refusal) rights, item userights, item naming rights, item commercialization rights and the like.Ownership rights may include rights provided to an owner(s) of the item(e.g., a party who owns at least a portion of the item). Ownershiprights may include rights provided with the item (e.g., rights to adaptthe item, rights to reproduce the item, rights to an easement such asaccess to a parking space, and the like). Item related rights that maybe transaction dispositive may include rights to set a minimum saleprice, rights to set restrictions on buyer financial risk, and the like.Item related use rights may include, without limitation, rights to usean item for a limited duration, during a specific time frame, rights tocause normal wear and tear on the item, jurisdictional restrictions ofuse, and the like. Item commercialization rights may include rights torent, lease, or otherwise license access to, use of, and resale of theitem, use of the item for promotional purposes, and the like. Itemnaming rights may include rights to determine, such as for publication,promotion, and identification purposes a name of the item (e.g., rightsto make a sports arena/stadium for a duration of time, such as for acalendar year and the like.

In example embodiments, the methods and systems of FIG. 251 for rightsconformance evaluation may be performed by a robotic process automationservice 24260 that may be trained on a set of rights conformanceevaluation actions, such as actions taken by humans, the rightsconformance evaluation system 24270, the smart contract processingsystem 24258, the terms and conditions analysis system 24266, and thelike. Robotic process automation services 24260 may facilitateautonomous evaluation of rights conformance and generation of rightstokens based on rights of an item in a source exchange for use in atarget exchange. Robotic process automation services 24260, whencombined with the rights conformance evaluation system 24270 and rightstoken generation system 24264 capabilities may automate rightsconformance evaluation and rights token generation based on item termsand conditions 24256, item smart contract 24254, and target exchangegoverning rules 24262.

Rights Token Generation and Adaptable Rights Tokens

Referring to FIG. 252 , embodiments of methods and systems forgenerating adaptable rights tokens that are representative of a set ofrights relating to an item based at least in part on one or more of asmart contract of an item, a set of terms and conditions of the item,target exchange governance rules, and adaptation factors are depicted.

In example embodiments, adaptable rights token generation system 24364may receive item-related smart contract information (e.g., via a smartcontract processing system 24358 and the like that may generate and/orprovide a set of smart contract parameters) and/or item-related termsand conditions (e.g., via a terms and condition analysis system 24366and the like that may generate and/or provide a set of item-relatedterms and conditions) and produce, among other things, an adaptable itemrights token 24368 that may be based at least in part on a targetexchange set of governing rules 24362.

In example embodiments, rights related to an item 24352 that may beencoded into a generated adaptable item rights token 24368 may include,without limitation ownership rights, transaction dispositive (e.g., lastright of refusal) rights, item use rights, item naming rights, itemcommercialization rights and the like. Ownership rights may includerights provided to an owner(s) of the item (e.g., a party who owns atleast a portion of the item). Ownership rights may include rightsprovided with the item (e.g., rights to adapt the item, rights toreproduce the item, rights to an easement such as access to a parkingspace, and the like). Item related rights that may be transactiondispositive may include rights to set a minimum sale price, rights toset restrictions on buyer financial risk, and the like. Item related userights may include, without limitation, rights to use an item for alimited duration, during a specific time frame, rights to cause normalwear and tear on the item, jurisdictional restrictions of use, and thelike. Item commercialization rights may include rights to rent, lease,or otherwise license access to, use of, and resale of the item, use ofthe item for promotional purposes, and the like. Item naming rights mayinclude rights to determine, such as for publication, promotion, andidentification purposes a name of the item (e.g., rights to make asports arena/stadium for a duration of time, such as for a calendar yearand the like.

The adaptable rights token generation system 24364 may rely on a smartcontract processing system 24358 to parse, decode, process (e.g.,execute), or otherwise identify rights granted through and/or controlledby the smart contract for parties related to the item, such as a buyer,seller, curator, and the like. As an example, an item smart contract24354 may indicate that a party may sell his/her share of the item;however a process for selling called out by the smart contract mayindicate that the owned portion shall first be offered to one or more ofthe other owners/partial owners, who may have a first right of refusal,and the like. The smart contract processing system 24358 may determineif an item is controlled by a smart contract and may automaticallyprocess the smart contract to identify rights associated therewith. Thesmart contract processing system 24358 may also determine if an item hasa reporting relationship with the smart contract, such as if the item isrequired to report into the smart contract. In a non-limiting examplethe item may be an electronic item (and/or may be monitored by anelectronic monitoring device) that may be required to report activity toa smart contract associated with monetization by an exchange in which itis listed, and the like.

In example embodiments, an item for which an adaptable rights token maybe generated, such as item 24352, may be associated with (e.g., mayinclude) a set of terms and/or conditions 24356 that may identify,influence, or otherwise impact generation of an adaptable rights tokenfor the item. Although these terms and conditions 24356 may not includeexplicit terms and conditions for rights, they may indicate certainfactors that may impact generating an adaptable item rights token 24368.In an example, a condition associated with the item 24352 may includelimits on operating the item in a residential neighborhood after dark.Such a condition may be analyzed by the terms and conditions analysissystem 24366 that may produce a set of data regarding this conditionthat the adaptable right token generation system 24364 may convert intoa limit on a set of use rights, such as rights to use (e.g., operate)the item include only daytime use.

In example embodiments, the adaptable rights token generation system24364 may consult a set of target exchange governing rules 24362 whengenerating an adaptable item rights token 24368 for the target exchange.Sample target exchange governing rules may include exchange-specificrules, such as exchange transaction timing (e.g., settlement dwell timeand the like), record keeping (e.g., use of a distributed ledger), rulesassociated with a platform through which the target exchange operates,and the like. Other sample target exchange governing rules 24062 mayinclude transaction-specific rules, such as exchange of physical itemsmay provide rights to a smart contract of the target exchange to monitorconditions of sale of items transacted through the exchange. A targetexchange governing rule that may relate to adaptable item rights tokengeneration may include minimum use durations (e.g., a minimum calendartime must lapse before the item can be re-transacted). Commercializationrights may be impacted by a set of target exchange governing rules24362, such as requiring that resale of the item occur through thetarget exchange unless a fee (e.g., buyer resale release fee) is paid tothe exchange, and the like.

In example embodiments, other example target exchange governing rules24362 may include transactor and/or transactor-type rules. As anexample, a set of item rights for an adaptable item rights token 24368may be impacted by a liquidity of a transactor, such as a buyer and/orseller in a transaction for an item. A target exchange may retain adegree of ownership rights when a liquidity of a buyer is below athreshold. In such a case a smart contract may optionally be configuredto update the degree of exchange ownership throughout a probation periodafter completion of a transaction for the item by the buyer.

In example embodiments, adaptation of an adaptable item rights token24368 may be based on a set of target exchange adaptation rules 24372,data associated with a transactor participant accessing the adaptablerights token 24368 and the like. A rights token adaptation system 24370may, responsive to a request by an exchange participant (e.g., atransactor) 24376, adapt one or more aspects of the adaptable rightstoken 24368 to produce at least one transactor-specific item rightstoken 24374. The set of target exchange adaptation rules 24372 mayinclude rules that define adaptation limits and criteria for certainrights, such as ownership rights, resale rights, use rights, and thelike. An example target exchange adaptation rule 24372 may includeconstraints on how a right to use an item (e.g., a facility) may beadapted based on aspects of the transactor/buyer of the item. A right touse provided in an adaptable rights token for the item may be adaptedbased on an entity type of the transactor. Use rights for a non-profittype entity transactor may be adapted based on regulations for use of afacility by a non-profit entity. The rights token adaptation system24370 may capture a request 24378 for the adaptable rights token 24368corresponding to the facility from a non-profit exchange transactor24376. Based on rules for facility use by a non-profit entity that maybe provided by the target exchange adaptation rules data set 24372, therights token adaptation system 24370 may generate a non-profittransactor-specific rights token 24374.

An adaptable item rights token generation and use system as depicted inFIG. 252 may facilitate per-use rights adaptation to suit a range oftarget exchange-specific rights constraints, participant-specificrights, and the like. In example embodiments, per-use rights adaptationof an adaptable item rights token 24368 may include generating aplurality of differentiated rights tokens for a plurality ofdifferentiated transactors from a common set of adaptable item rights asmay be captured by an adaptable item rights token 24368. Such per-useadaptation may also facilitate modeling of transactor-specific rightsfor items in different target exchanges. Application (e.g., in anoff-line/sandbox/emulation mode) of a candidate set of exchangeadaptation rules 24372 for a candidate target exchange totransactor-specific request data by the rights token adaptation system24370 may facilitate predicting a set of data descriptive of acorresponding transactor-specific rights token 24374. Through use of arobotic process automation system 24360, a plurality of suchtransactor-specific rights token data sets may be generated andoptionally presented to a transactor for evaluating which of a pluralityof target exchanges provide the transactor with rights that align with,for example, a set of business goals of the transactor.

In example embodiments, the methods and systems of FIG. 252 foradaptable rights token generation may be performed by a robotic processautomation service 24360 that may be trained on a set of adaptablerights token generation actions, such as actions taken by humans, theadaptable rights token generation system 24364, the smart contractprocessing system 24358, the terms and conditions analysis system 24366,a rights token adaptation system 24370, and the like. Robotic processautomation services 24360 may facilitate autonomous generation ofadaptable rights tokens based on rights of an item in a source exchangefor use in a target exchange. Robotic process automation services 24360,when combined with the adaptable rights token generation system 24364capabilities may automate generation of a plurality of adaptable itemrights tokens 24368 based on item terms and conditions 24356, item smartcontract 24354, and target exchange governing rules 24362, and the like.

Automated Orchestration of Exchanges with Cross-Exchange ActionResponsiveness

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces.Automated orchestration may include cross-exchange workflow initiationassociated with value normalization, value translation, item tokengeneration, rights token generation and the like. In an example, suchmethods and systems may have a set of robotic process automationservices that are configured to orchestrate a set of transactionworkflows in each of a plurality of exchanges, such that initiation of aset of actions in one exchange automatically results in the triggeringof a set of actions in at least one other exchange. In exampleembodiments, orchestration of the set of transaction workflows in eachof the plurality of exchanges may initiate a set of actions in the setof transaction workflows that causes and/or contributes to initiation ofone of the set of workflows in one exchange automatically results in thetriggering of a set of corresponding/coordinating/item-centric actionsthat result in activating at least one of a corresponding set ofworkflows in the other exchange.

Referring to FIG. 253 , a set of robotic process automation services24460 may be applied to sets of workflows for a plurality of exchanges,such as exchange X 24452, exchange Y 24454, and exchange Z 24456. Theset of robotic process automation services 24460 may facilitateautomating one or more workflows 24458 of the exchanges.

Actions of a first exchange, such as actions 24462 of exchange X 24452may include a first action XA1 24464. The first action 24464 may beselected from a set of actions 24466 including, without limitationnormalization of item values within the first exchange, translation ofvalue of an item from/to the first exchange to/from a second exchange,generation of an item token, generation of a rights token, and otheractions. Initiation of the first action 24464 may trigger, cause, orcontribute to initiation of at least one action in the second exchange.In example embodiments, initiation of the first action 24464 in exchangeX 24452 may trigger activation of a set of actions 24468 in exchange Y24454. The set of actions 24468 in exchange Y 24454 may include anaction of a workflow of exchange Y, such as workflow Y WF 1 and/orworkflow Y WF 2. The set of actions 24468 in exchange Y 24454 mayinclude an action selected from a list of actions including valuenormalization, value translation, item token generation, rights tokengeneration, and other actions.

Further, initiation of action XA1 24464 (a first action) in exchange X24452 (a first exchange) may trigger initiation of action ZAn 24470 (athird action) in exchange Z 24456 (a third exchange). In this example,the first action (an action to normalize a value of an item) in thefirst exchange, may trigger activation of a value translation action(third action) in the third exchange.

In embodiments, methods and systems are provided having a set of roboticprocess automation services that are configured to state the value of aset of items that are represented in a plurality of exchanges, such thatrepresentation of the value of each member of the set of items in theplurality of exchanges is normalized based on the native currencies ofthe respective exchanges and having a set of robotic process automationservices that are configured to orchestrate a set of transactionworkflows in each of a plurality of exchanges, such that initiation of aset of actions in the set of transaction workflows thatcauses/contributes to initiation of one of the set of workflows in oneexchange automatically results in the triggering of a set ofcorresponding/coordinating/item-centric actions that result inactivating at least one of a corresponding set of workflows in at leastone other exchange.

In embodiments, methods and systems are provided having a set of roboticprocess automation services that are configured to automaticallytranslate the value of an item represented in a first exchange into avalue of the item for representation in a second exchange and having aset of robotic process automation services that are configured toorchestrate a set of transaction workflows in each of a plurality ofexchanges, such that initiation of a set of actions in the set oftransaction workflows that causes/contributes to initiation of one ofthe set of workflows in one exchange automatically results in thetriggering of a set of corresponding/coordinating/item-centric actionsthat result in activating at least one of a corresponding set ofworkflows in at least one other exchange.

In embodiments, methods and systems are provided having a set of roboticprocess automation services that are configured to generate token thatrepresents an item in an exchange based on characteristics of the itemdetermined from data from a different exchange and having a set ofrobotic process automation services that are configured to orchestrate aset of transaction workflows in each of a plurality of exchanges, suchthat initiation of a set of actions in the set of transaction workflowsthat causes/contributes to initiation of one of the set of workflows inone exchange automatically results in the triggering of a set ofcorresponding/coordinating/item-centric actions that result inactivating at least one of a corresponding set of workflows in at leastone other exchange.

In embodiments, methods and systems are provided having a set of roboticprocess automation services that are configured to generate a digitalrepresentation of a set of rights relating to an item that is consistentwith the governing rules of an exchange based on processing at least oneof a set of smart contracts and a set of terms and conditions relatingto the item and having a set of robotic process automation services thatare configured to orchestrate a set of transaction workflows in each ofa plurality of exchanges, such that initiation of a set of actions inthe set of transaction workflows that causes/contributes to initiationof one of the set of workflows in one exchange automatically results inthe triggering of a set of corresponding/coordinating/item-centricactions that result in activating at least one of a corresponding set ofworkflows in at least one other exchange.

In example embodiments, the methods and systems of FIG. 253 forautomatic triggering of actions across exchanges may be performed by arobotic process automation service 24460 that may be trained on a set ofcross exchange workflow triggering actions, such as actions taken byhumans, a cross-exchange action triggering facility, and the like.Robotic process automation services 24460 may facilitate autonomousconfiguration of links among transaction workflows, workflow actions,and exchange actions that enable actions in a first exchangeautomatically triggering actions in a second exchange. Robotic processautomation services 24460 may facilitate automatically triggering one ormore actions in one or more workflows for one or more exchanges for aset of transaction workflows across a plurality of exchanges.

Exchange Actions in a First Exchange May Initiate Workflows and/orInitiate Exchange Actions in a Second Exchange

Referring to FIG. 254 , a set of robotic process automation services maybe configured to orchestrate a set of transaction workflows in each of aplurality of exchanges, such that initiation of a set of actions in afirst exchange that causes initiation of a first transaction workflow ofthe set of transaction workflows of the first exchange may automaticallyresult in the triggering of initiation of a set of corresponding actionsin at least one other exchange, wherein the set of corresponding actionsin the at least one other exchange cause initiation of a secondtransaction workflow of the set of transaction workflows of the at leastone other exchange.

In example embodiments, a robotic process automation service 24560 maybe configured to orchestrate a set of transaction workflows 24558 ineach of a plurality of exchanges, such as exchange X 24552, exchange Y24554, and exchange Z 24556. The set of transaction workflows 24558 mayinclude one or more workflows of exchange X 24552, such as X WF 1 and XWF 2 as depicted in FIG. 254 . The set of transaction workflows 24558may include workflows in exchange Y 24554 (workflows Y WF 1 and Y WF 2),and may include workflows in exchange Z 24556 (workflows Y WF 1 and Y WF2).

Exchange X 24552 may also include a set of actions 24562, such as actionXA1 24564 that may trigger workflow X WF 1 in the set of transactionworkflows 24558. The set of actions 24562 may also include action XA11that may automatically trigger a corresponding action YA1 in a set ofactions of exchange Y 24554. Action YA1 may initiate, within exchange Ya workflow 24568 in the set of transaction workflows 24558. FurtherExchange X 24552 may include a third action XA12 in the set of actions24562 that may automatically trigger an action ZAn 24570 in exchange X24556, wherein action ZAn 24570 may be outside of the set of transactionworkflows 24558.

The set of transaction workflows 24558 may include a plurality ofworkflows for achieving cross-exchange item handling as describedherein, that may be selected form a set of workflows 24566 includingwithout limitation one or more of an item value normalization workflow,an item value translation workflow, an item token generation workflow, arights token generation workflow, and the like.

In example embodiments, the methods and systems of FIG. 254 forautomatic triggering of actions across exchanges may be performed by arobotic process automation service 24560 that may be trained on a set ofcross exchange workflow triggering actions, such as actions taken byhumans, a cross-exchange action triggering facility, and the like.Robotic process automation services 24560 may facilitate autonomousconfiguration of links among transaction workflows, workflow actions,and exchange actions that enable actions in a first exchangeautomatically triggering actions in a second exchange. Robotic processautomation services 24560 may facilitate automatically triggering one ormore actions in one or more workflows for one or more exchanges for aset of transaction workflows across a plurality of exchanges.

Workflow Actions in a First Exchange May Initiate Workflows and/orWorkflow Actions in a Second Exchange

Referring to FIG. 255 , use of robotic process automation 24660 foroperation of a plurality of workflows 24658 across a plurality ofexchanges with triggered cross-exchange workflow initiation is depicted.A set of robotic process automation services may be configured toorchestrate a set of transaction workflows in each of a plurality ofexchanges, such that initiation of a set of actions in the set oftransaction workflows in a first exchange automatically results in thetriggering of a set of one or more corresponding actions in a workflowof the set of workflows in at least one other exchange. A set of roboticprocess automation services may be configured to orchestrate a set oftransaction workflows in each of a plurality of exchanges, such thatinitiation of an action in a workflow of the set of transactionworkflows of a first exchange automatically results in triggering acorresponding action in a workflow of the set of transaction workflowsof a second exchange. In example embodiments, triggering thecorresponding action in a workflow of the set of transaction workflowsof the second exchange automatically results in triggering acorresponding action in a workflow of the set of workflows of a thirdexchange. In example embodiments, the action of the first exchange is aselected from a set of actions comprising a value normalizing action, avalue translation action, an item token generation action, and a rightstoken generation action. In example embodiments, the automaticallytriggered action of the second exchange is a selected from a set ofactions comprising a value normalizing action, a value translationaction, an item token generation action, and a rights token generationaction. In example embodiments, the automatically triggered action ofthe third exchange is a selected from a set of actions comprising avalue normalizing action, a value translation action, an item tokengeneration action, and a rights token generation action.

In example embodiments, initiation of a set of actions 24664 from a setof transaction workflows 24662 of a first exchange 24652 mayautomatically result in triggering one or more workflows 24668 of a setof workflows in a second exchange 24654. The set of workflows in thesecond exchange may include workflows with actions that correspond withthe set of actions 24664. The set of workflows in the second exchangemay include workflows with actions that coordinate/compliment the set ofactions 24664. Further the set of workflows in the second exchange mayinclude workflows with item-centric actions for an item associated withthe set of transaction workflows 24662.

Further, initiation of the set of actions 24664 from the set oftransaction workflows 24662 of the first exchange 24652 mayautomatically result in triggering one or more workflow actions 24670 ofa third exchange 24656. Workflow robotic process automation mayfacilitate automated execution of workflows and/or workflow actionsacross a plurality of exchanges, including cascading actions across theplurality of exchanges. In the example of FIG. 255 , a first workflow XWF 2 24666 of a first exchange X 24652 may include a workflow action,such as a value normalization action as described herein. Activation ofthe value normalization action in the first exchange 24652 may trigger acorresponding workflow action, such as a value normalization action in asecond workflow Y WF 2 of a second exchange Y 24654. This triggeredcorresponding value normalization action may further trigger activationof an item token generation action in a third workflow Z WF 2 of a thirdexchange Z 24656. In example embodiments, each of the cascaded actionsmay be performed for a common item. In this example, a valuenormalization action of a first item in exchange X may trigger acorresponding value normalization action for the first item in exchangeY, which may trigger a corresponding item token generation action forthe first item in exchange Z. In example embodiments, the triggeredcorresponding value normalization action of the Exchange Y may be basedat least in part on a result of the value normalization action ofexchange X. Likewise, the item token generation action of exchange Z maybe based on a result of value normalization for the item of exchange Y.In example embodiments, while workflows and/or workflow actions, and/orexchange actions may automatically be triggered across the plurality ofexchanges, each such action and/or workflow may be independent, otherthan due to triggering, of any other action in any other exchange,including cascading workflows and actions.

In example embodiments, the methods and systems of FIG. 255 forautomatic triggering of actions across exchanges may be performed by arobotic process automation service 24660 that may be trained on a set ofcross exchange workflow triggering actions, such as actions taken byhumans, a cross-exchange action triggering facility, and the like.Robotic process automation services 24660 may facilitate autonomousconfiguration of links among transaction workflows, workflow actions,and exchange actions that enable actions in a first exchangeautomatically triggering actions in a second exchange. Robotic processautomation services 24660 may facilitate automatically triggering one ormore actions in one or more workflows for one or more exchanges for aset of transaction workflows across a plurality of exchanges.

Configuring a Smart Contract from Analysis of a Set of Smart Contractsin Each of a Plurality of Exchanges

In embodiments, methods and systems may include a set of robotic processautomation services that are configured to state the value of a set ofitems that are represented in a plurality of exchanges, such thatrepresentation of the value of each member of the set of items in theplurality of exchanges is normalized based on the native currencies ofthe respective exchanges and may include a set of robotic processautomation services that are configured to inspect a set of smartcontracts in each of a plurality of exchanges and to configure a smartcontract that provides terms and conditions for a transaction thatinvolves a transactional step in each of the plurality of exchanges.

In embodiments, methods and systems may include a set of robotic processautomation services that are configured to automatically translate thevalue of an item represented in a first exchange into a value of theitem for representation in a second exchange and may include a set ofrobotic process automation services that are configured to inspect a setof smart contracts in each of a plurality of exchanges and to configurea smart contract that provides terms and conditions for a transactionthat involves a transactional step in each of the plurality ofexchanges.

In embodiments, methods and systems may include a set of robotic processautomation services that are configured to generate token thatrepresents an item in an exchange based on characteristics of the itemdetermined from data from a different exchange and may include a set ofrobotic process automation services that are configured to inspect a setof smart contracts in each of a plurality of exchanges and to configurea smart contract that provides terms and conditions for a transactionthat involves a transactional step in each of the plurality ofexchanges.

In embodiments, methods and systems may include a set of robotic processautomation services that are configured to generate a digitalrepresentation of a set of rights relating to an item that is consistentwith the governing rules of an exchange based on processing at least oneof a set of smart contracts and a set of terms and conditions relatingto the item and may include a set of robotic process automation servicesthat are configured to inspect a set of smart contracts in each of aplurality of exchanges and to configure a smart contract that providesterms and conditions for a transaction that involves a transactionalstep in each of the plurality of exchanges.

Referring to FIG. 256 exemplary embodiments of the methods and systemsfor configuring a smart contract, such as a cross-exchange smartcontract, that provides terms and conditions for a transactionassociated with an item are depicted. The example embodiments are forapplying a generated smart contract to adapt a normalized item valueacross transaction workflows in a plurality of exchanges. A roboticprocess automation service 24760 may be configured to operate and/orinteract with a set of smart contract analysis services 24764 that mayinterface with a set of smart contract generation services 24766 toproduce a cross-exchange smart contract 24770 that may be useful in oneor more steps of one or more of a plurality of transaction workflows ofone or more of a plurality of exchanges. A plurality of exchanges may berepresented by an exemplary first exchange A 24752 that may beassociated with one or more sets of smart contracts 24758 and that mayoptionally include a set of governing rules 24762. The exchange mayprocess a transaction workflow 24776 that may include one or more stepsfor adapting a normalized item value 24774 of an item 24772. As depictedeach of two additional exchanges in the plurality of exchanges,specifically exchanges B 24754 and exchange C 24756 may include one ormore sets of smart contracts 24758, optionally one or more sets ofgoverning rules 24762, an exchange-specific transaction workflow thatmay be similar to transaction workflow 24776 that may rely upon a crossexchange smart contract 24770 to adapt a normalized item value 24774 foran item 24772. In example embodiments, a set of smart contracts for eachdistinct exchange may include one or more smart contracts that may besimilar to and/or distinct from other smart contracts in the set. One ormore smart contracts in a set of smart contracts for a first exchangemay be distinct from at least one other smart contract of at least oneother exchange. In example embodiments, differences among smartcontracts within and/or across exchanges may include differences interms (e.g., effectivity start/stop dates), differences in conditions(jurisdictional differences, for example), differences driven by aspectsof a corresponding exchange, and the like.

Governing rules 24762 for each of the plurality of exchanges may beconfigured to address exchange-specific governing factors, such astaxation, import/export regulations, exchange transaction fee structure,and many others. In example embodiments, governing rules may be optionalfor one or more of the plurality of exchanges.

In example embodiments, the smart contract analysis system 24764 maycapture information that is descriptive of each of a plurality of smartcontracts for at least a portion of the plurality of exchanges. Inexample embodiments, the smart contract analysis system 24764 mayinclude a smart contract execution and/or simulation capability throughwhich a smart contract may be simulated and monitored to captureinformation that is descriptive thereof, such as terms, conditions,input data sources, algorithms applied to such sources, thresholds, andthe like. Through analysis of smart contracts for the portion of theplurality of exchanges, a set of terms and conditions may be determinedthat may be suitable for application to a set of transaction workflows.In an example, a resulting set of terms and conditions may be derivedfrom a subset of terms and conditions that are common among the analyzedsmart contracts. In this example, a common condition for each analyzedcontract may include a condition that contract terms that are satisfiedwhen the exchange is closed (e.g., after local business hours) arerecorded as being satisfied when the exchange next opens (e.g., nextbusiness day and the like). In an example, a resulting set of terms andconditions may be a superset of terms and conditions from the analyzedsmart contracts. In this example, a smart contract for a first exchangemay include a condition that exchange data is captured hourly, and asmart contract for a second exchange may include a condition thatexchange data is captured every 30 seconds. A resulting smart contractmay include conditions that require exchange data to be captured every30 seconds and every hour.

In example embodiments, a deliverable from the smart contact analysissystem 24764 may include a set of terms and conditions, processingalgorithms, data sources, and the like that may be adapted based ongoverning rules from one or more of the plurality of exchanges in asmart contract generation system 24766 that produces a smart contractthat includes one or more terms and conditions from at least one of theplurality of sets of smart contracts. The smart contract generationsystem 24766 may further apply governing rules from each of theexchanges that apply to terms and/or conditions produced from the smartcontract analysis system 24764. In example embodiments, a term for smartcontact of a first exchange that survives the smart contract analysissystem 24764 (e.g., that is included in a deliverable set of termsproduced by the smart contract analysis system 24764) may be adaptedbased on a governing rule of the first exchange. In example embodiments,governing rules of a first exchange may be used to adapt at least one ofterm and conditions in the set of deliverables derived from analysis ofa smart contract from a second exchange. As an example of application ofexchange-specific governing rules to a resulting set of terms, anormalization action in a transaction workflow may be adapted tocalculate the normalized value to a degree of precision defined by thegoverning rules. In example embodiments, if a governing rule in a firstexchange defines normalized value precision to be two decimal places,and a governing rules in a second exchange defines normalized valueprecision to be three decimal places, a resulting cross-exchange smartcontract may require normalization to three decimal places for eachapplication of the cross-exchange smart contract. In exampleembodiments, a smart contract produced by the smart contract generationsystem 24766 may include a cross-exchange smart contract 24770.

Further in the exemplary embodiment depicted in FIG. 256 , thecross-exchange smart contract 24770 may be configured to impact atransaction workflow step, such as step Y in each of a plurality oftransaction workflows 24776 for a plurality of exchanges. As an exampleof application of a cross-exchange smart contract derived from aplurality of smart contracts, step Y in each of the plurality oftransaction workflows 24776 may include accessing a normalized value foran item in the exchange, applying a smart contract-specified adjustment,and setting a transaction price in the exchange for the item. In theexample embodiment of FIG. 256 , the cross exchange smart contract 24770may provide an adjustment value, an adjustment approach (e.g., analgorithm), and/or other conditions under which the accessed normalizeditem value is adjusted. Further the cross-exchange smart contract 24770may be configured to prescribe an adjustment that applies to a specificexchange, to a plurality of exchanges, or to all exchanges. Yet furtherthe cross-exchange smart contract 24770 may provide conditions underwhich the normalized value is adjusted that are different for one ormore of the plurality of exchanges. In an example, a smart contract forexchange A may include a term that requires no adjustment in normalizedvalue; a smart contract for exchange B may include a term that requiresa conditional adjustment (e.g., based on transaction value and thelike); a smart contract for exchange C may include a term that requiresan adjustment to normalized value that results in rounding off thenormalized value to a whole unit of local currency. Each of these termsmay be configured into the cross exchange smart contract 24770 so thatwhen it is applied to a transaction workflow, a term that corresponds toan exchange in which the transaction workflow occurs can be applied toadjust the normalized value. Through application of a cross exchangesmart contract 24770, normalized item value may be automaticallygenerated for an item across a plurality of exchanges. Further use ofrobotic process automation for generating cross exchange smart contractsmay facilitate orchestration of transaction workflows that can beautomatically and dynamically adapted.

In the exemplary normalized item value embodiments depicted in FIG. 256, use of the methods and systems for generating and applying a crossexchange smart contact 24770 may facilitate generating anexchange-specific normalized value for an item in each of a plurality ofexchanges that factors in one or more terms and conditions associatedwith a smart contact for each of the exchanges.

In example embodiments, the methods and systems of FIG. 256 forcross-exchange smart contract generation may be performed by a roboticprocess automation service 24760 that may be trained on a set of smartcontract generation actions, such as actions taken by humans, the smartcontract analysis system 24764, the smart contract generation system24766, and the like. Robotic process automation services 24760 mayfacilitate autonomous generation of a smart contract based on terms andconditions of a plurality of smart contracts across a plurality ofexchanges. Robotic process automation services 24760, when combined withthe smart contract analysis system capabilities and the smart contractgeneration system capabilities may automate generation of a crossexchange smart contract 24770 based on a plurality of exchange governingrules 24762 across the plurality of exchanges.

Self-Adapting Asset Data Delivery Network Infrastructure Pipeline

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems are provided having a set of robotic processautomation services that are configured to state the value of a set ofitems that are represented in a plurality of exchanges, such thatrepresentation of the value of each member of the set of items in theplurality of exchanges is normalized based on the native currencies ofthe respective exchanges and having a data and network infrastructurepipeline that is configured to deliver data from a set of assets to aninterface by which an operator orchestrates a set of parameters for aset of transaction workflows involving the assets, wherein the pipelineis automatically configured to adjust a network path based on thecharacteristics of the data and at least one performance parameter ofthe network path.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems are provided having a set of robotic processautomation services that are configured to automatically translate thevalue of an item represented in a first exchange into a value of theitem for representation in a second exchange and having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to an interface by which an operator orchestrates a setof parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems are provided having a set of robotic processautomation services that are configured to generate token thatrepresents an item in an exchange based on characteristics of the itemdetermined from data from a different exchange and having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to an interface by which an operator orchestrates a setof parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems are provided having a set of robotic processautomation services that are configured to generate a digitalrepresentation of a set of rights relating to an item that is consistentwith the governing rules of an exchange based on processing at least oneof a set of smart contracts and a set of terms and conditions relatingto the item and having a data and network infrastructure pipeline thatis configured to deliver data from a set of assets to an interface bywhich an operator orchestrates a set of parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust a network path based on thecharacteristics of the data and at least one performance parameter ofthe network path.

Referring to FIG. 257 , a data and network infrastructure pipeline 24800is configured to deliver data from a set of assets 24852 to one or moremarketplace entities for one or more marketplaces 24868 in whichtransactions for portions of the sets of assets 24852 are conducted. Inexample embodiments, the data from the set of assets 24852 is deliveredby the pipeline 24800 to an interface by which an operator orchestratesa set of parameters for a set of transaction workflows involving theassets. The pipeline 24800 may be automatically configured to adjust anetwork path for delivery of data from the set of assets 24852 to theinterface based on the characteristics of the data and at least oneperformance parameter of the network path. In example embodiments, thepipeline 24800 may be automatically configured to adjust timing of assetdata delivery from the set of assets 24852 to the interface based on atleast one of a transaction parameter and a network performanceparameter.

Referring again to FIG. 257 , the data and network infrastructurepipeline 24800 may include sets of asset-centric intelligent networkresources 24854 that may be disposed proximal to the set of assets24852. These sets of asset-centric intelligent network resources 24854may include a set of asset local resources that are configured to workcooperatively manage, for example use of network storage to preservedata delivered from the sets of assets 24852 for supporting delivery ofthe asset data through the pipeline 24800. These asset local resources24854 may be configured to interface with intelligent assets, such aselectronic assets. These asset local resources 24854 may automaticallydetermine, such as through analysis of data from such electronic assetconfiguration parameters for interfacing with one or more correspondingelectronic assets.

The data and network infrastructure pipeline 24800 may further include aset of intermediate intelligent network resources 24856 that may beadapted to deliver asset data from the asset local resources 24854 on toone or more marketplace related interfaces 24868, such as a userinterface, a smart contract, and the like. The set of intermediateintelligent network resources 24856 may include a network pathadaptation/determination system that facilitates adapting a network pathby producing an automatically adapted network route for the asset data.Such a network path adaptation/determination system may perform networkpath determination based on characteristics of the asset data.

The data and network infrastructure pipeline 24800 may also include aset of marketplace-centric intelligent network resources 24866 that maybe disposed proximal to recipients of the asset data, such as interfacesassociated with a marketplace 24868 in which one or more transactions(and associated transaction workflows) for the assets 24852 may beconducted. Examples of marketplace-centric intelligent network resources24866 may include an item value normalization system 24858, across-exchange item value translation system 24860, an item tokengeneration system 24862, an item rights token generation system 24864,and the like.

In example embodiments, the item value normalization system 24858 mayinclude a set of robotic process automation services that are configuredto state the value of a set of items that are represented in a pluralityof exchanges, such that representation of the value of each member ofthe set of items in the plurality of exchanges is normalized based onthe native currencies of the respective exchanges, example embodimentsof which are described herein, including without limitation embodimentsdepicted in FIGS. 241, 242, and 243 . In example embodiments, thecross-exchange item value translation system 24860 may include a set ofrobotic process automation services that are configured to automaticallytranslate the value of an item represented in a first exchange into avalue of the item for representation in a second exchange, exampleembodiments of which are described herein, including without limitationembodiments depicted in FIGS. 244 and 245 . In example embodiments, theitem token generation system 24862 may include a set of robotic processautomation services that are configured to generate a token thatrepresents an item in an exchange based on characteristics of the itemdetermined from data from a different exchange, example embodiments ofwhich are described herein, including without limitation FIGS. 246, 247,and 248 . In example embodiments, the item rights token generationsystem 24864 may include a set of robotic process automation servicesthat are configures for generating rights tokens (and optionalcorresponding smart contracts) that are representative of a set ofrights relating to an item based at least in part on one or more of asmart contract of an item, or a set of terms and conditions of the item,example embodiments of which are described herein, including withoutlimitation FIGS. 249, 250, 251, and 252 .

The one or more sets of intelligent network resources, such as sets ofasset-centric resources 24852, intermediate resources 24856, or sets ofmarketplace-centric resources 24866 may be implemented in or inassociation with physical resources of a data communication network,such as the Internet and the like. Sets of asset-centric resources 24854and/or sets of marketplace (e.g., asset data recipient) centricresources 24866 may include network infrastructure resources, such asedge computing devices, inter-network interface devices (e.g., bridges,routers, and the like), aggregation devices, such as a distributedantenna system, and the like.

In embodiments, methods and systems are provided that may include a setof robotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a digital twin platform, such that interactionswith a set of interfaces of the digital twin platform automaticallytrigger a set of transaction workflows within the marketplace.

In embodiments, methods and systems are provided that may include a setof robotic process automation services that are configured toautomatically translate the value of an item represented in a firstexchange into a value of the item for representation in a secondexchange and having a digital twin that represents a set of entities,workflows, and transaction parameters of a plurality of exchanges, suchthat interaction with the interface of the digital twin can orchestratean interaction in each of the plurality of exchanges.

In embodiments, methods and systems are provided that may include a setof robotic process automation services that are configured to generate atoken that represents an item in an exchange based on characteristics ofthe item determined from data from a different exchange and having adigital twin that represents a set of entities, workflows, andtransaction parameters of a plurality of exchanges, such thatinteraction with the interface of the digital twin can orchestrate aninteraction in each of the plurality of exchanges.

In embodiments, methods and systems are provided that may include a setof robotic process automation services that are configured to generate adigital representation of a set of rights relating to an item that isconsistent with the governing rules of an exchange based on processingat least one of a set of smart contracts and a set of terms andconditions relating to the item and having a digital twin thatrepresents a set of entities, workflows, and transaction parameters of aplurality of exchanges, such that interaction with the interface of thedigital twin can orchestrate an interaction in each of the plurality ofexchanges.

In embodiments, methods and systems are provided that may include a setof robotic process automation services that are configured to generate adigital representation of a set of rights relating to an item that isconsistent with the governing rules of an exchange based on processingat least one of a set of smart contracts and a set of terms andconditions relating to the item and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a digital twin platform, such that interactions with aset of interfaces of the digital twin platform automatically trigger aset of transaction workflows within the marketplace.

Normalization, Translation, Item Tokens, Rights Tokens, and CombinationsThereof

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems having a set of robotic process automation servicesthat are configured to state the value of a set of items that arerepresented in a plurality of exchanges, such that representation of thevalue of each member of the set of items in the plurality of exchangesis normalized based on the native currencies of the respectiveexchanges.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofrobotic process automation services that are configured to automaticallytranslate the value of an item represented in a first exchange into avalue of the item for representation in a second exchange. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofrobotic process automation services that are configured to generatetoken that represents an item in an exchange based on characteristics ofthe item determined from data from a different exchange. In embodiments,such methods and systems are provided having a set of robotic processautomation services that are configured to state the value of a set ofitems that are represented in a plurality of exchanges, such thatrepresentation of the value of each member of the set of items in theplurality of exchanges is normalized based on the native currencies ofthe respective exchanges and having a set of robotic process automationservices that are configured to generate a digital representation of aset of rights relating to an item that is consistent with the governingrules of an exchange based on processing at least one of a set of smartcontracts and a set of terms and conditions relating to the item.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofrobotic process automation services that are configured to orchestrate aset of transaction workflows in each of a plurality of exchanges, suchthat initiation of a set of actions in one exchange automaticallyresults in the triggering of a set of actions in at least one otherexchange.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofrobotic process automation services that are configured to orchestrate aset of transaction workflows in each of a plurality of exchanges, suchthat initiation of a set of actions in the set of transaction workflowsin one exchange automatically results in the triggering of a set ofcorresponding/coordinating/item-centric in at least one other exchange.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofrobotic process automation services that are configured to orchestrate aset of transaction workflows in each of a plurality of exchanges, suchthat initiation of a set of actions in the set of transaction workflowsthat causes/contributes to initiation of one of the set of workflows inone exchange automatically results in the triggering of a set of actionsthat result in activating at least one of a corresponding set ofworkflows in at least one other exchange.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofrobotic process automation services that are configured to orchestrate aset of transaction workflows in each of a plurality of exchanges, suchthat initiation of a set of actions in the set of transaction workflowsthat causes/contributes to initiation of one of the set of workflows inone exchange automatically results in the triggering of a set ofcorresponding/coordinating/item-centric actions that result inactivating at least one of a corresponding set of workflows in at leastone other exchange. In embodiments, such methods and systems areprovided having a set of robotic process automation services that areconfigured to state the value of a set of items that are represented ina plurality of exchanges, such that representation of the value of eachmember of the set of items in the plurality of exchanges is normalizedbased on the native currencies of the respective exchanges and having adigital twin that represents a set of entities, workflows, andtransaction parameters of a plurality of exchanges, such thatinteraction with an interface of the digital twin can orchestrate aninteraction in each of the plurality of exchanges.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to set of smart contracts that include terms, conditionsand parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to an interface by which an operator orchestrates a setof parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust timing ofdata delivery based on at least one of a transaction parameter and anetwork performance parameter. In embodiments, such methods and systemsare provided having a set of robotic process automation services thatare configured to state the value of a set of items that are representedin a plurality of exchanges, such that representation of the value ofeach member of the set of items in the plurality of exchanges isnormalized based on the native currencies of the respective exchangesand having a data and network infrastructure pipeline that is configuredto deliver data from a set of assets to set of smart contracts thatinclude terms, conditions and parameters for a set of transactionworkflows involving the assets, wherein the pipeline is automaticallyconfigured to adjust timing of data delivery based on at least one of atransaction parameter and a network performance parameter.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into an electronic wallet system, such thatinteractions with a set of interfaces of the wallet system automaticallytrigger a set of transaction workflows within the marketplace.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a digital twin platform, such that interactionswith a set of interfaces of the digital twin platform automaticallytrigger a set of transaction workflows within the marketplace.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into an enterprise database platform, such thatinteractions with a set of interfaces of the enterprise databaseplatform automatically trigger a set of transaction workflows within themarketplace.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a platform-as-a-service platform, such thatinteractions with a set of interfaces of the platform-as-a-serviceplatform automatically trigger a set of transaction workflows within themarketplace.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a computer-aided design platform, such thatinteractions with a set of interfaces of the computer-aided designplatform automatically trigger a set of transaction workflows within themarketplace.

In embodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to state thevalue of a set of items that are represented in a plurality ofexchanges, such that representation of the value of each member of theset of items in the plurality of exchanges is normalized based on thenative currencies of the respective exchanges and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a video game, such that interactions with a set ofinterfaces of the video game automatically trigger a set of transactionworkflows within the marketplace.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems having a set of robotic process automation servicesthat are configured to automatically translate the value of an itemrepresented in a first exchange into a value of the item forrepresentation in a second exchange. In embodiments, such methods andsystems are provided having a set of robotic process automation servicesthat are configured to automatically translate the value of an itemrepresented in a first exchange into a value of the item forrepresentation in a second exchange and having a set of robotic processautomation services that are configured to generate token thatrepresents an item in an exchange based on characteristics of the itemdetermined from data from a different exchange. In embodiments, suchmethods and systems are provided having a set of robotic processautomation services that are configured to automatically translate thevalue of an item represented in a first exchange into a value of theitem for representation in a second exchange and having a set of roboticprocess automation services that are configured to generate a digitalrepresentation of a set of rights relating to an item that is consistentwith the governing rules of an exchange based on processing at least oneof a set of smart contracts and a set of terms and conditions relatingto the item. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto automatically translate the value of an item represented in a firstexchange into a value of the item for representation in a secondexchange and having a set of robotic process automation services thatare configured to orchestrate a set of transaction workflows in each ofa plurality of exchanges, such that initiation of a set of actions inone exchange automatically results in the triggering of a set of actionsin at least one other exchange. In embodiments, such methods and systemsare provided having a set of robotic process automation services thatare configured to automatically translate the value of an itemrepresented in a first exchange into a value of the item forrepresentation in a second exchange and having a digital twin thatrepresents a set of entities, workflows, and transaction parameters of aplurality of exchanges, such that interaction with the interface of thedigital twin can orchestrate an interaction in each of the plurality ofexchanges. In embodiments, such methods and systems are provided havinga set of robotic process automation services that are configured toautomatically translate the value of an item represented in a firstexchange into a value of the item for representation in a secondexchange and having a set of robotic process automation services thatare configured to inspect a set of smart contracts in each of aplurality of exchanges and to configure a smart contract that providesterms and conditions for a transaction that involves a transactionalstep in each of the plurality of exchanges. In embodiments, such methodsand systems are provided having a set of robotic process automationservices that are configured to automatically translate the value of anitem represented in a first exchange into a value of the item forrepresentation in a second exchange and having a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to set of smart contracts that include terms, conditions andparameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path. In embodiments, such methodsand systems are provided having a set of robotic process automationservices that are configured to automatically translate the value of anitem represented in a first exchange into a value of the item forrepresentation in a second exchange and having a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to an interface by which an operator orchestrates a set ofparameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust timing ofdata delivery based on at least one of a transaction parameter and anetwork performance parameter. In embodiments, such methods and systemsare provided having a set of robotic process automation services thatare configured to automatically translate the value of an itemrepresented in a first exchange into a value of the item forrepresentation in a second exchange and having a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to set of smart contracts that include terms, conditions andparameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust timing ofdata delivery based on at least one of a transaction parameter and anetwork performance parameter. In embodiments, such methods and systemsare provided having a set of robotic process automation services thatare configured to automatically translate the value of an itemrepresented in a first exchange into a value of the item forrepresentation in a second exchange and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into an electronic wallet system, such that interactions witha set of interfaces of the wallet system automatically trigger a set oftransaction workflows within the marketplace. In embodiments, suchmethods and systems are provided having a set of robotic processautomation services that are configured to automatically translate thevalue of an item represented in a first exchange into a value of theitem for representation in a second exchange and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a digital twin platform, such that interactionswith a set of interfaces of the digital twin platform automaticallytrigger a set of transaction workflows within the marketplace. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to automaticallytranslate the value of an item represented in a first exchange into avalue of the item for representation in a second exchange and having aset of application programming interfaces to a marketplace that areconfigured to be integrated into an enterprise database platform, suchthat interactions with a set of interfaces of the enterprise databaseplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto automatically translate the value of an item represented in a firstexchange into a value of the item for representation in a secondexchange and having a set of application programming interfaces to amarketplace that are configured to be integrated into aplatform-as-a-service platform, such that interactions with a set ofinterfaces of the platform-as-a-service platform automatically trigger aset of transaction workflows within the marketplace. In embodiments,such methods and systems are provided having a set of robotic processautomation services that are configured to automatically translate thevalue of an item represented in a first exchange into a value of theitem for representation in a second exchange and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a computer-aided design platform, such thatinteractions with a set of interfaces of the computer-aided designplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto automatically translate the value of an item represented in a firstexchange into a value of the item for representation in a secondexchange and having a set of application programming interfaces to amarketplace that are configured to be integrated into a video game, suchthat interactions with a set of interfaces of the video gameautomatically trigger a set of transaction workflows within themarketplace.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems having a set of robotic process automation servicesthat are configured to generate token that represents an item in anexchange based on characteristics of the item determined from data froma different exchange. In embodiments, such methods and systems areprovided having a set of robotic process automation services that areconfigured to generate token that represents an item in an exchangebased on characteristics of the item determined from data from adifferent exchange and having a set of robotic process automationservices that are configured to generate a digital representation of aset of rights relating to an item that is consistent with the governingrules of an exchange based on processing at least one of a set of smartcontracts and a set of terms and conditions relating to the item. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to generatetoken that represents an item in an exchange based on characteristics ofthe item determined from data from a different exchange and having a setof robotic process automation services that are configured toorchestrate a set of transaction workflows in each of a plurality ofexchanges, such that initiation of a set of actions in one exchangeautomatically results in the triggering of a set of actions in at leastone other exchange. In embodiments, such methods and systems areprovided having a set of robotic process automation services that areconfigured to generate token that represents an item in an exchangebased on characteristics of the item determined from data from adifferent exchange and having a digital twin that represents a set ofentities, workflows, and transaction parameters of a plurality ofexchanges, such that interaction with the interface of the digital twincan orchestrate an interaction in each of the plurality of exchanges. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to generatetoken that represents an item in an exchange based on characteristics ofthe item determined from data from a different exchange and having a setof robotic process automation services that are configured to inspect aset of smart contracts in each of a plurality of exchanges and toconfigure a smart contract that provides terms and conditions for atransaction that involves a transactional step in each of the pluralityof exchanges. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto generate token that represents an item in an exchange based oncharacteristics of the item determined from data from a differentexchange and having a data and network infrastructure pipeline that isconfigured to deliver data from a set of assets to set of smartcontracts that include terms, conditions and parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust a network path based on thecharacteristics of the data and at least one performance parameter ofthe network path. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto generate token that represents an item in an exchange based oncharacteristics of the item determined from data from a differentexchange and having a data and network infrastructure pipeline that isconfigured to deliver data from a set of assets to an interface by whichan operator orchestrates a set of parameters for a set of transactionworkflows involving the assets, wherein the pipeline is automaticallyconfigured to adjust timing of data delivery based on at least one of atransaction parameter and a network performance parameter. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to generatetoken that represents an item in an exchange based on characteristics ofthe item determined from data from a different exchange and having adata and network infrastructure pipeline that is configured to deliverdata from a set of assets to set of smart contracts that include terms,conditions and parameters for a set of transaction workflows involvingthe assets, wherein the pipeline is automatically configured to adjusttiming of data delivery based on at least one of a transaction parameterand a network performance parameter. In embodiments, such methods andsystems are provided having a set of robotic process automation servicesthat are configured to generate token that represents an item in anexchange based on characteristics of the item determined from data froma different exchange and having a set of application programminginterfaces to a marketplace that are configured to be integrated into anelectronic wallet system, such that interactions with a set ofinterfaces of the wallet system automatically trigger a set oftransaction workflows within the marketplace. In embodiments, suchmethods and systems are provided having a set of robotic processautomation services that are configured to generate token thatrepresents an item in an exchange based on characteristics of the itemdetermined from data from a different exchange and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a digital twin platform, such that interactionswith a set of interfaces of the digital twin platform automaticallytrigger a set of transaction workflows within the marketplace. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to generatetoken that represents an item in an exchange based on characteristics ofthe item determined from data from a different exchange and having a setof application programming interfaces to a marketplace that areconfigured to be integrated into an enterprise database platform, suchthat interactions with a set of interfaces of the enterprise databaseplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto generate token that represents an item in an exchange based oncharacteristics of the item determined from data from a differentexchange and having a set of application programming interfaces to amarketplace that are configured to be integrated into aplatform-as-a-service platform, such that interactions with a set ofinterfaces of the platform-as-a-service platform automatically trigger aset of transaction workflows within the marketplace. In embodiments,such methods and systems are provided having a set of robotic processautomation services that are configured to generate token thatrepresents an item in an exchange based on characteristics of the itemdetermined from data from a different exchange and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a computer-aided design platform, such thatinteractions with a set of interfaces of the computer-aided designplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto generate token that represents an item in an exchange based oncharacteristics of the item determined from data from a differentexchange and having a set of application programming interfaces to amarketplace that are configured to be integrated into a video game, suchthat interactions with a set of interfaces of the video gameautomatically trigger a set of transaction workflows within themarketplace.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems having a set of robotic process automation servicesthat are configured to generate a digital representation of a set ofrights relating to an item that is consistent with the governing rulesof an exchange based on processing at least one of a set of smartcontracts and a set of terms and conditions relating to the item. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to generate adigital representation of a set of rights relating to an item that isconsistent with the governing rules of an exchange based on processingat least one of a set of smart contracts and a set of terms andconditions relating to the item and having a set of robotic processautomation services that are configured to orchestrate a set oftransaction workflows in each of a plurality of exchanges, such thatinitiation of a set of actions in one exchange automatically results inthe triggering of a set of actions in at least one other exchange. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to generate adigital representation of a set of rights relating to an item that isconsistent with the governing rules of an exchange based on processingat least one of a set of smart contracts and a set of terms andconditions relating to the item and having a digital twin thatrepresents a set of entities, workflows, and transaction parameters of aplurality of exchanges, such that interaction with the interface of thedigital twin can orchestrate an interaction in each of the plurality ofexchanges. In embodiments, such methods and systems are provided havinga set of robotic process automation services that are configured togenerate a digital representation of a set of rights relating to an itemthat is consistent with the governing rules of an exchange based onprocessing at least one of a set of smart contracts and a set of termsand conditions relating to the item and having a set of robotic processautomation services that are configured to inspect a set of smartcontracts in each of a plurality of exchanges and to configure a smartcontract that provides terms and conditions for a transaction thatinvolves a transactional step in each of the plurality of exchanges. Inembodiments, such methods and systems are provided having a set ofrobotic process automation services that are configured to generate adigital representation of a set of rights relating to an item that isconsistent with the governing rules of an exchange based on processingat least one of a set of smart contracts and a set of terms andconditions relating to the item and having a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to set of smart contracts that include terms, conditions andparameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path. In embodiments, such methodsand systems are provided having a set of robotic process automationservices that are configured to generate a digital representation of aset of rights relating to an item that is consistent with the governingrules of an exchange based on processing at least one of a set of smartcontracts and a set of terms and conditions relating to the item andhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust timing of data delivery based on at least one of a transactionparameter and a network performance parameter. In embodiments, suchmethods and systems are provided having a set of robotic processautomation services that are configured to generate a digitalrepresentation of a set of rights relating to an item that is consistentwith the governing rules of an exchange based on processing at least oneof a set of smart contracts and a set of terms and conditions relatingto the item and having a data and network infrastructure pipeline thatis configured to deliver data from a set of assets to set of smartcontracts that include terms, conditions and parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust timing of data delivery based on atleast one of a transaction parameter and a network performanceparameter. In embodiments, such methods and systems are provided havinga set of robotic process automation services that are configured togenerate a digital representation of a set of rights relating to an itemthat is consistent with the governing rules of an exchange based onprocessing at least one of a set of smart contracts and a set of termsand conditions relating to the item and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into an electronic wallet system, such that interactions witha set of interfaces of the wallet system automatically trigger a set oftransaction workflows within the marketplace. In embodiments, suchmethods and systems are provided having a set of robotic processautomation services that are configured to generate a digitalrepresentation of a set of rights relating to an item that is consistentwith the governing rules of an exchange based on processing at least oneof a set of smart contracts and a set of terms and conditions relatingto the item and having a set of application programming interfaces to amarketplace that are configured to be integrated into a digital twinplatform, such that interactions with a set of interfaces of the digitaltwin platform automatically trigger a set of transaction workflowswithin the marketplace. In embodiments, such methods and systems areprovided having a set of robotic process automation services that areconfigured to generate a digital representation of a set of rightsrelating to an item that is consistent with the governing rules of anexchange based on processing at least one of a set of smart contractsand a set of terms and conditions relating to the item and having a setof application programming interfaces to a marketplace that areconfigured to be integrated into an enterprise database platform, suchthat interactions with a set of interfaces of the enterprise databaseplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto generate a digital representation of a set of rights relating to anitem that is consistent with the governing rules of an exchange based onprocessing at least one of a set of smart contracts and a set of termsand conditions relating to the item and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a platform-as-a-service platform, such that interactionswith a set of interfaces of the platform-as-a-service platformautomatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto generate a digital representation of a set of rights relating to anitem that is consistent with the governing rules of an exchange based onprocessing at least one of a set of smart contracts and a set of termsand conditions relating to the item and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a computer-aided design platform, such that interactionswith a set of interfaces of the computer-aided design platformautomatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a set of robotic process automation services that are configuredto generate a digital representation of a set of rights relating to anitem that is consistent with the governing rules of an exchange based onprocessing at least one of a set of smart contracts and a set of termsand conditions relating to the item and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a video game, such that interactions with a set ofinterfaces of the video game automatically trigger a set of transactionworkflows within the marketplace.

Governance

Data curation allows companies to provide personalized, high qualityservices to their customers. AI allows for data curation on a granderscale, providing companies with more expansive and detailed informationabout the usage of their product. However, AI has not proven to be aflawless data collector and curator—rather, the development of AI in thedata collection and curation sphere has brought about new challengesthat require governance and policy. As new laws and regulations begin toemerge surrounding the governance of AI, companies must adapt their datacuration and collection to provide a system of rules that guarantees aquality of service across the network of their organization. More thanever, trust and accountability must be at the forefront of anydeveloping AI technology. High profile incidents involving breaches ofpersonal data have ebbed away at public trust, and AI introduces a newvariable that could further destabilize the general perception of datacollection and curation. Therefore, it is necessary to establish coreguidelines and practices surrounding the governance of AI training datasets in their connection to neural networks. Memory Enhanced ArtificialNeural Networks establish basic operating measures for AI that allowtraining, model verification, and algorithm validation of data sets.These practices address the biases in AI technology that stem fromerroneous assumptions in the training data used in a machine learningmodel. These systemic prejudices could skew datasets and provide anincomplete summary based on a portion of the population that either doesnot accurately represent the whole or does not incorporate appropriatenuance into its analysis. In a world as diverse and interconnected asour own, these biases are hindrances that prevent companies fromensuring their customers receive the most secure and optimized userexperience. MEANN and/or DPANN enable the governance of human-AIinteractions to better address these biases through transparency andfeedback, creating auditing systems that ensure that the AI's trainingdata sets meet standardized rules and regulations. These networks fostertransparency by providing consumer access to the training data. Inembodiments, governance of a neural network may include identification,calculation, and utilization of various measures of trust of a neuralnetwork, such as ones that factor in one or more of visibility of inputdata, visibility of feedback factors, outcome tracking, training dataset quality, model verification data set quality, algorithm validation(which in embodiments may occur within a derivative marketplace forvalidation), various indices (such as pricing, ranking, rating, andothers), accuracy measures (including comparisons to other AI-basedsolutions and other systems), consistency, reliability, and various testmeasures (such as ones of performance, reliability, energy consumption,and others).

In embodiments, the platform 100 may include a governance system 23360configured to create a governance parameter based on a governance goal,the governance parameter being a rule to be followed by the AI entityand embed the governance parameter into the AI deployment system. The AIdeployment system is configured to apply the governance parametergoverning at least one parameter of the deployment of the AI entity,such that the AI entity is trained and deployed to perform the operationin compliance with the governance parameter. The governance system 23360may be at least partially enabled by an AI module. The AI module may beconfigured to perform modification of the governance parameter. The AImodule may be configured to perform modification of the governance goal.

In embodiments, the AI module may be configured to determine whether,after training of the AI entity, performance of the operation by the AIentity meets the governance goal. The AI module may be configured to,upon determining that the performance of the operation by the AI entitydoes not meet the governance goal, modify the governance parameter. TheAI module may be or include one or more of a machine learned process, anintelligent agent, and a neural network. The AI module may be or includea dual-process artificial neural network.

In embodiments, the platform 100 may include a governance interfaceconfigured to facilitate interaction with the governance system 23360 bya user. The governance interface may allow the user to input theoperation that the user desires the AI entity to be trained to perform.The governance system 23360 may create the governance parameter based onthe governance goal and the operation. The governance interface mayallow the user to input the governance goal that the user desires the AIentity to be trained to perform. The governance system 23360 may createthe governance parameter based on the governance goal. The governanceinterface may allow the user to view the training dataset. Thegovernance interface may allow the user to apply the governanceparameter to the training dataset. The governance interface may allowthe user to view at least one of input data, feedback factors, outcometracking, training dataset quality, model verification data set quality,algorithm validation, indices, accuracy measures, consistency,reliability, or test measures during or after training of the AI entity.The governance interface may allow the user to set at least onegovernance parameter governing the at least one of input data, feedbackfactors, outcome tracking, training dataset quality, model verificationdata set quality, algorithm validation, indices, accuracy measures,consistency, reliability, or test measures.

In embodiments, the governance system 23360 may be configured todetermine input data, feedback factors, outcome tracking, trainingdataset quality, model verification data set quality, algorithmvalidation, indices, accuracy measures, consistency, reliability, and/ortest measures during or after training of the AI entity. The governancesystem 23360 may be configured to determine a measure of trust of the AIentity.

In embodiments, the AI module may be configured to determine whether theAI entity meets a trust threshold. The AI module may be configured to,upon determining that the AI entity does not meet the trust threshold,modify the governance parameter.

In embodiments, the governance goal may include governing an AI-humaninteraction framework. The AI entity may be one or more of a machinelearned process, an intelligent agent, and a neural network. Theoperation may include analysis of sensitive data. The systemic bias mayinclude an erroneous assumption, the erroneous assumption causing a skewin performance of the AI entity. The governance parameter may relate toat least one of a training dataset, an input data set, a configurationparameter, a function, an output, a feedback parameter, or an objectiveof the AI deployment system. The operation may include a classificationoperation. The operation may include a prediction operation. Theoperation may include a recommendation operation. The operation mayinclude an optimization operation.

In embodiments, the operation may include a control operation. Thecontrol operation may include data curation.

In embodiments, the governance goal may include reducing systemic biasof the AI entity. The governance goal may include reducing systemic biasin the training data set of the AI entity. The governance system 23360may recommend an augmentation of the training data set of the AI entitythat reduces the systemic bias.

In embodiments, governance of a neural network may includeidentification, calculation, and utilization of various measures oftrust of a neural network, such as ones that factor in one or more ofvisibility of input data, visibility of feedback factors, outcometracking, training data set quality, model verification data setquality, algorithm validation (which in embodiments may occur within aderivative marketplace for validation), various indices (such aspricing, ranking, rating, and others), accuracy measures (includingcomparisons to other AI-based solutions and other systems), consistency,reliability, and various test measures (such as ones of performance,reliability, energy consumption, and others). As one example, an energypolicy may govern the amount, timing, and/or source of energy that ispermitted to be used for an activity, such as an operational activity,computational activity, or the like, such as one that requires carbonneutrality of an overall operation, one that requires use of a fractionof renewable energy, one that requires renewable energy credits, or manyothers. The policy may track the amount and type of energy used for AIworkloads (including workloads used to train a model and/or to run amodel in operation. In embodiments, a training data set may includetracking data that indicates the energy used for model creation and theenergy required for model deployment, including based on context, suchas energy required under varying conditions, such as differentprocessing environments, different network environments, and the like.Thus, an energy-governed AI model and/or energy-governed AI trainingdata set may be provided in connection with support of marketplaceoperations and/or automation, and an energy-compliance measure of trustmay be provided for model rating or comparison.

Traditional asset classes and new asset classes like cryptocurrency maybe represented in combination in a wallet with improvedvisibility/transparency, increased control and transferability.Reduction of costs associated with efficiency will broaden the role offinancial services into new types of markets.

Ethereum tokens enable an ethereum based system, as it is programmableand can form a smart contract. NFTs may have intrinsic value, therebyremoving the economics of the token and attaching a value of the tokenassociated with a specific piece of property. Different kinds of NFTsinclude a disposable asset, such as an experience (e.g., a movie ticket)or physical asset, a unique asset such as a piece of art, such asfractional ownership of a piece of art, virtual real estate (e.g.,inside video games and other spaces), NFTs representing types of rightsor fractional rights for ownership of goods, such as fractionalownership of a car or boat, NFTs representing verification of ownershipof a physical item, specific seats or a class of seats, NFTsrepresenting approved use, for example with drugs making sure people donot over consume, or with graphics cards the maker prefers to sell togamers (e.g., for branding reasons), and/or NFTs representingtransformation of social media, such as information about the rankwithin the community.

Intelligent Data Layers

The present disclosure relates to a platform for intelligent data layers(IDLs) for facilitating and reorienting transaction flows, such as flowsof software orchestrated transactions, by providing timely, contextualand transaction-impacting data for buyers, sellers, and automatedplatform functions, such as software orchestration. In embodiments, IDLsmay actively harvest, curate, and ready transaction-derived data tofacilitate cross market interactions thereby enhancing provisioning ofcomplementary services within or as a direct derivative of transactions.

Referring to FIG. 258 , a block diagram of exemplary features,capabilities, and interfaces of an exemplary embodiment of anintelligent data layer platform 25900 is depicted. Intelligent datalayers (referred to herein and elsewhere as an IDL when singular andIDLs when plural) may be configured as a portion (or portions) of an IDLplatform. The exemplary embodiment in FIG. 258 depicts an IDL 25904characterized with at least one each of an ingestion, parse, analyze,and control tower elements interconnected for providingintelligence-based and other derivatives of data sources, such as IDLdata sources 25902. Exemplary embodiments of 25904 are depicted anddescribed elsewhere herein. Associated with the exemplary IDL platform25900 of FIG. 258 , IDL data sources 25902 may represent one or moresources of information, such as business data, sensor data, outputs ofother IDLs, virtual data, and the like to which IDL processes may beapplied. In an exemplary transaction platform deployment of IDL methodsand systems, which is described elsewhere herein in greater detail, datasources 25902 may represent transaction outcomes, buyer and/or selleroperating environments, market data, and the like.

In embodiments, an IDL, such as 25904 may be configured with oroperationally connected to a set of application programming interfaces(APIs) through which, among other things, IDL source data may beretrieved and/o received. In exemplary embodiments, an API for an IDLmay be an open/standardized API (e.g., banking/financial institutionopen APIs) that, among other things, may equip the IDL platform 25900for integration with and into current and emerging eco systems. Use ofopen/standardized APIs, while not essential for all IDL embodiments, mayfurther enable IDLs to integrate into a wide range of data workflows,such as corporate internal workflows, inter-jurisdiction data workflows,and the like.

An IDL platform such as 25900 may include, reference, and/or providemarket orchestration elements 25908 that may facilitate use of IDLcapabilities for various aspects of market orchestration, including,without limitations, software orchestrated transactions, softwareorchestrated marketplaces, and the like. Market orchestration elements25908 may facilitate deployment of an IDL, such as a web serviceembodiment, as an integrated function of a market orchestrationplatform, such as an automated market orchestration system of systems asdescribed herein. In embodiments, an IDL may provide data and networkpipeline capabilities for market orchestration when configured as aportion of an IDL platform 25900 in association with marketorchestration elements 25908 and the like.

IDL platform 25900 may include, reference and/or provide cross marketinteraction capabilities 25910 that may enable leveraging intelligencedata layer principals, computation capabilities, storage and datasourcing capabilities, as well as intelligence capabilities for crossmarket interactions. Cross market interaction capabilities 25910 mayinclude interfaces to one or more marketplaces, transactionenvironments, and the like, so that, among other things, an IDL may beconfigured with one market in a cross-market integration deployment as asource of data and with another market in the cross-market integrationdeployment as a consumer of the IDL. In embodiments, a similararrangement may be constructed between two or more markets so that datain either market can be used as a data source and can be influenced bydata from another market. Cross market interactions 25910 may beaccomplished through one or more market-to-market IDLs that form datapipelines for intelligent exchange of data among markets, such as dataabout buyers in one market and about sellers in another.

In the exemplary IDL platform embodiment of FIG. 258 , functions andprocesses 25912, for an exemplary market-oriented deployment may includesoftware-oriented transaction functions and processes, automatictransaction transactions and processes, and the like. Functions andprocesses 25912 for an IDL platform 25900 may include signalingavailability of data (e.g., emergence of an occurrence of source data)that impacts data produced by (for example) an intelligent data layer ofthe platform. Other exemplary functions and processes 25912 may includeembedding into smart contracts, tokens, publishing data on a schedule,or other occurrence (e.g., an initiation of a smart contract and thelike). Yet other functions and processes may include paymentsbetween/among machines and the like.

In embodiments, an IDL platform may include and/or be associated withintelligent data layer technology enablers 25914, such as 5G networking,artificial intelligence, visualization technology (e.g., VR/AR/XR),distributed ledger, and the like.

In embodiments, an IDL platform, such as platform 25900 may includeand/or leverage cloud-based virtualized containerization capabilitiesand services 25916, such as without limitation a container deploymentand operation controller, such as Kubernetes 25918 and the like.Cloud-based virtualized containers provide for IDLs to be deployed closeto source data, thereby potentially reducing network bandwidthconsumption or the potential for network disturbances in a data workflowand without substantive investment in infrastructure by the IDL operatorand/or consumer.

The IDL platform of FIG. 258 may further include business systeminterfaces 25920, such as APIs and the like that facilitate adoption ofIDLs by enterprises for development, among other things of adata-centric business workflow environment that enables cross-functionaldata use, seamless aggregation, and immediate contextualization ofcorporate data for each individual department, enterprise, subsidiary,and the like.

IDL enabled markets 25922 may be enabled by and/or enhanced through theadoption of intelligent data layer technology. Markets, such as marketsat an intersection of financial service and physical product offeringsmay be revealed and/or enabled through application of IDLs to helpparse, analyze, and provide intelligence for a wide range ofmarket-impacting and/or related data sources. These emergent markets maybe substantively constructed as a result of intelligence gathered by useof IDLs within or in association with individual markets, and the like.

Technologies that may be provided by and/or enabled by an IDL platform25900 may include intelligence services 25924, such as artificialintelligence, machine learning and the like. These intelligence services25924 may be provided by the platform 25900, or accessed (e.g., asthird-party services) via one or more interfaces o the platform 25900.Each IDL embodiment 25904 may be provided access to these intelligenceservices 25924 via the platform. One or more IDL embodiments 25904 maybring to the platform its own set of intelligence services, which may bededicated to the host IDL, or may be shareable, such as via the platform25900 with other IDLs of the platform, for example.

In the exemplary embodiment of FIG. 258 , transaction/market-orientedcapabilities, services, and deployment may include market platforms25926, transaction flows 25928, buyers 25932, sellers 25931, andintelligent data layers that enrich transactions, transaction servicesand the like 25930. For multi-party transaction environments, aplurality of IDLs may be configured and operated to satisfy a range ofconsumer needs for market analysis, transaction efficiencies, costcontainment, buy/sell decisions and the like.

Referring to FIG. 259 , an exemplary intelligent data layer 26000architecture is depicted. The exemplary IDL architecture includes acontrolled pipeline of data processing stages that process data from oneof a plurality of data sources 26002. The controlled pipeline includesan ingestion stage 26004, an analysis stage 26006, a derivedintelligence stage 26008, and an optional publisher stage 26010. Theingestion stage 26004 receives and/or harvests data from one or more ofthe plurality of sources 26002. Ingestion stage processing may includeparsing content of data sources, such as to determine structure,content, relationships among data elements, intended meaning of the dataelements, relationships between data, structure, and meaning, and thelike. In embodiments, an ingestion facility that may operate at theingestion stage 26004 may be configured to be aware of data sourceaspects, such as structure, etc. Ingestion stage 26004 may bepreconfigured, such as by an operator of the layer, a platformcontroller, an intelligent data layer controller 26012, and the like.Configuration of the ingestion stage 26004 may be based on one or moredata structures that represent aspects of one or more of the datasources 26002. One such aspect is a location of the data source, such asan Internet or other type of address (e.g., URL, port number, streamidentifier, publication and/or broad channel, sensor output location,and the like) from which data may be accessed, queried, pulled,downloaded, streamed or otherwise accessed. Another such aspect of adata source that may be included in a configuration of the ingestionstage 26004 may include an interface method or protocol, such as throughan Application Programming Interface, data transfer handshake, InternetProtocol, query language, data block size, access rate (e.g., maximum,frequency, or other timing-related parameter related to accessing thedata source) and the like. Yet other aspects of a data source 26002 thatmay be useful when configuring an ingestion stage 26004 of anintelligent data layer 26000 may include one or more meanings of datafrom the data source, such as may be represented through a data sourceontology and the like. Information such as units, scalars and the likefor numerical values provided from the data source may be represented inan ingestion stage configuration data structure. In an example of datasources providing measurement data, a first source may provide numericalvalues in inches, a second source may provide values in meters, and athird source may provide values in light years. This local data sourcecontext may prove useful for relating data sources. In an example ofdata sources providing reputation rating values, an ontology for eachsource that establishes a minimum value, maximum value, and incrementstherebetween provides a way of establishing meaning of a data elementfrom such a source. In yet another example of aspects of a data sourcethat may be usefully applied when configuring at least an ingestionstage 26004 of the intelligent data layer 26000, a data source mayimpose or be arranged with a geometry/structure that may imbue meaningon data values, relationships among data values, and the like. Oneexemplary embodiment of a structure that impacts meaning andrelationships of data value from a data source is a hierarchicalarrangement of the data. When an ingestion facility 26004 is configuredto receive/retrieve and process data that is configured as a hierarchy,the ingestion facility 26004 may be configured to assign a relationshipattribute to a pair of data values that are configured as parent/childin the hierarchy. Likewise, a rule that may be applied in the hierarchy,such as certain types of changes to a parent data value impacting acorresponding child data value establish an immutable relationshipbetween the data values as they are processed through the ingestionprocessing pipeline (e.g., ingestion, analysis, and intelligence).

Configuration of the ingestion stage 26004 may be automated, such asthrough programmatic configuration of data values in an ingestion stageexecution data structure. These data values may be retrieved from, forexample, an ingestion parameters portion of an IDL data processing datastructure 26018. Configuration of the ingestion stage 26004 may befurther automated through performing data parsing operations, and thelike of data from a data source to determine aspects, such as theexemplary aspects described above. Yet further intelligence functions,such as machine learning may facilitate training an artificialintelligence system to identify aspects of a data source that arerelevant for configuring an ingestion stage 26004 to receive and processits data. In embodiments, configuration of an ingestion stage 26004 maybe performed at least in part by an intelligent data layer control tower26012.

In embodiments, an ingestion stage, such as ingestion stage 26004 maydevelop an understanding of data sources, such as meaning of datavalues. In embodiments, developing an understanding may be in context ofan expected use of data from one or more data sources, such as use ofthe data for determining a status of a term of a smart contract, aresult of a software orchestrated transaction, and the like. Furtherdata from data sources may be understood within a context of other datasources, such as other data sources from which the ingestion stage 26004receives data. In an example of such understanding, a plurality ofmarketplace monitors may capture data regarding activity within amarketplace. When data from one of the marketplace monitors is placed incontext of marketplace transactions, data from other marketplacemonitors may be understood in this context, so that data valuesassociated with transactions within the marketplace may be evaluatedobjectively against other source data descriptive of the marketplaceactivity.

The ingestion stage 26004 may further be configured to maintain aschedule of collection activity for one or more of the data sources26002. A collection schedule may be one of a plurality of aspectsassociated with ingestion that may be influenced by data sources and byIDL pipeline processing needs (e.g., to satisfy needs of a user of theIDL). Such a collection schedule may be based on a rate or occurrence ofavailability of new or revised data from a source. In embodiments, somedata sources may produce new/updated data on a schedule determined fromactivities associated with the data source, such as a sample schedule ofa sensor and the like. In an example, a business rule for a system thatproduces data available through a data source may limit data releasesperiodically (e.g., such as at the end of a work shift, once per day,and the like). In another example of data source-dependent collectionactivity performed by an ingestion stage 26004, data may be madeavailable based on events, such as completion of marketplacetransactions and the like. An ingestion stage may monitor for suchevents. In an event monitoring example, the ingestion stage may monitora port on a data network for an indication of data availability at adata source. When the ingestion stage 26004 detects the indication(e.g., a change in a data value of the port), an ingestion process maycommence.

Other information of processes related to ingestion may include costs,such as costs to perform data source access, ingestion processing andthe like. Cost for data collection may include access fees charged by adata source (e.g., subscription costs, access event costs, demand-basedcosts, and the like). Costs for data collection may be based at least inpart on an amount that a consumer (e.g., a user of capabilities andoutput from an intelligent data layer) pays for access to informationproduced by the intelligent data layer that is based at least in part ondata from the data source. In an example of consumption-based ingestionfees, an intelligent data layer may ingest and process data from a datasource without initial payment to the data source and instead may makepayment based on use of the data by consumers of the intelligent datalayer. In embodiments, costs to perform data source access may be in theform of a credit to an operator of the intelligent data layer, such aswhen a data source provides a form of payment for use of its data. Theremay be a range of cost structures for source data access, at least someof which may be based on data source reputation, relevance of data fromthe data source, timeliness of updates of the data from the data source,and the like. In an exemplary embodiment, an intelligent data layer mayaccess data from a data source and utilize it a plurality of times toproduce layer intelligence for a plurality of users of the intelligentdata layer. Costs for access and for the occurrences of use of theaccessed data may be different from each other, such as a cost to accessmay be a multiple of (e.g., 2-time, 10-times and the like) of a cost foreach subsequent occurrence of use of the accessed data.

In embodiments, an intelligent data layer may be configured as acomponent of a producer of source data, so that a correspondingingestion facility may be owned (and optionally operated) by the dataproducer. In an example of data source owned intelligent data layers, adata source may retain privacy of its source data by exposing, such asthrough publication and the like, an output of the owned intelligentdata layer, which may include information derived from the source dataor select portions of the source data, such as non-confidentialinformation associated with marketplace transactions and the like.

In embodiments, activities of an ingestion stage, such as ingestionstage 26004 may be affected by factors not directly related to a datasource (e.g., data availability schedule and the like). Factors that mayinfluence ingestion stage activity may include a determination about whythe source data is being ingested. As an ample, an ingestion activityfactor may include an arrangement (e.g., a contractual term of a smartcontract and the like) between a producer of the content (e.g., amarketplace orchestrator) and a consumer of intelligence derived fromthe content by the intelligent data layer (e.g., a transactor of themarketplace). In this example, who is producing the data and who isconsuming IDL intelligence of the data may influence ingestion activity.When two different consumers have different ingestion requirements fordata from a single data source, ingestion activity for the data sourcemay be impacted differently. One basic example is rate of update ofsource data-based intelligence processing. One consumer may requiredaily intelligence updates, whereas another consumer may require weeklyupdates. One consumer may require intelligence based on aggregations ofdata from a plurality of sources and another may require intelligencebased on a single source of the plurality of sources. In these basicexamples, ingestion activity for data from a single data source mayvary. In addition to different use schedules and multiple sourceaggregation, a purpose of use of intelligence derived from data from adata source may influence ingestion stage activity. The ingestion stage26004, optionally directed by the intelligent data layer control tower26012 may determine that security use of derived intelligence may have ahigher priority for ingestion than other uses, such as monitoringshipping status and the like. Higher priority use may influenceingestion activity by, for example, ensuring that ingestion from asource used to generate security intelligence is performed ahead ofother lower priority ingestion activities.

Other factors that may affect ingestion stage 26004 activity may betime-constraint based. Factors such as source data validity phases(e.g., data from an access of data from a data source may be tagged withan expiration date), aging factors (e.g., over time an instance of dataaccess may have less relevance), and the like may impact ingestionactivity as well as impact other stages of an intelligent data layerpipeline. Ingestion stage (and other pipeline stage) activity may beinfluenced by other time-constraint based factors, such ascollection/availability cycle and related timing. In an exemplaryembodiment, a data source may provide access to its data (e.g., via anetwork port and the like) based on an access schedule, such as a dailyaccess window between 2 AM and 5 AM local time. An ingestion stage 26004may be configured and/or controlled to ensure to access data from thedata source during the access window. Other time-constraint basedfactors that may impact ingestion activity includes relative timingconstraints, such as data availability from a first data source may bedependent on updates to data from a second data source. An example ofsuch a data source availability relationship may be found in atransaction-oriented environment, where data from an inventory datasource would be dependent on changes to data in a sales transaction datasource. In another example, availability of data from data sourceproviding results of transactions may be dependent on transactionexecution timing, settlement timing, a right of last refusal window, andthe like associated with a transaction. In these examples, relationshipsamong data sources indicate sequences of ingestion that may be enactedby an intelligent data layer.

Yet further operation of an intelligent data layer 26000, includingingestion operation may also be based on a method of data collection. Inembodiments, a data source 26002 may be part of a data supply chain. Anexemplary embodiment of a data supply chain may include a physicalchain, such as may be embodied by a set of physical sensors (e.g.,industrial internet of things sensors) that capture physical activity(e.g., of an industrial machine, and the like) and provide arepresentation of that activity as a form of data. A physicalconnection, such as a set of networked devices (e.g., the Internet), mayconvey the representation of the activity produced by the sensor(s) to,for example, a physical access port (e.g., a networked computer and thelike) from which an intelligent data layer may ingest this data. Othertypes of data collection may include logical supply chains, such as datamarts, data marketplaces, aggregated data publishers, and the like. Inembodiments, data representative of a physical activity, such as aproduction machine in an enterprise, may be provided through a physicalinterface that presents the data from a corresponding sensor as itchanges in near real time. That same data may be provided through alogical interface, such as a data base that facilitates access to aplurality of values of data from the sensor, optionally with a capturetime, capture sequence and the like to enable batched or delayed use ofdata from the data source. An ingestion stage 26004 of an intelligentdata layer 26000 may be controlled to capture the physical near realtimedata, the stored data, or both to meet various needs of the intelligentdata layer. In an example, a market maker may utilize intelligencederived from a live feed of commodity pricing data to adjust its bid/askpricing activity. The market maker may utilize intelligence derived fromthe stored data values to determine its bid/ask volume activity.

As referenced above, meaning of data from a data source may be reliedupon for various intelligent data layer operations, such as parsingsource data, generating intelligence therefrom and the like. A datasupply chain may turn raw data (e.g., from a physical sensor) intocontextual data thereby overlaying meaning based on the context onto thedata. An ingestion stage 26004 may adapt operation, such as a parsingoperation based on such data supply chain activity. Whereas raw sensordata may be parsed according to a specification for a physical sensor,for example, contextually adapted data may be parsed according to thecontextual overlay as well as the raw data definition. As an example,raw sensor data may be accurate to three decimal places, whereas the rawdata when contextualized may only be presented with a single decimalplace. Raw sensor activity data that records start and stop times of anactivity may be accurate to the second, whereas that activity data in apractical context may only need to be represented in whole minutes. Inembodiments, the ingestion stage 26004 may apply contextual constraintsupon raw data, thereby adjusting at least one aspect of the raw data(e.g., a number of decimal places) based on the context.

In embodiments, the ingestion stage 26004 may be in communication withthe intelligent data layer control tower 26012. As noted above, theintelligent data layer control tower 26012 may communicate configurationto the ingestion stage 26004 as well as control all aspects of activityof the ingestion stage 26004. In embodiments, the ingestion stage 26004may be a set of ingestion and parsing algorithms as well as otheringestion functions described herein that may execute on one or moreprocessors. These one or more processors may comprise the intelligentdata layer control tower 26012. In such embodiments, the ingestion stage26004 may be integrated into the intelligent data layer control tower26012. Further in such embodiments, the ingestion stage 26004 mayexecute in virtual containers on, for example, cloud computing systemsthat are distinct from a physical embodiment of the intelligent datalayer control tower 26012.

The ingestion stage 26004 may communicate ingested data, results ofingestion, results of parsing, and the like to the intelligent datalayer control tower 26012.

Referring further to FIG. 259 , an intelligent data layer pipeline mayinclude an analysis stage 26006 that may receive data from the ingestionstage 26004. The analysis stage 26006 may receive raw ingestion data,adapted ingestion data (e.g., contextually adjusted), data derived fromingestion data (e.g., differences between sequential accesses of asingle data source), metadata associated with the ingestion data (e.g.,validity window, units, access costs, and the like), and the like.

The analysis stage 26006 may perform various operations on ingestionstage 26004 parsing and other ingestion activity results based on arange of factors, such as comparing data from a plurality of sources forsimilarity, fitness to a purpose, differences, based on types of datawithin or across data sources and the like. In embodiments, analysis mayinclude comparing sources against a target use of intelligence derivedfrom a data source. Analysis of ingestion results may attempt todetermine if one or more data elements from a data source may meetconsumption target requirements, such as meeting a validity timeconstraint, an accuracy constraint, a frequency of update constraint,relevance to a consumption subject matter focus, and the like. Inembodiments, an intelligent data layer may target providing intelligencefor buyers of services in a software orchestrated transactionmarketplace. The analysis stage 26006 may determine if one or more dataelements from one or more data sources 26002 may be relevant forgenerating intelligence about the services and based on the results ofanalysis may indicate to the intelligent data layer control tower 26012to utilize the data for generating derived intelligence. An intelligentdata layer 26000 may publish or otherwise convey requests for data, suchas types of data, and the like that one or more data sources may attemptto meet. The analysis stage 26006 may determine if ingested data meetsrequirements of the published request for data, such as if the datacomplies with one or more parameters in the request.

In embodiments, the analysis stage 26006 may facilitate configuring datain the layer for publication, such as configuring one or moreadvertisements that characterize the ingested data in terms of potentialintelligence value, relevance and the like. Examples include makingdata, such as derived intelligence data available on a marketplace(e.g., configuring indexing schemes and the like), making the contentsearchable (e.g., identifying keywords, terms, values, or the like thatmay facilitate discovery of intelligence derived from the ingested datathrough use of a search capability. The analysis stage 26006 mayfacilitate access visibility to information of the intelligent datalayer by publishing, communicating, or broadcasting samples of the dataover a network, directly to potential consumers and the like. Inembodiments, potential consumers of intelligent data layer intelligenceand services may include other intelligent data layers, existing datasupply chain participants, and the like.

In embodiments, the analysis stage 26006 may suggest, predict, and/orestimate value of ingested data for a plurality of different consumers.These estimates may be used by the control tower to impact intelligentdata layer functions, such as IDL intelligence pricing and the like thatmay be differentiated for different users. Further, such analysis mayindicate that intelligence derived from a first data source may be moreor less valuable to different target consumers.

The analysis stage 26006 may use feedback from intelligent data layerusers regarding, among other things, usefulness of intelligence derivedfrom one or more data sources 26002 to facilitate ingestion and analysisactivities and the like. In an example, positive feedback onintelligence derived from a data source may result in communication fromthe analysis stage 26006 to the data layer control tower 26012 to makeuse of the data source for deriving other types of intelligence and thelike. Feedback handled by the analysis stage 26006 may include feedbackfrom uses of similar data, such as use of data from different sourcesthat may be determined to be similar. In an example, positive feedbackregarding use of a data from a first data source may trigger thepublishing requests for similar data. Feedback handled by the analysisstage 26006 may be based on similar intelligent data layers.

In embodiments, multiple intelligent data layers may collaborate to meetdata consumer needs, such as cross market transaction environments andthe like. An analysis stage 26006 of a first IDL (e.g., for producingmarket intelligence for a product market) may collaborate with ananalysis stage of a second IDL (e.g., for producing market intelligencefor a service market). In embodiments, IDL collaboration may be enabledthrough exchange of data, such as by a first collaborating IDL analysisstage producing analysis results that are provided as a data source fora second collaborating IDL.

In embodiments, an IDL may ingest data from a plurality of data sources;each such set of data may be individually analyzed by the analysis stage26006. However, the analysis stage 26006 may analyze data from aplurality of data sources, such as by aggregating data from theplurality of sources. In embodiments, data from a plurality of datasources may be parsed, such as by the ingestion stage 26004 so that datawith similar characteristics (e.g., data that is indicative of a buyerreputation) may be aggregated and analyzed by the analysis stage 26006.Examples of multiple data sources that may provide data with similarcharacteristics include mobile devices, types of sensors, market-focusedtransaction systems (e.g., commodity trading, resource exchange,currency exchange markets and the like). In embodiments, the analysisstage 26006 may be in communication with the intelligent data layercontrol tower 26012. As noted above, the intelligent data layer controltower 26012 may communicate configuration data (e.g., sets of data thatenable the analysis stage 26006 to perform various analysis functions)to the analysis stage 26006 as well as control all aspects of activityof the analysis stage 26006. In embodiments, the analysis stage 26006may be a set of analysis algorithms that may execute on one or moreprocessors. These one or more processors may comprise the intelligentdata layer control tower 26012. In such embodiments, the analysis stage26006 may be integrated into the intelligent data layer control tower26012. Further in such embodiments, the analysis stage 26006 may executein virtual containers on, for example, cloud computing systems that aredistinct from a physical embodiment of the intelligent data layercontrol tower 26012.

The analysis stage 26006 may communicate ingested data, results ofanalysis, information received from an ingestion stage 26004, and thelike to the intelligent data layer control tower 26012.

A stage in an intelligent data layer pipeline may include anintelligence stage 26008. An intelligence stage 26008 may utilizeartificial intelligence capabilities to develop an understanding aboutdata sources including, among things, uses of data, values of data,applicability of data, collection patterns and relevance to intelligenceconsumption and the like. Additional intelligence that may be derived byintelligence stage 26008 may include, without limitation, layer specificdata relevance, relevance of data from one layer to another, value ofintelligence to a consumer, such as to a transactor. In an example,intelligence stage 26008 may derive intelligence useful for forming newmarketplaces from transactional data gathered from an existingmarketplace.

In embodiments, the intelligence stage 26008 may be in communicationwith the intelligent data layer control tower 26012. As noted above, theintelligent data layer control tower 26012 may communicate configurationdata (e.g., sets of data that enable the intelligence stage 26008 toperform various intelligence functions) to the intelligence stage 26008as well as control all aspects of activity of the intelligence stage26008. In embodiments, the intelligence stage 26008 may be a set ofintelligence algorithms that may execute on one or more processors.These one or more processors may comprise the intelligent data layercontrol tower 26012. In such embodiments, the intelligence stage 26008may be integrated into the intelligent data layer control tower 26012.Further in such embodiments, the intelligence stage 26008 may execute invirtual containers on, for example, cloud computing systems that aredistinct from a physical embodiment of the intelligent data layercontrol tower 26012.

The intelligence stage 26008 may communicate data received from theanalysis stage 26006, derived intelligence, and the like to theintelligent data layer control tower 26012.

Referring further to FIG. 259 , the intelligent data layer 26000 mayinclude a consumer portal 26010 that may communicate with elements ofthe IDL pipeline, such as the intelligence stage 26008 from which theconsumer portal may receive derived intelligence. The consumer portal26010 may facilitate access to derived intelligence (and optionally toother data of the intelligent data layer 26000). The consumer portal26010 may announce availability of derived intelligence to apreconfigured set of consumers and candidate consumers through use of amessaging channel (e.g., SMS messaging and the like). The consumerportal 26010 may announce derived intelligence through various othertechniques including, broadcasting across one or more communicationchannels (e.g., TWITTER™, and the like). The consumer portal 26010 maydeliver at least select derived intelligence to intelligent data layerconsumers based on a subscription or similar arrangement between theconsumer(s) and the intelligent data layer. In embodiments, the consumerportal 26010 may reference (or be provided, such as by the intelligentdata layer control tower 26012) intelligence publication configurationdata that may identify which consumers are to receive which portion(s)of intelligence derived from which data source and cause the derivedintelligence to be provided to (and/or made available to) one or moreconsumers based on this intelligence publication data. The intelligencepublication data may be stored, such as in an intelligent data layerdata store 26020 and accessed by the consumer portal 26010 via, forexample, an IDL data store access function of the intelligent data layercontrol tower 26012. The consumer portal 26010 may also communicate withthe intelligent data layer control tower 26012, such as to receiveconfiguration, access intelligence data, analyzed data, ingested dataand the like.

The consumer portal 26010 may further receive from one or more IDL dataconsumers, consumer preferences for interfacing with the consumer,requests for updates to previously communicated derived intelligencedata, requests for onboarding, feedback on uses of derived intelligencedata and the like. In embodiments, a consumer may communicate a derivedintelligence delivery schedule to the consumer portal 26010 where it maybe combined with other intelligence delivery data, such as otherconsumer delivery schedules, and the like and utilized by the consumerportal (26010) and/or the intelligent data layer control tower 26012when performing derived intelligence delivery and communicationfunctions.

An intelligent data layer may include and/or reference an intelligentdata layer control tower 26012 that may provide configuration, control,storage, and processing capabilities for the IDL, such as for providingaccess by an ingestion stage 26004 to ingestion algorithms, facilitatingaccess by a derived intelligence stage 26008 to intelligence services26014, managing storage of an intelligent data layer configuration datastore 26018, managing storage of intelligent data layer ingestion dataand outcomes, analysis outcomes, derived intelligence and the like in anIDL data store 26020, and without limitation providing a mechanism bywhich a user, such as an owner and/or operator of the intelligent datalayer 26000 can configure and otherwise interface with the modules ofthe intelligent data layer. In embodiments, the intelligent data layercontrol tower 26012 may provide (or provide access to) processingcapabilities that may be used by the various stages, such as theingestion stage 26004, the analysis stage 26006, the derivedintelligence stage 26008, the consumer portal 26010, the intelligenceservices 26014, and the like.

In an exemplary embodiment, the intelligent data layer control tower26012 may function in cooperation with the ingestion stage 26004 togather and store ingestion parameters for use when executing variousingestion activities, such as determining availability of newlyrefreshed data from one of a plurality of data sources 26002 (e.g., aschedule of release of new data or a port address of an indicator of newdata status). In embodiments, parsing data may include use of a parsingkey set of ingestion parameters and the like. These parameters may beaccessed in the intelligent data layer configuration data store 26018.

In another exemplary embodiment, the intelligent data layer controltower 26012 may facilitate access to analysis algorithms by the analysisstage 26006. Further the intelligent data layer control tower 26012 maywork cooperatively with an algorithm portal 26016 to receive algorithmsfor analysis, ingestion, deriving intelligence, and the like. As anexample, a data source 26002 may identify and/or provide one or moreingestion algorithms for performing ingestion actions on data providedfrom the source. The algorithm may be provided through the algorithmportal 26016 received and optionally vetted by the intelligent datalayer control tower 26012 and stored in the intelligent data layerconfiguration data store 26018. In another exemplary embodiment of useof the algorithm portal 26016, a consumer may provide an algorithm forderiving intelligence from data under the consumer's control, such as ina marketplace transaction environment in which a seller providestransaction data as a source of data to the intelligent data layer forprocessing, optionally with other relevant data, for derivingintelligence associated with seller marketplace activities. Inembodiments, deployment of an intelligent data layer as part of a dataworkflow for an enterprise may involve adapting existing workflow stepswith intelligent data layer capabilities. As an example, a purchasingdepartment of an enterprise may have a set of algorithms that are usedto process sales forecast data for generating purchasing guidelines. Anintelligent data layer may be constructed for the enterprise thatproduces intelligence regarding the generated purchasing guidelines byutilizing sales forecast processing algorithms that have been uploadedthrough, for example, the algorithm portal 26016.

In embodiments, the intelligence services 26014 may include a range ofintelligence functions and capabilities including, without limitationartificial intelligence functions, machine learning functions, neuralnetwork functions, prediction capabilities, and many others. In anexample of intelligence services 26014 for an intelligent data layer26000, an ingestion stage 26004 may provide data from a data source,along with associated descriptive information (e.g., metadata,structural data, ontology data and the like) to a self-learning neuralnetwork capability of the intelligence services 26014 to aid indetermining an approach to parsing the data source.

Intelligence services may further have access to subject matterassociated intelligence, such as cross-market intelligence gatheredthrough processing, optionally external to the intelligent data layer26000, marketplace configuration, operational, and transaction outcomesfor different sets of cross-market offerings. In continuing the exampleabove of use of intelligence services for ingestion, this subject matterintelligence may be applied when a data source is determined to berelated to a product or other offering that is similar to products orofferings on which the subject matter intelligence is based. So, if asource of data relates to a product (e.g., mobile device) and subjectmatter intelligence known to the intelligence services 26014 is based onor associated with mobile device technology, the correspondingintelligence services may be utilized for enhancing/optimizing pipelineoperations being performed on the source data.

In embodiments, an intelligent data layer, such as intelligent datalayer 26000 as depicted in FIG. 259 , may include a user interface 26022through which a user, such as an operator and the like, may interfacewith modules of the IDL and the like (e.g., query and maintain data inthe parameter data store 26018 or the pipeline data store 26020). Theuser interface 26022 may facilitate configuring portions of theintelligent data layer, such as the algorithm portal, data retentionrules for data stored in the IDL data store 26020, prioritization of useof the intelligent data layer resources by data consumers, and the like.

Referring to 260, an intelligent data layer embodied as an accessibleservice, such as a service available to the public for accessingintelligence from a range of data sources. In embodiments, theintelligent data layer embodiment of 260 may operate independently toprovide intelligence determination services for data consumers. Theindependent intelligent data layer of 260 may be hired/rented/utilizedby a plurality of independent data consumers, such as through payment ofa subscription fee, one-time use fee, and the like. In embodiments, theindependent intelligence data layer of 260 depicts an entity forproducing data for a plurality of data consumers. A micro-servicearchitecture, such as described herein and elsewhere, may further enableisolated and independent processing throughout the layer operatingpipeline for each consumer, such as by initiating a virtualizedcontainer to perform one or more of the intelligent data layer pipelinefunctions for each data consumer (e.g., consumer X, Y, Z). In anexample, a virtualized container may be operated (e.g., on a cloudprocessing architecture that has low latency access to data beingprocessed in the container). In embodiments, low latency access here mayinclude, without limitation, local access, such as a data processingserver in a networked data storage facility and the like. A virtualizedcontainer may be configured with a consumer-specific instance of theingestion stage 26104. In this example, the consumer-specific instanceof the ingestion stage 26104 may be configured with consumer-specificingestion parameters and/or functions, so as to, for example, listen tocertain source data channels 26116 designated and/or selected whenconfiguring the ingestion stage instance for the consumer. Inembodiments, an intelligence derivation stage 26108 of the intelligentdata layer pipeline may be instantiated in, for example, a virtualizedcontainer environment. The instance may be configured with intelligencederivation algorithms associated with a specific consumer, such as dataconsumer Y 26112.

While data consumer-specific instances of pipeline stage services aredescribed as possible embodiments for the independent intelligent datalayer of 260, other architectures are possible and contemplated herein.One such architecture is abstracting (e.g., through use of virtualizedcontainers) use of pipeline stage functions that operate on one or moreservers. In this example architecture, a core pipeline stage service mayoperate on a server with data for a plurality of data consumers beingstored in a low-latency data storage facility. In this exampleembodiment, virtualization facilitates on-demand access to the computingcapabilities of the server and more specifically to the computingcapabilities and functions of the pipeline stage services, whileisolating input data, in-process data, configuration data, andintelligence outcomes so that each consumer appears to have full accessto the intelligent data layer based on their needs.

In yet another exemplary embodiment, a plurality of functions of theintelligent data layer may be instantiated within or associated with avirtualized container environment that may be dedicated to providingintelligence services to a specific data consumer or set of dataconsumers. In this way, ingestion, analysis, intelligence, controltower, storage, and publishing (e.g., producing a data and/orintelligence feed for the specific consumer) may be logically configuredwithin a virtualized environment for providing intelligent data layerservices independently of other consumers.

The embodiment of 260 may be differentiated from other embodiments, suchas embodiments where an intelligent data layer is integrated into a dataconsumer (or optionally a data supplier) computing environment.

Data layer processing stage elements 26104, 26106, and 26108 may, forpurposes of disclosure efficiency, be substantially, although notexhaustively, as described in corresponding elements 26004, 26006, and26008 from FIG. 259 respectively. Further, some features of acorresponding stage in FIG. 259 may, in embodiments, be configureddifferently or excluded from a corresponding stage in 260. As anexample, the ingestion stage 26004 may include data conversioncapabilities that may be excluded from embodiments of the ingestionstage 26104, at least for instances where those capabilities are notneeded, such as when an instance of the ingestion stage 26104 isingesting data from a source for which at least some types of dataconversion are not required.

In embodiments, ingestion stage 26104 may, in addition to interfacingwith data sources 26102 (that may be, for purpose of compact disclosure,substantially, although not exhaustively, as described in correspondingelement 26002 from FIG. 259 ) may further interface with data channels26116 and on-demand data sources 26118. The data channels 26116 may beserviced by an ingestion stage, using, for example, a channel listeningfunction that may be controlled by and/or integrated with intelligentdata layer control tower 26120. In embodiments, data consumers mayindicate, such as through configuration of the consumption parameters26122 and the like specific channel(s) of data from which, for exampleintelligence is desired, or from which data is required for processingin one or more of the intelligent data layer processing pipeline stagesbased on, for example, configuration data for a consumer-specificinstance of the intelligent data layer. A data consumer, such as dataconsumer X 26110 may indicate that a channel that delivers a stream oftransactions within or for an institution or marketplace as a channelsource of data from which or in association with which the data consumerdesires derived intelligence. As an example, a buyer associated with atransaction marketplace, may desire to be informed, such as through useof the methods and systems of intelligence data layers described herein,of intelligence to be derived from a stream of transaction outcomesprovided on a secondary marketplace channel. In this example, theintelligent data layer control tower 26120 may process consumptionparameters 26122 to configure a schedule for listening to secondarymarket transaction outcomes. The consumption parameters for consumer Xmay, in this example also define one or more of ingestion and/oranalysis, and/or derived intelligence algorithms and/or processes to beapplied when processing those outcomes along the pipeline (as streamed,in batch or the like as may be specified in, for example, theconsumption parameters 26122 for consumer X 26110) via the ingestionstage 26104, the analysis stage 26106, and the intelligence stage 26108.In embodiments, data channels 26116 may also publish data according to apublication schedule. The intelligent data layer control tower 26120 maycoordinate the consumption parameters 26122 with each channel'spublication schedule so that the ingestion stage 26104 connects with achannel that corresponds to the consumption parameters 26122contemporaneous with the scheduled publication time. In an example, aninstance of the ingestion stage 26104 may be configured to beginlistening for data from a specific channel or channels before or at astart of a scheduled publication. Alternatively, the ingestion stage26104 may be configured and/or activated to begin listening at a pointin time relative to the start of scheduled publication, such as after apreamble of the publication, at an initiation of publication of detaileddata values, at or near to an end of publication of detailed datavalues, or after a configurable number of publication steps, and thelike.

As noted elsewhere herein intelligence may be derived from sourcecontent, structure, and metadata, among other things. In embodiments,intelligence associated with a data channel 26116 may be derived basedat least in part on the respective channel's publication schedule. Oneexample of intelligence that may be based on a publication scheduleincludes awareness of timing of potential changes in data from thechannel. Therefore, changes in resulting intelligence may be adaptedbased on the schedule, such as indicating that intelligence derivedprior to a new data publication schedule may be deemed to be “aged”(e.g., weighted lower than more updated intelligence and the like).Time-based averaging of data from such a source may be synchronized withits publication schedule.

As noted herein, another potential source of data may include on-demanddata sources 26118. Unlike channels of data, such as data channels 26116that may publish data on a schedule or based on events or the like, anon-demand data source 26118 may be controlled, such as by theintelligent data layer control tower 26120 to generate (e.g., publish ormake available) data when requested. An on-demand data source 26118 mayinclude devices that “sleep” by activating a lower power mode in betweenrequests (demands) for data. While depicted as individual entities, datasources that provide channels 26116 and data sources that provideon-demand data 26118 may not be distinct. A single data source mayprovide a plurality of data interfaces, including in this example anon-demand interface and a publication channel interface.

The independent intelligent data layer 26100 may include a configurationdata storage facility 26122 that may include, among other things,consumption parameter storage for each of a plurality ofclients/consumers/users of the layer 26100, such as consumer X 26110,consumer Y 26112 and/or consumer Z 26114 and the like. In embodiments,layer configuration data for a data consumer may be stored separatelyfrom the parameter storage 26122 and may be accessed through, forexample, a link to the separate configuration data in the parameterstorage 26122. Configuration parameter storage facility 26122 (e.g.,that may be virtualized and the like) may be configured with dataconsumer distinct portions to facilitate isolation between users of thelayer 26100. A type of configuration parameter that may be accessible inor through the configuration parameter storage facility 26122 mayinclude ingestion parameters, such as for facilitate control ofingestion activities by, for example, the intelligent data layer controltower 26120, an instance (e.g., in a virtualized container) of theingestion stage 26104 and the like.

The layer configuration storage facility 26122 may be accessed by a dataconsumer of the data layer 26100 through various computer-to-computerprotocols, including networked storage protocols, streaming protocols,indirect access protocols (e.g., through a proxy service that providesaccess to the storage) and the like.

In the exemplary embodiment of 260, configuration data may includeinformation that facilitates ingestion (e.g., data sources and ingestioncontrols), analysis (e.g., data source processing, data sourcerelationships, and the like), intelligence (e.g., intelligencealgorithms, and/or identification of third-party intelligence servicesto be used when processing data for the consumer) and the like.

An intelligent data layer 26100 may include and/or have access toartificial intelligence services, such as machine learning services toenhance, among other things, handling of configuration parameters, suchas ingestion parameters, data weights and the like that impactoperations of the pipeline stages. In embodiments, machine learning26124 may facilitate processing feedback, such as results of derivingintelligence via the intelligence stage 26108, data analysis outcomesvia the analysis stage 26106, ingestion processing (e.g., data parsingand the like) via the ingestion stage 26104 and the like. In an exampleof machine learning-enabled feedback utilization, a set of consumptionparameters (e.g., including a minimum window of time after ingestion ofdata from a data source 26102) may be adapted based on learning fromoutcomes of intelligence derived from the ingested data. The feedbackmay facilitate identifying an impact on the derived intelligence basedon an amount of time since last ingestion from the data source. Amachine learning system may train the intelligent data layer controltower 26120 ingestion processing control algorithm(s) to, for example,adjust (e.g., increase) the minimum window of time between ingestionevents from a data source based on a degree of change in intelligencederived from data ingested from the data source. This learning mayreduce ingestion events, ingestion frequency and the like, which canlead to reduced operation costs, while maintaining at least a minimumlevel of confidence in the derived intelligence. This information may berelayed on to a corresponding consumer, such as consumer X 26110 whereingestion frequency information may be used to further optimize orbenefit use of the derived intelligence.

Referring to FIG. 261 , an intelligent data layer is depicted as a datastrategic approach for an enterprise. The intelligent data layer of FIG.261 may include several elements that may have commonality with otherembodiments herein, such as, without limitation an ingestion pipelinestage 26212, an analysis pipeline stage 26214, an intelligence pipelinestage 26216, an intelligent data layer control tower 26224. While theseand corresponding intelligent data layer elements from other embodimentshave similarity, there may be some differences that are generallydescribed below.

Overall, a data strategic approach for an enterprise as depicted in FIG.261 may facilitate deriving intelligence from a plurality ofenterprise-specific data sources each with optionally distinctontologies for meeting data-based intelligence needs and preferences fora plurality of enterprise entities (e.g., department, specific user,user role type, and the like), while taking into considerationenterprise goals, such as goals that are aligned across enterpriseentities. Such an intelligent data layer may further facilitatecompliance with security requirements, such as user/role-basedrestrictions on data exposure. One substantive advantage of such a datastrategic approach is that intelligence may be derived from sources ofdata to which a given consumer of the intelligence (e.g., a department,employee, contractor, and the like) does not have access permissions.Another benefit of such a data strategic approach for an enterprise isharmonization of disparate data source ontologies through use ofsource-specific ingestion and/or analysis metadata. This harmonizationmay facilitate deriving intelligence from substantively distinct datasources using, for example, a common understanding of some aspects ofthe data sources. An example may include text data stored in differentlanguages may be harmonized to a preferred common language that may beused for analysis, deriving intelligence, and the like. Many otherexamples are evident, such as different time zones for differententities of an enterprise, different currencies, and the like.

In embodiments, an ingestion pipeline stage facility 26212 may, usingone or more of the exemplary techniques for ingestion of data from oneor more data sources described herein ingest data from portions of anenterprise, such as individual departments, business units, fieldoffices, subsidiaries, and the like. These enterprise-centric datasources may be referred to herein as department sources 26202. As notedabove, data ontologies, formats, structures, units, and the like mayvary from one department source 26202 (e.g., sales) to another (e.g.,engineering). The ingestion stage 26212 may be constructed and/orconfigured to process data ingested from different department sources26202 using corresponding ingestion parameters that may beupdated/utilized on per department source ingestion-event basis. Forexample, when data is ingested from an engineering department source,ingestion control parameters (e.g., data ingestion rates, contentdefinitions, and the like) associated with the engineering departmentsource may be utilized by ingestion stage 26212 algorithms, circuitryand the like. When ingestion is performed from a sales departmentsource, the ingestion stage 26212 algorithms, circuitry and the like maybe adjusted (e.g., reconfigured) to enable ingestion of sales departmentdata. In embodiments, the intelligent data layer control tower 26224 mayconfigure the relevant portions of the ingestion stage 26212 based onthe specific department source 26202 from which data is to be ingested.Further, the intelligent data layer control tower 26224 may adapt itsinternal control of ingestion stage 26212 resources (e.g., computingelements, data communication elements, and the like) based on anindication of one of the department sources 26202 from which data is tobe ingested.

In embodiments, the intelligent data layer as a data strategic approachfor an enterprise of FIG. 261 may interface with a variety of types ofdata sources, such as department data sources 26202 described above,on-demand sources 26220 that may be similar to on-demand sources 26118of the embodiments of FIG. 260 , and at least channels, such as periodicreport channels 26218 that may be similar to the channels ID-316 of theembodiments of FIG. 260 . As noted above not all aspects of the datasources of FIG. 261 (department sources 26202, channel periodic reports26218, and on-demand source(s) 26220) include all of the functions andfeatures of corresponding elements in other embodiments, such as thecorresponding element depicted in FIG. 260 (e.g., sources 26102,channels 26116, and on-demand sources 26118 respectively). An exemplaryenterprise embodiment may include periodic reports channels 26218 thatare comparable to periodic enterprise reports. Examples include, dailyupdates of MRP systems, hourly updates of cash flow, weekly salesreports, quarterly profit/loss reports and the like. These examplesdepict only a few of the wide range of enterprise-specific data sourcesembodied as the periodic report data sources 26218. Others includewithout limitation, start/stop of shift production records, qualitycontrol periodic reports (e.g., coordinated with production activities),and the like. These data sources may produce updates of data on aperiodic basis for use by the intelligent data layer intelligencederivation pipeline. As an example of periodic channel data sourcing,sales figures for each region may be updated and processed by a businessdata processing system of the enterprise each day between 3 and 5 AM toproduce daily sales reports (e.g., by region, sales office, persalesperson, enterprise wide and the like). The intelligent data layermay ingest data from a corresponding periodic report at, for example 5AM for processing through the intelligence pipeline so that intelligencederived from sales data can be delivered (e.g., published, and the like)as a general broadcast for access by a plurality of intelligenceconsumers of the enterprise and/or delivered to specific intelligenceconsumers of the enterprise (e.g., specific department/role, such asrole X 26206 and the like). As described herein, ingestion through theingestion stage 26212 may further based on detecting an indication ofnewly available data from a data source, such as a periodic report datasource 26218. This may exemplarily be performed by a function of theingestion stage 26212 that samples a port of one or more data sources ofthe enterprise, for an indication of new data availability. When such anindication is detected, ingestion may commence and/or the intelligentdata layer control tower 26224 may be notified, such as through an APIof the control tower and the like.

In embodiments, the intelligent data layer control tower 26224 mayreceive an indication of available ingestion data and initiate aningestion sequence of events that may include optionally interfacingwith one or more intelligence consumers (e.g., depart/role X, Y, Z) forauthorization to perform ingestion from the indicated source. Such asequence may be useful when the corresponding data source (and/or anoperator/owner thereof) requires payment (e.g., receiving a credit to anaccount and the like) when ingestion is performed. In this way, aconsumer of intelligence derived from the available ingestion datasource (based on the indication of new source data) may optionallyauthorize or decline performance of the ingestion. The intelligent datalayer control tower 26224 may include these and other factors whencontrolling the layer resources for functions such as ingestion and thelike. The intelligent data layer control tower 26224 may further manageingestion based in an indication of newly available source data bytaking into consideration other uses of the source data. As an example,even when a source of data charges a fee for access of its data, theintelligent data layer may ingest the data independent of anauthorization by a target intelligence consumer. In this example, theintelligent data layer may be configured to derive and publishintelligence for consumption by intelligence consumers of the enterprisefor each indication of available data from a data source andsubsequently debit an account of an intelligence consumer (e.g., abudget of department X) for a portion of the ingestion fee when theintelligence consumer receives/accesses the derived intelligence.

Another type of data source for an embodiment of an intelligent datalayer as a data strategic approach for an enterprise may be an on-demandsource 26220. In embodiments, such an on-demand data source 26220 may becomparable to the on-demand data source 26118 of the embodiments of FIG.260 .

In embodiments, operation of stages along a data intelligence derivingpipeline of the intelligent data layer of FIG. 261 may be influenced byenterprise aligned goals 26204. These goals may be embodied as businessrules that may be applied during processing of data in one or more ofthe stages of the pipeline. As an example, a business rule 26204 foringestion may indicate that ingestion from some sources should beperformed only during non-peak times (e.g., not during regular businesshours, not during a time when end of the data reports are being uploadedand the like). Such a business rule may impact ingestion from acorresponding source by adjusting a time of day when ingestion isperformed, or a rate of ingestion to avoid overloading the data sourceduring its peak time. Another exemplary enterprise aligned goal 26204may include performing analysis of ingested data only after adjustingthe ingested data for compliance with a data record structure (e.g.,number of decimal places). The analysis stage 26214 may react to such agoal by first adjusting data records received from the ingestion stage26212 to comply with this goal and then performing one or more analysisfunctions. Another exemplary enterprise aligned goal 26204 that mayinfluence handing of data by one or more of the intelligence derivationpipeline stages may include use of a minimum number of different datasources when deriving some types of intelligence. This may beexemplified by adapting one or more intelligence derivation algorithmsto be processed by the intelligence stage 26216 to ensure inclusion ofthe minimum number of data sources. Another exemplary aligned goal 26204may specify a maximum amount of historical data to be factored whenderiving intelligence. This may be embodied as a maximum number of daysof historical data, maximum number of historical ingestion cycles, andthe like. Yet another exemplary enterprise aligned goal 26204 forcontrol of the intelligence derivation pipeline stages of an enterpriseembodiment of FIG. 26104 is use of artificial intelligence duringprocessing of data. While a specific intelligence algorithm may notimpose a constraint to use artificial intelligence, the algorithm may beadapted to use artificial intelligence (e.g., machine learning and thelike) based on the aligned goal.

An intelligent data layer control tower 26224 may configure and/or adaptan on-demand ingestion process, such as ingesting data from on-demandsources 26220, based on user-related instructions, preferences and thelike. Users of the platform may be users associated with an enterprisefor which the Intelligent data layer control tower is configured. Thetower 26224 may include an interface to a set of on demand usercredentials 26222 that may be referenced when responding to userrequests for ingestion and other operations of the system. Inembodiments, the user credentials 26222 may inform access privileges andrights for individual users to effect on-demand ingestion and otherintelligent data layer functions. In embodiments, the user credentials26222 may be used to identify specific configurations and/or ingestionactivities associated with certain users, certain types of users,certain user roles, and the like. On-demand user credentials 26222 mayinform ingestion activities, scheduling, and the like. In an exemplaryuse of user credentials 26222 the intelligent data layer control tower26224 may utilize aspects of user credential content to facilitateprioritizing use of on-demand ingestion resources. In this example, aproduction supervisor may request use of the ingestion capabilities ofthe system for validating employee payroll contemporaneously with abenefits team looking for benefit cost-trends. In example embodiments,the production supervisor, represented by an entry in an on-demand usercredentials data store 26222 may, for this specific request, bedesignated as having higher priority access to the IDL frameworkresources than the benefits team member due at least in part to arelationship of the supervisor activity (payroll) to the benefits teammember activity (research). This may result in the IDL control tower26224 organizing the resources of the system to meet the supervisor'sinformation needs ahead of the benefits team member's information needs.

Activities of an intelligent data layer, such as the intelligent datalayer depicted in FIG. 261 , may further be adapted based on one or moreset of rules associated with the layer, such as layer rules for one ormore departments, roles, and the like as may be depicted by layer rules26226. Layer rules 26226 may influence a wide range of layer operationsincluding ingestion, data sourcing, analysis, intelligence derivation,generation of out feeds, use of machine learning, and the like. Layerrules 26226 for a specific department may be prioritized overuser-specific layer constraints that may be derived from usercredentials 26222. Similarly, enterprise aligned goals that may beembodied as one or more sets of business rules 26204 may be prioritizedover department and/or role-associated layer rules 26226. When these andother various sources of rules and the like that may influence anorganization, activities, and functionality of the intelligent datalayer conflict, such prioritization of rules (e.g., business rules ruleover department rules that rule over user credentials, and the like) maybe employed by the intelligent data layer control tower 26224 to resolveconflicts.

In example embodiments, an intelligent data layer control tower 26224may apply department layer rules 26226 when configuring and/or operatingan ingestion facility 26212 for handling data sources, such as source ofan enterprise for various departments 26202. A department layer rule26226 may be configured to inform the intelligent data layer regardinglimitations of use of data sourced from department X. Such a set ofrules may indicate that data from department X is not available for useby members of department Y unless authorized to do so by anauthorization agent, such as a supervisor or owner of access rights todepartment X data. Another such set of rules may indicate that summarydata for department X (but not details that contribute to summarizingthe data, such as specific entries in department X data, orsummarization rules, and the like) may be used freely by any enterpriseuser with access credentials (26222) that permit use of the intelligentdata layer.

In addition to department layer rules 26226 may include role-specific orrole-associated data layer rules. Data layer rules associated with arole may be for one or more types of roles in an enterprise (e.g., allhuman resources personnel, human resource managers, human resourceexecutives, all executives independent of department, and the like).Data layer rules for roles may include rules associated with externalroles, such as vendors, regulators, business partners, affiliates,subsidiaries, competitors, smart contract systems, automated transactionsystems, and the like. External role data rules may influence a range ofoperations and data access services available to external users. As anexample, a marketplace may use an intelligent data layer to provideaccess to marketplace data (e.g., activities of the marketplace,financials, and the like). The marketplace may configure an automatedtransaction system role layer rule that enables access to a subset ofthe marketplace information, such as transaction type and price, but notparticipants in the transaction, settlement terms among theparticipants, and the like. While personally identifying information isone example of information that may not be exposed to certain externalroles by a marketplace, the example above suggests that there is a widerange of other, potentially valuable information that may be harvestedfrom marketplace activity that operators (and/or participants) of themarketplace may deem to be excluded from access by external transactionautomation systems, for example. Layer rules 26226 may be enforced bythe intelligent data layer control tower 26224 in a variety of waysincluding, without limitation, adapting ingestion services (e.g.,ingesting only a subset of information available from a source, limitinga rate or quantity of on-demand ingestion, and the like), applyingfilters and the like during analysis operations 26214 (e.g., to controlgeneration of analysis outcomes, such as by rounding results to fewerdecimal places, removing some results outside of a designated range ortimeframe, and the like), adapting intelligence derivation functions(e.g., providing trending content, but avoiding predictions basedthereon), and the like.

An intelligent data layer 26200 may include and/or have access toartificial intelligence services, such as machine learning services toenhance, among other things, handling of configuration parameters, suchas ingestion parameters, data weights and the like that impactoperations of the pipeline stages. These intelligence services may beprovided by an intelligence data layer of an enterprise and/or platformwith which the IDL is associated. In a converged transactions platformembodiment, configured intelligence services, such as those providedthrough an intelligence service controller and/or adapted artificialintelligence modules and the like may provide access to a wide range ofintelligence and learning capabilities. Therefore, machine learning26124 may be more fully described by and embody aspects of suchconfigured intelligence services (e.g., as may be provided by aconverged transactions platform, a value chain network platform, and thelike). In an example, a machine learning/feedback system 26228 mayfacilitate processing feedback, such as results of deriving intelligencevia the intelligence stage 26216, data analysis outcomes via theanalysis stage 26214, ingestion processing (e.g., data parsing and thelike) via the ingestion stage 26212 and the like. In an example ofmachine learning-enabled feedback utilization, a set of consumptionparameters (e.g., including a minimum window of time after ingestion ofdata from a data source 26202, channel 26218, or on-demand source 26220)may be adapted based on learning from outcomes of intelligence derivedfrom the ingested data. The feedback may facilitate identifying animpact on the derived intelligence based on an amount of time since lastingestion from the data source. A machine learning system may train theintelligent data layer control tower 26224 ingestion processing controlalgorithm(s) to, for example, adjust (e.g., increase) the minimum windowof time between ingestion events from a data source based on a degree ofchange in intelligence derived from data ingested from the data source.This learning may reduce ingestion events, ingestion frequency and thelike, which can lead to reduced operation costs, while maintaining atleast a minimum level of confidence in the derived intelligence. Thisinformation may be relayed to a corresponding consumer, such as consumerX 26206 where ingestion frequency information may be used to furtheroptimize or benefit use of the derived intelligence.

In example embodiments, intelligent data layers may include or beassociated with a comprehensive data collection and handling system thatmay be configured with dozens (hundreds, thousands, millions) of probesdeployed in data networks, at data sources, listening on many contentchannels. Each probe may be tunable to “hear” certain types of content,specific content, variances in content, occurrence of events, and thelike. In example embodiments, a set of probes may be configured(individually or in groups) to monitor a plurality of news sources forbreaking news, such as financial news and the like that may indicate oneor more changes to intelligence provided by one or more data layers thatrelies on financial news, for example. Each probe may individually, orin groups signal one or more intelligent data layer control towers toactivate ingestion actions. In example embodiments, intelligent datalayer probes may listen to data sources, data users (e.g.,consumers/subscribers of data layer intelligence outputs), markets,transactions, and the like.

Referring to FIG. 262 , an embodiment of an intelligent data layercombined with remotely deployed intelligence data layer probes isdepicted. In general, remotely deployed probes may facilitate dynamicon-demand operation of one or more intelligence data layer functions.Further in embodiments, the intelligence data layer system of FIG. 262may be embodied as a distributed intelligence data layer whereoperations, such as ingestion, analysis, and intelligence gathering maybe disposed at a plurality of locations, such as at sources of data, atintermediate network components, such as edge computing, on one or moremobile intelligent data layer systems, and the like.

Intelligence data layer pipeline 26304 may include one or more dataprocessing devices, processors, functions, algorithms, and the like asmay be described herein for performing, among other things, ingestionfrom data sources, analysis of ingested source data, and intelligencederivation. Although not described in detail for this exemplaryembodiment, aspects of the intelligence data layer pipeline 26304 may besubstantially similar to comparable aspects in embodiments herein, suchas without limitation the ingestion, analysis, and intelligence servicesof the enterprise intelligent data layer of FIG. 261 , the ingestion,analysis, and intelligence services of the unaffiliated intelligent datalayer of FIG. 260 , and similar facilities and services of the exemplaryintelligent data layer of FIG. 259 .

Intelligence data layer probes, such as source probes 26310 may beconfigured to monitor aspects of sources of data, such as data setstates (e.g., for updates and/or other changes or transactionsassociated with such data sets), data producing or modifying activitiesof a data source, such as business workflows, transaction systems, andthe like, data access factors, such as changes to requirements (e.g.,authorization) for accessing one or more sources of data, time-relatedtriggers for data sources (e.g., early release of an update, delayedrelease of an update, an announcement of new sources, and the like). Ina further example of source probes 26310, a probe may be configured tomonitor transaction activity in a marketplace against a threshold(transaction counts, rates, value, assets, and the like). When themonitored activity detected by the probe exceeds (or fails to meet) athreshold, one or more corresponding intelligent data layers maybeactivated to take an action, such as ingesting data, marking data asout-of-date, and the like.

In example embodiments, source probes 26310 may be configured toaggregate probe monitoring results. As an example, a plurality of sourceprobes 26310 deployed to monitor traffic within a city or other regionmay collaborate to enable compound trigger conditions, such as to noticechanges in traffic patterns that indicate changes in commuting activity.This may include one or more of the probes communicating together todetermine that, due to an unmonitored activity (e.g., a traffic jam dueto a sink hold), traffic counts in certain roadways are different thantypical, for example. In another example, a plurality of source probesmay be configured to monitor smart contracts associated with a productor service. These source probes may be deployed with or as part of theproduct or service and therefore may be dispersed across a geographicregion (e.g., across a target market for the product/service). Althoughthese probes may be disparately distributed, the probes may beconfigured/adapted to aggregate monitoring activity and provide one ormore signals to one or more intelligent data layers when the aggregatedmonitoring indicates a need for action, such as ingestion of data fromsources associated with the probes.

In example embodiments, social media probes 26316 may be configured tomonitor a variety of social media-centric data, activities, events, andthe like. In example embodiments, a social media probe 26316 may bedeployed to monitor activity associated with a new product release. Asocial media probe 26316 may be deployed by a social mediacreator/influencer to monitor for mentions of his/her name or otheridentity based on a set of criteria, such as mentions in associationwith a topic of interest to the creator.

One or more intelligent data layer monitoring probes may be associatedwith consumption parameters 26322. Such parameter probe 26320 may beconfigured and/or adapted to monitor consumption parameter activity,such as to detect, for example, changes in one or more consumptionparameters. In embodiments, data consumers may indicate, such as throughconfiguration of the consumption parameters 26322 and the like specificchannel(s) of data from which, for example intelligence is desired, orfrom which data is required for processing in one or more of theintelligent data layer processing pipeline stages based on, for example,configuration data for a consumer-specific instance of the intelligentdata layer. A consumption parameter probe 26320 may detect such consumeractivity and activate one or more processes of the intelligent datalayer accordingly. An intelligent data layer consumer, such as dataconsumer X may indicate that a channel that delivers a stream oftransactions within or for an institution or marketplace as a channelsource of data from which or in association with which the data consumerdesires derived intelligence. As an example, a buyer associated with atransaction marketplace, may desire to be informed, such as through useof the methods and systems of intelligence data layers described herein,of intelligence to be derived from a stream of transaction outcomesprovided on a secondary marketplace channel (that may include activationof a channel probe). In this example, an intelligent data layer controltower 26328 may respond to a trigger or other indication by theparameters probe 26320 by processing consumption parameters 26322 toconfigure a schedule for listening to secondary market transactionoutcomes. The consumption parameters for consumer X may, in this examplealso define one or more of ingestion and/or analysis, and/or derivedintelligence algorithms and/or processes to be applied when processingthose outcomes along the pipeline (as streamed, in batch or the like asmay be specified in, for example, the consumption parameters 26322 forconsumer X) via ingestion, analysis, and intelligence derivation. Inembodiments, data channels 26312 may also publish data according to apublication schedule. An intelligent data layer control tower 26328 maycoordinate the consumption parameters 26322 with each channel'spublication schedule so that the ingestion stage connects with a channelthat corresponds to the consumption parameters 26322 contemporaneouswith the scheduled publication time as may be influenced by a channelprobe 26326. In example embodiments, a consumption and parameter probe26320 may monitor an impact of activity by a machine learning andfeedback facility 26324 that may provide feedback, such as suggestedchanges in consumption parameters and the like.

In example embodiments, consumption probes 26306 may be configured foreach of a plurality of intelligence data layer consumers 26308. Consumerprobes 26306 may be configured with and/or integrated into one or moreaspects of a consumer system, such as an interface between the consumer26308 and the intelligent data layer 26304. A consumer probe 26306 maymonitor, for example, consumer interactions with and uses of datasourced from one or more intelligent data layers. In exampleembodiments, a single consumer probe 26306 may be configured to notify aplurality of intelligent data layers when certain consumer activity isobserved, such as when a consumer accesses data downloaded from theintelligent data layer. A single consumer probe 26306 may be configuredwith a plurality of monitoring settings to facilitate monitoring aplurality of conditions at a consumer that may be of interest to one ormore intelligent data layers. In an example, a first intelligent datalayer may provide intelligence and data to a first consumer associatedwith the first consumer's role as a purchaser in a marketplace. A secondintelligent data layer may provide intelligence and/or data to the firstconsumer associated with the first consumer's role as a seller in asecond marketplace. A single consumer probe 26306 may be configured tomonitor the first consumer's activities in association with contentprovided by both the first and second intelligent data layers. Thesingle consumer probe 26306 may signal to the first intelligent datalayer based on a first set of monitoring conditions associated therewithand may signal to the second data layer based on a second set ofmonitoring conditions associated therewith.

In example embodiments, intelligent data layer probes may be configured,activated, deactivated, adapted, and the like through actions of anintelligent data layer control capability such as the intelligent datalayer control tower 26328, such as control tower 26224, 26120, 26012described herein. Actions of an intelligent data layer control tower26328 that impact one or more deployed probes may be activated by one ormore other probes. In example embodiments, a social media probe 26316may identify activity (e.g., negative reviews) associated with a product(e.g., a health care device) from a particular source (e.g., devicemanufacturer) in a pool of sources 26302. The identified activity maycause the social media probe 26316 to activate a control tower of acorresponding intelligent data layer to take an action. The controltower 26328 may determine that one such action may include activating aprobe that monitors one or more data sources (e.g., purchasing,shipments, customer support activity, and the like) of the devicemanufacturer. In example embodiments, the control tower may respond tothe identified activity by adapting an ingestion scope associated withthe corresponding source of data, such as adapting a schedule ofingestion, and the like.

In example embodiments, probes may be activated, distributed,reconfigured, aggregated, triggered for monitoring, and the like basedon other activities of the intelligent data layer, such as correspondingingestion schedules, changes in consumption requirements, parameters,based on machine-learning enabled feedback and the like. As an example,a change in consumption parameters for a source of data to which asource probe is deployed may cause a change in, for example monitoringthreshold for data elements being monitored by the source probe. Aconsumption parameter of a user of the intelligent data layer maydeemphasize content coming from a specific source. A source probeassociated with the specific source may be reconfigured to monitor forchanges that impact a higher percentage (e.g., 20%) of the source dataas compared to detecting changes to as little as 5% of the source dataprior to the changes in the consumption parameters to deemphasize thesource. In another example of operations of the intelligent data layerimpacting probe configuration, machine learning may recommend monitoringfor changes on a more frequent basis than the frequency of currentmonitoring. An intelligent data layer control tower may adjust adeployed probe, and/or deploy a replacement probe to perform monitoringmore frequently. I this example, a second probe may be deployed thatmonitors data at the same rate as the first probe but on a differentcycle, thereby effectively doubling the monitoring rate. The first andsecond probes may further be adapted to aggregate their results whendetermining if an intelligent data layer activation threshold is met.

In example embodiments, a source of data may set a price for use of dataprovided from the source. Pricing of source data may be a factor thatsource probes may be configured to monitor. An intelligent data layercontrol tower may configure a source probe to monitor a price for thedata that may trigger ingestion of data from the source. In exampleembodiments, the source probe may be configured with a compound set ofmonitoring criteria, such as a target price and an availability windowof time that matches one or more consumer criteria of the intelligentdata layer.

Operation of one or more intelligent data layers may include and/or bebased at least in part on an understanding of relative values of aspectsof source data to both a data source provider and an intelligent datalayer output consumer. A data producer, such as a marketplacetransaction platform, may assign a high value to format of certaincontent being produced, such as using a streaming format for transactiondata. An intelligent data layer consumer may choose to obfuscate theformat, focusing instead on timing of certain types of data beingproduced by the marketplace transaction platform, such as for detectingtrading rates and the like. A data producer (e.g., a data source asdescribed herein) may deem that timeliness of data delivery has asubstantive impact on a value (e.g., a cost to access the data). Forexample, data that represents recent activities of the marketplacetransaction platform may be priced higher than data representing aged(e.g., historic) activity. A consumer may deem that recent marketactivity may be less valuable due to the market's dynamic nature,whereas data from prior transaction sessions, now fully settled forexample, may represent more stable and therefore more valuable.

The value mapping structure 26402 embodied in FIG. 263 may facilitatedeveloping and/or documenting such understanding of value to producersand consumers. A producer 26404 may consider aspects of data beingproduced, such as data format(s) 26406, data content 26408, a meaning ofthe content 26410, a cost of the data 26412, and the like. A consumer26414 may consider aspects of data used by an intelligent data layer toprovide data ingestion, analysis, and intelligence services for theconsumer, such as a value of the data 26416, format(s) of the data26418, timing of the data 26420, and meaning of the data 26422, and thelike. Intersections indicated in FIG. 263 between producer aspects andconsumer aspects may be populated with one or more values, algorithms,functions, references and the like (e.g., intersection content). Suchintersection content may be embodied into one or more functions of acorresponding intelligent data layer. As an example, an intersection ofa producer meaning 26410 with a consumer value 26416 may enable applyinga consumer's perception of value to one or more meanings of data thatmay be defined by the producer. In this example, a producer may define ameaning of a set of source data to mean an error rate in surgicalprocedures that require re-admittance. A consumer, such as an insurermay determine that data with this meaning imputes a high value to theconsumer, such as for setting reimbursement to a facility. A higher rateof error that results in readmittance (e.g., with 48 hours and/or basedon a determination that the surgical error prompted the readmittance)may be used by the intelligent data layer consumer to withholdreimbursements for certain surgical procedures for a time period thatexceeds a likelihood of a patient returning to the hospital due to theerror (e.g., at least 48 hours post-surgery). The withhold threshold maybe configured into a source probe at the surgical facility that monitorsadmittances so that when a readmittance occurs, the intelligent datalayer may be activated to process the readmittance data for the insurer.

Another example of mapping producer perception of aspect value toconsumer valuation may include ascribing a consumer meaning 26422 for acost of collection 26412 required by a producer. In example embodiments,such a meaning may be within a context of understanding of the consumer.In this example, a consumer may deem that a low cost of collection forcertain data maps to a higher tactical/decision meaning than that samelow collection cost data means for long-term adjustment to ongoingoperations. In another example, a meaning of certain data to a consumermay suggest that the producer demand a higher value than would otherwisebe acceptable based on the producer's collection cost of the data. Inthis example, an intelligent data layer control tower may implement theconsumer's meaning as requiring frequent updates by ingesting data fromthe source frequently. The frequent updates may result in relativelysmall changes to ingested content. In this context, the intelligent datalayer control tower may negotiate a lower cost for each ingestion withthe data producer due to the small amount of new data being produced.

Yet another example of mapping producer perception of aspects of sourceddata to consumer perception may include determining a relationshipbetween consumer perceived value 26416 and a producer's collection costs26412. As in the example above, a consumer may highly value certain datafrom a producer that has a relatively low collection cost. In exampleembodiments, a producer may set a price for sourcing data that isinconsistent with a consumer's perceived value thereof. A control towerfor an intelligent data layer may respond to a determination of thisinconsistency by cancelling a scheduled ingestion of the source data,automatically negotiating with the producer for a price that may reflectthe consumer perceived value, seeking other sources of data that providecomparable consumer value at lower price, and the like. The controltower may determine this inconsistency by detecting that aconsumer-provided content in an intersection of producer cost 26412 andconsumer value 26416 reflects this inconsistency. In exampleembodiments, this inconsistency may include detecting producer cost datain the intersection that may be high compared to other collection costscoupled with a consumer value data that may be low compared to otherconsumer value entries for other source data, and the like. Further, aconsumer may provide a function that enables the control tower todetermine this inconsistency by, for example, the consumer may providetarget cost data that would be acceptable for use of the producer data.In this example the consumer may provide a maximum target cost data, atarget cost with an automatic escalation clause (that maybe administeredby a smart contract between the consumer and the intelligent data layerentity), a cost per unit of time (e.g., a maximum amount to be allocatedby the intelligent data layer control tower to use of the source of dataper day, week, month, and the like).

In yet another example of intelligent data layer operation based on amapping of provider and consumer perspectives may include operationbased on a mapping of a provider perspective of available content 26408with a consumer perspective on timing of the content 26420. In thisexample, a consumer may desire access through the intelligent data layerto content, such as content provided by data source X. However, theconsumer may identify a timing of use of the content, such as based on apoint in time (e.g., an upcoming or recently occurred event), based on aduration of time (e.g., content availability must meet a time-framecondition), and the like. When the desired content X is availableoutside of the time constraint, the consumer entity may elect to not usethe content. Further in this example, a consumer entity may configure atrial period for end users to access intelligence that the consumerderives through the intelligent data layer based at least in part oncontent X. When an end user activates a trial, the consumer may signalto the intelligent data layer, such as through adjustment of theconsumer timing parameter 26420 for the source content 26408 to make useof content X during the trial period. Once the trial period expires, theintelligent data layer may pause ingestion of content X until activatedto do so again. In another content-time mapping example, a provider maysignal that content is available for use other than between time X andtime Y (e.g., outside of a blackout period, such as a blackout periodassociated with a live sporting event and the like). The consumer thatis interested in this access time-constrained content may designate in arepresentative map 26402 that the content can be used by the intelligentdata layer for a duration of time (e.g., a business day) commencing attime Y. In this way, only content that is made available by the sourcesoon after the blackout window would be ingested by the intelligent datalayer for use in producing intelligent data layer content (e.g.,intelligence derived therefrom) for the consumer.

In example embodiments, an intelligent data layer control tower may useartificial intelligence functions, such as intelligence services thatmay be provided by a platform (e.g., a transaction marketplace platformand/or system of systems, a value chain network control tower platformand/or system of systems, and the like, to determine a set of operatingcriteria for each of a plurality of users (e.g., consumer entities) ofthe intelligent data layer based on analysis of the mapped producer andconsumer parameters of parameter map 26402. In example embodiments, anintelligent data layer control tower may have access to a plurality ofsuch maps 26402. As an example, each consumer of the intelligence datalayer may be associated with a map.

In example embodiments, an intelligent data layer may learn (e.g.,through use of machine learning and the like) configurations of MAP26402 that may be valuable to one or more candidate consumers. Learningmay be based on a plurality of consumer-configured maps 26402. Learningmay be based on consumer utilization of data sources, optionallycombined with consumer configuration parameters, such as consumptionparameters 26322 and the like. The intelligent data layer control towermay speculatively configure the intelligent data layer to produceoutputs (e.g., intelligence and the like) based on the learnings ofconsumer use and mapping 26402 and may offer/market/publish set of databased on the speculative configuration. In example embodiments, anintelligent data layer control tower and/or intelligent data layerentity may publish/market/advertise/offer one or more learnedconfigurations for use by other intelligent data layer entities, such asthrough a licensing scheme and the like.

Intelligent data layers may be configured to operate in cooperation withenterprise systems. In example embodiments, enterprise systems mayinclude a plurality of modules, such as for distinct departments,subsidiaries, and the like that may benefit from intelligent exchange ofinformation. An intelligent data layer may facilitate informationexchange combined with at least entity-specific intelligence that mayimprove utility and value of information gathered and/or used in suchmodules. In example embodiments, one or more such modules may includedistinct processing systems, locally and/or remotely deployed, andcommunicating through one or more networks, such as an intranet, aninternet, and the like. In example embodiments, an enterprise mayinclude a networked chain of value-contributing entities, such asparticipants in a value chain network and the like. In exampleembodiments, a plurality of intelligent data layers may be configuredfor an entity to achieve one or more objectives of information sharing.

Referring to FIG. 264 , embodiments of methods and systems forintelligent data layers implementing entity data-centric strategies aredepicted. Enterprises, such as businesses, government agencies,educational institutions, religious institutions, networks of entities,and the like may include a plurality of participants in a data-centricstrategy 26510 for the enterprise. Data centric strategy participantsmay include a range of sub-entities, divisions, departments, bureaus,subsidiaries, locations, franchises, and the like. For simplicity in theexemplary embodiments of FIG. 264 , a department 26502 is depicted torepresent any and all such participants. While a department of anenterprise may generally be thought of as integral to the enterprise, adepartment 26502 may not be so constrained; a department 26502 be aseparate enterprise in one or more potentially loose and/or transientassociations with an enterprise for which the department 26502 is aparticipant in a data-centric strategy 26510 for the enterprise.Examples may include, two competitive enterprises that have optionallyentered an agreement for information sharing associated with a thirdcompetitor, a new market, international relations, and the like. Adepartment, such as department 26502 may be distinguished for entitydata-centric strategy purposes from external sources merely based on adegree of participation in the strategy. As an example, external sourcesof data that may provide information, such as external intelligent datalayers and the like, that is useful for achieving a successfuldata-centric strategy, may usefully be differentiated from a department26502.

In the embodiments of FIG. 264 , a department 26502 may receive datafrom one or more sources, including enterprise-internal sources andother sources. A department 26502 may subscribe to and/or receiveinformation from one or more external intelligence data layers 26504. Adepartment 26502 may be a consumer of information (e.g., data andderived/related intelligence) from the external intelligent data layer26504. The department 26502 may be a producer of data for one or moreexternal intelligent data layers. Sources of data for a department 26502may include any such sources disclosed herein, including, for example,one or more data feeds 26506. The department 26502 may participate in anentity data-centric strategy 26510 via one or more internal intelligentdata layers through which data, intelligence, and the like may beexchanged. As an example of an internal intelligent data layer between adepartment 26502 and an entity data-centric strategy 26510, a departmentmay publish content for the strategy that is processed with an inputintelligent data layer 26508. The internal intelligent data layer 26508may be embodied as and/or may include any functionality of any of theintelligent data layers described herein. In an example, an inputintelligent data layer 26508 may operate with the department 26502 as adata source and with the strategy 26510 as a consumer thereof.

An example of an internal intelligent data layer between the department26502 and the strategy 26510 may include an output intelligent datalayer 26514 that may operate with the enterprise strategy 26510 as asource of data and the department 26502 as a consumer of the layer26514.

While a single department 26502 and singular input and outputintelligent data layers 26508 and 26514 respectively are depicted in theembodiments of FIG. 264 , any number of departments, and any number ofinput and output intelligent data layers may be configured for achievingan entity data-centric strategy 26510. A single department may generatea plurality of different types of data that may be useful to the entitystrategy and processed through a plurality of distinct intelligent datalayers. Likewise, a department may consume data from a plurality ofintelligent data layers as may be configured for publishing dataassociated with the strategy. Further although depicted as distinctinput and output intelligent data layers, any data layer may operatebidirectionally. Each intelligent data layer, such as 26508 and 26514may be configured to process and/or provide compound layer intelligenceand the like.

A data-centric strategy 26510 may be configured to handle data sharingneeds for an enterprise. The data-centric strategy 26510 may includesubsets of data associated with operations of the enterprise that arestored locally, such as in the localized data store 26516. A localizeddata store 26516 may be configured as a single storage facility, a setof distributed storage facilities distributed throughout theorganization and connected physically and/or logically through one ormore networks, such as an internet, an intranet, and the like. A datacentric strategy 26510 may further interface with cloud-based datastores 26512, such as to store data useful and/or pertinent to operationof workflows of the enterprise, including data and intelligence capturedfrom external sources through one or more intelligent data layers, dataand intelligence generated in the course of executing workflows of oneor more portions of the enterprise, such as the department 26502, dataand intelligence produced through one or more intelligent data layers ofthe enterprise and the like. In example embodiments, a data-centricstrategy service of an enterprise may include a cloud-based data storemanagement capability that, among other things, maintains freshness ofdata and/or intelligence that may be used by one or more portions of theenterprise, such as for performing one or more workflows, and the like.In an example, a set of intelligence content that may be stored in thecloud-based data store 26512 may include strategic pricing predictionsfor the enterprise. These pricing predictions may be dependent on arange of enterprise-internal data as well as external information, suchas fuel costs, shipping costs, currency exchange rates, and the like. Inthis example the data-centric strategy service may maintain a currencyof such pricing prediction intelligence by capturing, such as throughintelligent data layers, relevant content including, without limitation,current fuel costs, light crude futures, regional fuel costs, shippingcapacity and demand data, shipping costs from contracts with shippers,and the like.

The specifics of how an organization chooses to locally store data mayinform structural constraints of one or more intelligent data layers ofthe enterprise. As an example, an intelligent data layer that accesseslocally stored data associated with enterprise from a plurality ofdistributed data stores may include a plurality of ingestion servicesthat may be tuned to retrieve data from distinct data sources. Inexample embodiments, ingestion services of intelligent data layers thatwork cooperative to provide a data-centric strategy for an enterprisemay be configured and operated similarly to ingestion services ofintelligent data layers described elsewhere herein, such as ingestionservices of intelligent data layer 26304 of the exemplary probe-enabledintelligent data layer of FIG. 262 , ingestion facility 26212 of theexemplary strategic approach for an enterprise of FIG. 261 , ingestionsystem 26104 of the exemplary independent data layer of 260, ingestionfunction 26004 of exemplary intelligent data layer architecture of FIG.259 , and the like.

Each intelligent data layer of an architecture to provide data sharingfor achieving a data-centric strategy for an enterprise may beconfigured with a control tower configured to operate the correspondingdata layer. An enterprise may be configured with one or moreinterconnected control towers (not depicted in FIG. 264 ) thatfacilitate control of one or more of the intelligent data layers, suchas by coordinating operation of the distinct intelligent data layercontrol towers and/or by controlling one or more of the intelligent datalayers independent of a presence of a control tower for a correspondingintelligent data layer.

In the exemplary embodiments of FIG. 264 , a data-centric strategy 26510may employ one or more external intelligent data layer handlingfacilities 26518 for facilitating use of external intelligent datalayers, such as layers that may provide data and/or intelligence basedon data from sets of sources that are external to the enterprise. As anexample, an industry consortium may operate one or more intelligent datalayers that offer industry-impacting data and intelligence to consortiummembers, and the like. Such an external intelligent data layer maysupport customized data and/or intelligence production upon request. Theexternal intelligence data layer controller 26518 may adapt requests26522 that may satisfy one or more data/intelligence needs of thestrategy 26510 to configure an external intelligent data layer-specificrequest.

The external intelligent data layer controller 26518 may be configuredto handle a plurality of differently configured external intelligentdata layers. Such a plurality of external data layers 26520 may bedepicted in FIG. 264 as IDL-EX, IDL-EY, and IDL-EZ. External intelligentdata layer IDL-EX may provide data/intelligence based on operations of avalue chain network to which the enterprise may be a participant.External intelligent data layer IDL-EY may provide intelligence onconsumer buying trends for one or more products/services of theenterprise. External intelligent data layer IDL-EZ may providedata/intelligence on marketplace transactions that may carry products ofthe enterprise and/or products that are similar to and/or compete withproducts of the enterprise.

The external intelligent data layer controller 26518 may provide aprogrammatic interface between external intelligent data layers 26520and the enterprise strategy 26510, to facilitate, among other things,consolidation of external data layer data/intelligence into a single,optionally composite enterprise input intelligent data layer IDL-EI. Thecontroller 26518 may be configured to make a portion of the plurality ofexternal intelligent data layers to appear to the enterprise as a singleintelligent data layer, optionally with composite and/or compoundoperation. As an example of this capability of the controller 26518, theenterprise strategy may form a request 26522 for data that may not bedirectly available from a single external intelligent data layer. Thecontroller 26518 may identify relevant potential external sources tosatisfy the request 26518, such as a combination of two externalintelligent data layers 26520. In this example, the controller may parsethe request, thereby revealing that the request includes a first type ordomain of data/intelligence (e.g., operations of a value chain network)that may be provided by a first external intelligent data layer (e.g.,IDL-EX). Parsing the request may further reveal that a second type ordomain of data in the request (e.g., consumer buying trends for one ormore products/services of the enterprise) may be provided by a secondexternal intelligent data layer (e.g., IDL-EY). In example embodiments,the controller 26518 may establish a consumer-type relationship with thetwo external intelligent data layers to receive data and/or intelligencethat may satisfy at least the two types of data in the request. Thecontroller may further make at least a portion of the information fromthe two external intelligent data layers available for use in achievingthe entity data-centric strategy 26510. The controller may make theinformation available by consolidating information it consumes from thetwo external intelligent data layers into a consolidated data set foruse by the entity. The controller 26518 may configure the informationconsumed from the two external intelligent data layers into a compoundintelligent data layer for consumption and use in the data-centricstrategy 26510.

Referring to FIG. 265 , a configuration of intelligent data layersforming a set of networked data sharing interfaces among a plurality ofsystems, internet-of-things devices, and the like is depicted.Intelligent data layers may be configured with interfaces thatfacilitate sharing of data among entities to achieve a wide variety ofdata sharing services and capabilities. Intelligent data layers may beinterfaced with each other to form intelligent networks and/or contentchannels with one or more physical networks that provide not only rawdata transfer capabilities, but also provide delivery and sharing ofcontent and intelligence arranged for a specific consumer, need, orother criteria.

Data sources, such as internet-of-things devices may have limitedprocessing capacity, and or may be configured for purpose-specificoperation (e.g., a data sensor and the like). While the informationprovided by these devices may be rich in a context of its deployment,without the context the information may be less valuable. As an example,a data sensor that puts out a stream of temperature readings may providevaluable and accurate temperature information. However, by itself, thisinformation may be hard to appreciate. Such as what is the temperatureinformation indicative of? Two otherwise identical engines may producesubstantively different core lubricant temperatures, such as based on acontext of the deployment of the engine. One of the engines may bedeployed in an environmentally protective box on a line pole in theCaribbean (thereby indicating a temperature at or near the maximumpermitted) and the other may be operating above the arctic circle inwinter (thereby indicating a temperature well below the maximum). Merelyproviding raw temperature sensor data would likely not be sufficient forderiving much intelligence about the engine. However, when the sensedengine temperature is combined with, for example sensed ambienttemperature, the resulting intelligence value may be high. Byinterfacing intelligent data layers, such as exemplarily depicted inFIG. 265 , a richness of knowledge and intelligence may result,including increasing available information through combined intelligenceservices.

The networked intelligent data layers depicted in FIG. 265 facilitateintelligent data sharing among a first IoT device IoTY 26602, a secondIoT device IOTZ 26604, a first system, system Z 26606, and a secondsystem, system A 26608. In example embodiments, the networkedarchitecture of FIG. 265 may facilitate transfer of intelligence fromthe two IoT devices to a first system 26606 and further, optionallyincorporating intelligence and/or data produced by the first system26606 into a set of intelligent data layers consumed by the secondsystem 26608.

Each of IoTZ and IoTY may combine inputs, such as inputs A and B forIoTY and inputs C and D for IoTX to each produce a pair of intelligentdata layers, IDL-YA and IDL-YB, for IoTY and IDL-XC and IDL-XD for IoTX.Each of these four intelligent data layers may be combined in pairs toproduce composite IoT intelligent data layers, IDL-IoTY for IoTY andIDL-IoTX for IoTX. Yet further, intelligent data layer IDL-IoTXY may beformed from outputs of intelligent data layers IDL-IoTX and IDL-IoTY. Inexample embodiments, any of these data layers may operate substantiallysimilar to intelligent data layers described herein. As an example,intelligent data layer IDL-IoTX may provide one or more set of outputs,including data, intelligence and the like derived at least in part frominformation produced by intelligent data layers IDL-XC and IDL-XD.Intelligent data layers IDL-XC and IDL-XD may provide data and/orintelligence based on IoTX inputs C and D respectively. In an example,input C may monitor bidding activity for a marketplace including pricingof bids. Intelligent data layer IDL-XC may ingest and analyze themonitored bidding activity, and further may provide intelligence basedon, for example changes in the monitored bidding activity. Input D maymonitor settlement activity for completed transactions in themarketplace. Intelligent data layer IDL-XD may ingest and analyze themonitored settlement activity, and further may provide intelligencebased on, for example trends in settlement terms. Intelligent data layerIDL-IoTX may ingest the bidding activity change intelligence from IDL-XCalong with settlement terms trends from IDL-XD, (and optionally rawand/or analyzed source bidding activity and settlement terms from acorresponding intelligent data layer) to analyze these inputs anddeliver intelligence, for example regarding relative impacts of changesin bidding activity on settlement terms as one of one or more outputs ofIDL-IoTX.

Monitored information A and B of IoTY may be processed by intelligentdata layers IDL-YA and IDL-YB. Outputs from these intelligent datalayers may be further ingested and analyzed to produce at leastintelligence based thereon by intelligent data layer IDL-IoTY.

A first joining intelligent data layer ILD-IoTXY in FIG. 265 may consumecontent from intelligent data layers IDL-IoTX and/or ILD-IoTY anddeliver at least derived intelligence for consumption by system Z 26606.As an example, system Z may perform regulatory compliance validation formarketplaces being monitored by IoTY and IoTX. IDL-IoTXY may provideintelligence, raw transaction data, and/or analyzed transaction,marketplace, and financial data for a plurality of transactions in themonitored marketplace. System Z 26606 may apply transaction validationrules, such as rules derived from inputs E and F to generate a pluralityof types of data, optionally as a set of intelligent data layersincluding IDL-ZE, IDL-ZF, and IDL-Z. In this example, system Z 26606 mayproduce intelligent data layer IDL-ZE that provides at leastintelligence based on marketplace and/or transaction data derived fromintelligent data layer IDL-IoTXY and input E. Likewise intelligent datalayer IDL-ZF may provide raw and/or analyzed data and/or intelligencebased on validation source data F and content from intelligent datalayer IDL-IoTXY. System Z 26606 may further produce an intelligent datalayer IDL-Z from native data sources, internal operations, inputs (e.g.,E and/or F) and the like. Further, not all potential sources of data foruse by system Z 26606 are depicted; however, other sources, includinginternal, external, and the like are contemplated as aspects of theembodiments of at least FIG. 265 .

Further in the embodiments of FIG. 265 , intelligent data layer IoTXYZmay be formed to facilitate access by other entities to data, and/orintelligence derived from one or more of IoT device IoTY 26602, deviceIoTX 26604, and system Z 26606 via intelligent data layer IDL-Z. Inexample embodiments, other entities that may consume intelligence andthe like from intelligent data layer IoTXYZ may include system A 26608.In example embodiments, system A 26608 may further consume data and/orintelligence associated at least with system Z 26606 via intelligentdata layer IDL-Z.

In example embodiments, system Z 26608 may ingest content from source Gas well as one or both of intelligent data layers IDL-Z, and IDL-IoTXYZ.In the embodiments of FIG. ILD-8, system A 26608 may produce a firstintelligent data layer IDL-AG that may be based on information consumedfrom source G. System A may also produce a second intelligent data layerIDL-AZG that may provide data and/or intelligence derived from source Gand one or more of intelligent data layers IDL-Z and IDL-IoTXYZ.

The network of intelligent data layers depicted in FIG. 265 mayfacilitate access to intelligence provided by system A 26608 (e.g.,through intelligent data layer IDL-AZG) that may take into considerationdata and or intelligence derived throughout the network and being basedon one or more of inputs E and F to system Z, inputs A and B to IoTY,and inputs C and D to IoTX.

Intelligent data layer architectures may include cloud-based variants.Exemplary embodiments of cloud-based intelligent data layers aredepicted in FIG. 266 . A cloud-based intelligent data layer may beembodied as an accessible service, such as a service available to thepublic for accessing intelligence from a range of data sources. Inembodiments, the cloud-based intelligent data layer 26700 embodiment ofFIG. 266 may operate independently to provide intelligence determinationservices for data consumers. This intelligent data layer may be providedas a service, (e.g., hired/rented/utilized) by a plurality ofindependent data consumers, such as through payment of a subscriptionfee, one-time use fee, and the like. In embodiments, the cloud-basedintelligence data layer 26700 depicts a distributed set of entities forproducing data for a plurality of data consumers. A micro-servicearchitecture, such as described herein and elsewhere, may further enableisolated and independent processing throughout the layer operatingpipeline for each consumer, such as by initiating a virtualizedcontainer to perform one or more of the intelligent data layer pipelinefunctions for each data consumer (e.g., consumer X, Y, Z). In anexample, a virtualized container may be operated (e.g., on a cloudprocessing architecture that has low latency access to data beingprocessed in the container). In embodiments, low latency access mayinclude, without limitation, local access, such as a data processingserver in a networked data storage facility and the like. A virtualizedcontainer may be configured with a consumer-specific instance of theingestion server 26704. In this example, the consumer-specific instanceof the ingestion server 26704 may be configured with consumer-specificingestion parameters and/or functions, so as to, for example, listen tocertain source data channels 26710 designated and/or selected whenconfiguring the ingestion server instance for the consumer. Inembodiments, an intelligence analysis server 26708 of an intelligentdata layer pipeline of this intelligent data layer 26700 may beinstantiated in, for example, a virtualized container environment. Theinstance may be configured with intelligence derivation algorithmsassociated with a specific consumer, such as data consumer Y 26720.

While data consumer-specific instances of pipeline services aredescribed as possible embodiments for the cloud-based intelligent datalayer 26700, other architectures are possible and contemplated herein.One such architecture includes abstracting (e.g., through use ofvirtualized containers, and the like) use of pipeline server functionsthat operate on one or more physical, logical, and/or virtual servers.In this example architecture, a core pipeline service may operate on aserver with data for a plurality of data consumers being stored in alow-latency data storage facility. In this example embodiment,virtualization facilitates on-demand access to the computingcapabilities of the server and more specifically to the computingcapabilities and functions of a corresponding pipeline server, whileisolating input data, in-process data, configuration data, andintelligence outcomes so that each consumer appears to have full accessto the intelligent data layer based on their needs.

In yet another exemplary embodiment, a plurality of functions of theintelligent data layer may be instantiated within or associated with avirtualized container environment that may be dedicated to providingintelligence services to a specific data consumer or set of dataconsumers. In this way, ingestion, analysis, intelligence, controltower, storage, and publishing (e.g., producing a data and/orintelligence feed for the specific consumer) may be logically configuredwithin a virtualized environment for providing intelligent data layerservices independently of other consumers.

The embodiment of FIG. 266 may be differentiated from other embodiments,such as embodiments where an intelligent data layer is integrated into adata consumer (or optionally a data supplier) computing environment,such as embodiments depicted in FIGS. 266 and 261 .

Data layer processing elements, such as ingestion server 26704, analysisserver 26706, and intelligence derivation server 26708 may, for purposesof disclosure efficiency, be substantially, although not exhaustively,as described in corresponding elements 26004, 26006, and 26008 from FIG.259 respectively. Further, some features of a corresponding stage inFIG. 259 may, in embodiments, be configured differently or excluded froma corresponding server in FIG. 266 . As an example, the ingestion stage26004 may include data conversion capabilities that may be excluded fromembodiments of the ingestion server 26704, at least for instances wherethose capabilities are not needed, such as when an instance of theingestion server 26704 is ingesting data from a source for which atleast some types of data conversion are not required.

In embodiments, ingestion server 26704 may, in addition to interfacingwith data sources 26702 (that may be, for purpose of compact disclosure,substantially, although not exhaustively, as described in correspondingelement 26002 from FIG. 259 ) may further interface with data channels26710 and on-demand data sources 26712. The data channels 26710 may beserviced by an ingestion server, using, for example, a channel listeningfunction that may be controlled by and/or integrated with intelligentdata layer control tower 26714. In embodiments, data consumers mayindicate, such as through configuration of the consumption parameters26716 and the like specific channel(s) of data from which, for exampleintelligence is desired, or from which data is required for processingin one or more of the intelligent data layer processing pipelineoperations based on, for example, configuration data for aconsumer-specific instance of the intelligent data layer. A dataconsumer, such as data consumer X 26718 may indicate that a channel thatdelivers a stream of transactions within or for an institution ormarketplace as a channel source of data from which or in associationwith which the data consumer desires derived intelligence. As anexample, a buyer associated with a transaction marketplace, may desireto be informed, such as through use of the methods and systems ofintelligence data layers described herein, of intelligence to be derivedfrom a stream of transaction outcomes provided on a secondarymarketplace channel. In this example, the intelligent data layer controltower 26714 may process consumption parameters 26716 to configure aschedule for listening to secondary market transaction outcomes. Theconsumption parameters for consumer X may, in this example also defineone or more of ingestion and/or analysis, and/or derived intelligencealgorithms and/or processes to be applied when processing those outcomesalong the pipeline (as streamed, in batch or the like as may bespecified in, for example, the consumption parameters 26716 for consumerX 26718) via the ingestion server 26704, the analysis server 26706, andthe intelligence server 26708. In embodiments, data channels 26710 mayalso publish data according to a publication schedule. The intelligentdata layer control tower 26714 may coordinate the consumption parameters26716 with each channel's publication schedule so that the ingestionserver 26704 connects with a channel that corresponds to the consumptionparameters 26716 contemporaneous with the scheduled publication time. Inan example, an instance of the ingestion server 26704 may be configuredto begin listening for data from a specific channel or channels beforeor at a start of a scheduled publication. Alternatively, the ingestionserver 26704 may be configured and/or activated to begin listening at apoint in time relative to the start of scheduled publication, such asafter a preamble of the publication, at an initiation of publication ofdetailed data values, at or near to an end of publication of detaileddata values, or after a configurable number of publication steps, andthe like.

As noted elsewhere herein intelligence may be derived from sourcecontent, structure, and metadata, among other things. In embodiments,intelligence associated with a data channel 26710 may be derived basedat least in part on the respective channel's publication schedule. Oneexample of intelligence that may be based on a publication scheduleincludes awareness of timing of potential changes in data from thechannel. Therefore, changes in resulting intelligence may be adaptedbased on the schedule, such as indicating that intelligence derivedprior to a new data publication schedule may be deemed to be “aged”(e.g., weighted lower than more updated intelligence and the like).Time-based averaging of data from such a source may be synchronized withits publication schedule.

As noted herein, another potential source of data may include on-demanddata sources 26712. Unlike channels of data, such as data channels 26710that may publish data on a schedule or based on events or the like, anon-demand data source 26712 may be controlled, such as by theintelligent data layer control tower 26714 to generate (e.g., publish ormake available) data when requested. An on-demand data source 26712 mayinclude devices that “sleep” by activating a lower power mode in betweenrequests (demands) for data. While depicted as individual entities, datasources that provide channels 26710 and data sources that provideon-demand data 26712 may not be distinct. A single data source mayprovide a plurality of data interfaces, including in this example anon-demand interface and a publication channel interface.

The cloud-based intelligent data layer 26700 may include a configurationdata storage facility 26716 that may include, among other things,consumption parameter storage for each of a plurality ofclients/consumers/users of the layer 26700, such as consumer X 26718,consumer Y 26720 and/or consumer Z 26722 and the like. In embodiments,layer configuration data for a data consumer may be stored separatelyfrom the parameter storage 26716 and may be accessed through, forexample, a link to the separate configuration data in the parameterstorage 26716. Configuration parameter storage facility 26716 (e.g.,that may be virtualized and the like) may be configured with dataconsumer distinct portions to facilitate isolation between users of thelayer 26700. A type of configuration parameter that may be accessible inor through the configuration parameter storage facility 26716 mayinclude ingestion parameters, such as for facilitate control ofingestion activities by, for example, the intelligent data layer controltower 26714, an instance (e.g., in a virtualized container) of theingestion server 26704 and the like.

The layer configuration storage facility 26716 may be accessed by a dataconsumer of the data layer 26700 through various computer-to-computerprotocols, including networked storage protocols, streaming protocols,indirect access protocols (e.g., through a proxy service that providesaccess to the storage) and the like.

In the exemplary embodiment of FIG. 266 , configuration data may includeinformation that facilitates ingestion (e.g., data sources and ingestioncontrols), analysis (e.g., data source processing, data sourcerelationships, and the like), intelligence (e.g., intelligencealgorithms, and/or identification of third-party intelligence servicesto be used when processing data for the consumer) and the like.

A cloud-based intelligent data layer 26700 may include and/or haveaccess to artificial intelligence services, such as machine learningservices to enhance, among other things, handling of configurationparameters, such as ingestion parameters, data weights and the like thatimpact operations of the pipeline. In embodiments, machine learning26724 may facilitate processing feedback, such as results of derivingintelligence via the intelligence server 26708, data analysis outcomesvia the analysis server 26706, ingestion processing (e.g., data parsingand the like) via the ingestion server 26704 and the like. In an exampleof machine learning-enabled feedback utilization, a set of consumptionparameters (e.g., including a minimum window of time after ingestion ofdata from a data source 26702) may be adapted based on learning fromoutcomes of intelligence derived from the ingested data. The feedbackmay facilitate identifying an impact on the derived intelligence basedon an amount of time since last ingestion from the data source. Amachine learning system may train the intelligent data layer controltower 26714 ingestion processing control algorithm(s) to, for example,adjust (e.g., increase) the minimum window of time between ingestionevents from a data source based on a degree of change in intelligencederived from data ingested from the data source. This learning mayreduce ingestion events, ingestion frequency and the like, which canlead to reduced operation costs, while maintaining at least a minimumlevel of confidence in the derived intelligence. This information may berelayed on to a corresponding consumer, such as consumer X 26718 whereingestion frequency information may be used to further optimize orbenefit use of the derived intelligence.

A cloud-based intelligent data layer architecture, such as architecture26700 may include communicating information, such as sourced data,intelligence algorithms, intermediate results, results from eachpipelined server through a network, such as the Internet. Furtherintelligent data layer controller 26714 may establish secure channelswith and among various other servers of the cloud-based architecturethrough an Internet to facilitate content sharing, operational controlsecurity and the like.

In example embodiments, an ingestion server 26704 may communicatethrough the Internet and/or other public or private network with datasources, such as source devices 26702, source channel servers 26710,on-demand servers 26712, the internet, and the like to perform ingestionof data used to produce intelligence and the like. An analysis server26706 may also communicate through the network, such as the Internet tocapture, analyze and process content output from the ingestion server26704. Intelligence server 26708 may interface with one or more otherservers of the cloud-based intelligent data layer architecture 26700through a network such as the Internet and the like. In exampleembodiments, consumer servers 26718, 26720, and 26722 may be constructedto operate on edge computing servers within the network that areproximal to a home computing system of each of the customers X, Y, and Zrespectively.

In example embodiments of a cloud-based intelligent data layer 26700, acustomer server, such as customer X server 26718 may optionally beconfigured to operate on customer X's home server, such as a server onan enterprise network for the customer X and the like. In this way, theaspects of a cloud-based intelligent data layer may be deployed onnetworked servers that may be proximal to source data and/or consumercomputing devices, storage and the like.

Referring to FIG. 267 , an embodiment of a multi-use intelligent datalayer 25850 that may be used to produce different layer intelligence andcontent for different purposes across a plurality of intelligent datalayer consumers. A multi-use intelligent data layer 25850 may employ anarchitecture that has some expected similarities with other intelligentdata layer architectures described herein, such as a data processing setof stages, referred to herein, in embodiments, as data processingpipeline stages including one or more ingestion stages, one or moreanalysis stages, and one or more intelligence stages. Each such stagemay be embodied as one or more sets of services that may be provided byone or more servers.

To facilitate dynamic multi-tenant use of the intelligent data layer25850, at least a portion of the pipeline stages may be configured toreceive and process data and corresponding parameters for performing itsrespective pipeline data operations. As an example, ingestion server25856 may receive source content 25854 and source parameters 25852. Oneexemplary ingestion processing function includes parsing unstructuredcontent, such as by applying a dictionary for determining relevanceand/or meaning of data received from data sources. For such an exemplaryingestion operation, a set of source content 25850 may be received froma source and a corresponding parsing dictionary may be providedcontemporaneously with the source content, such as in the form of one ormore ingestion parameters 25852. The source parameters, such as aparsing dictionary may be received by the ingestion server 25856 invarious forms, including one or more identifiers of correspondingparameters. As an example, a parsing dictionary for ingesting data fromsource X may be available to the ingestion server 25856 via a link thatmay be provided in association with source content 25854. The linkedparsing dictionary may have been received by the ingestion facility (orvia another interface to the intelligent data layer as may be describedelsewhere), stored for later use and assigned a link to the storeddictionary that is matched to an identifier known to the ingestionserver 25856 to be used for parsing content from source X. As contentfrom source X is received the ingestion server 25856 may reference thestored dictionary via the link corresponding to source X for parsingsource content from source X.

In example embodiments, ingestion server 25856 may receive content 25854and parameters 25852 in association with an ingestion event or action.Ingestion server 25856 may be configured to receive a stream of datafrom a source Y for the ingestion event. The server may also receive,contemporaneously with this stream (e.g., at an initiation of the streamevent) a set of ingestion parameters for processing content in thestream event from source Y. As an example, source parameters 25852 for astream may include units of measure (e.g., kilometers/hour, percent of avolume, currency exchange rates, and the like) for data values includedin the corresponding stream. The ingestion server 25856 may apply theunits of measure to data values received in the stream to facilitateconformance of the data value with other content used by the intelligentdata layer 25850.

The ingestion server 25856 may be configured to align source content25854 and source parameters 25852 for each ingestion event duringingestion processing. This may allow the ingestion server 25856 toreceive and maintain continuity of source content 25854 and sourceparameters 25852 from a plurality of sources for application to aplurality of intelligent data layer operations.

A multi-use intelligent data layer, such as intelligent data layer 25850may further facilitate configuration of an ingestion server 25856 toaccommodate a plurality of ingestion scenarios, such as distinctsources, distinct ingestion activities for each source and/or aplurality of intelligent data layer consumers, and the like. In exampleembodiments, one or more sets of ingestion control parameters 25968 maybe created, configured and/or maintained by one or more operation andcontrol processes of the intelligent data layer. Sets of ingestioncontrol parameters may be associated with consumers of the intelligentdata layer to facilitate ingestion operations that meet data andintelligence needs of the consumers. As described herein, a consumer mayidentify ingestion data sources, ingestion schedules, ingestion triggersfor on-demand ingestion, and the like. Sets of consumer-specificingestion parameters may be referenced by a control system foringestion, such as a control system of the ingestion server 25856 toalign ingestion operations with consumer expectations, layer needs, andthe like.

The ingestion server 25856 may provide ingested content, optionallyincluding one or more parameters (or information derived therefrom) touse by a data analysis server 25858. The ingestion server 25856 mayapply a source-specific dictionary to a set of ingested data to producea multi-dimensional output that includes data processed with thedictionary and analysis parameters that apply to the ingested content.As an example, a set of ingestion event and/or source-specific analysisparameters derived during ingestion may include information pertinent toa degree of accuracy of the ingested content, such as a number ofdecimals to which the source content is rounded during ingestion. Otheranalysis parameters that may be passed from an ingestion server to ananalysis server may include ingestion timing related parameters, such asa start and stop date/time for a set of ingested content being forwardedto the analysis server 25858.

Other exemplary embodiments, capabilities, features, services, aspects,functions, constructions, implementations, and variations that may beincluded with and/or incorporated into ingestion server 25856 may bedescribed in association with ingestion stage 26004, ingestion stage26104, and ingestion stage 26212.

The analysis server 25858 of the multi-use intelligent data layer 25850may perform various operations on a result of ingestion server 25856parsing and other ingestion activity based on a range of factors, suchas comparing data from a plurality of sources for similarity, fitness toa purpose, differences, based on types of data within or across datasources and the like. In embodiments, analysis may include comparingsources against a target use of intelligence derived from a data source.Analysis of ingestion results may attempt to determine if one or moredata elements from a data source may meet consumption targetrequirements, such as meeting a validity time constraint, an accuracyconstraint, a frequency of update constraint, relevance to a consumptionsubject matter focus, and the like. In embodiments, the multi-useintelligent data layer 25850 may target providing intelligence for aplurality of distinct buyers of services in a software orchestratedtransaction marketplace. The analysis server 25858 may determine if oneor more data elements from sources of content 25854 may be relevant forgenerating intelligence about the marketplace services and based on theresults of analysis may indicate to a controller (e.g., a control toweras described herein and the like) for the layer to utilize the sourcecontent (e.g., data) for generating derived intelligence. The multi-useintelligent data layer 25850 may publish or otherwise convey requestsfor content, such as types of data, and the like that one or morecontent sources 25854 may attempt to meet. The analysis server 25858 maydetermine if ingested content meets requirements of the publishedrequest for data, such as if the content complies with one or moreparameters in the request.

In embodiments, the analysis server 25858 may facilitate configuringdata in the layer for publication, such as configuring one or moreadvertisements that characterize the ingested data in terms of potentialintelligence value, relevance and the like. Examples include makingdata, such as derived intelligence data available on a marketplace(e.g., configuring indexing schemes and the like), making the contentsearchable (e.g., identifying keywords, terms, values, or the like thatmay facilitate discovery of intelligence derived from the ingested datathrough use of a search capability. The analysis server 25858 mayfacilitate access visibility to information of the intelligent datalayer 25850 by publishing, communicating, or broadcasting samples of thedata over a network, directly to potential consumers and the like. Inembodiments, potential consumers of intelligent data layer intelligenceand services may include other intelligent data layers, existing valuesupply chain participants, transaction marketplace participants, and thelike.

In embodiments, the analysis server 25858 may suggest, predict, and/orestimate value of ingested data for a plurality of different consumers.These estimates may be used by the control tower to impact intelligentdata layer functions, such as IDL intelligence pricing and the like thatmay be differentiated for different users. Further such analysis mayindicate that intelligence derived from a first data source may be moreor less valuable to different target consumers.

The analysis server 25858 may use feedback from intelligent data layerusers regarding, among other things, usefulness of intelligence derivedfrom one or more data sources to facilitate ingestion and analysisactivities and the like. In an example, positive feedback onintelligence derived from a data source may result in communication fromthe analysis server 25858 to a controller to make use of the data sourcefor deriving other types of intelligence and the like. Feedback handledby the analysis server 25858 may include feedback from uses of similardata, such as use of data from different sources that may be determinedto be similar. In an example, positive feedback regarding use of a datafrom a first data source may trigger the publishing requests for similardata. Feedback handled by the analysis server 25858 may be based onsimilar intelligent data layers. Feedback handled by the analysis server25858 may be based on alternate configurations of the multi-useintelligent data layer 25850 that may be activated to provideintelligence services for different consumers.

In embodiments, distinct configurations of the multi-use intelligentdata layer 25850 may collaborate to meet data consumer needs, such ascross market transaction environments and the like. An analysis server25858 configuration for a first use (e.g., for producing marketintelligence for a product market) may collaborate with an analysisserver 25858 configuration for a second use (e.g., for producing marketintelligence for a service market). In embodiments, collaboration acrossconfigurations of a multi-use intelligent data layer 25850 may beenabled through exchange of data, such as by a first collaboratingconfiguration of the analysis server 25858 producing analysis resultsthat are provided as a data source for a second configuration of themulti-use intelligent data layer 25850.

In embodiments, the analysis server 25858 may include and/or beconfigured as a set of analysis algorithms that may execute on one ormore processors. These one or more processors may comprise a controllerfor the intelligent data layer 25850, such as intelligent data layercontrol tower 26012 depicted and described in association with FIG. 259.

The analysis server 25858 may communicate ingested data, results ofanalysis, information received from an ingestion stage 25856, and thelike to an intelligence server 25864. Results of analysis may include,without limitation, analyzed content 25862, analyzed ingestion and/oranalysis parameters 25860, and the like. The analysis server 25858 mayreceive and analyze parameters received from the ingestion server 25856.This analysis may include adapting, summarizing, reconfiguring,prioritizing, decoding, encoding, filtering, and other types of analysisprocesses thereby producing a set of analyzed parameters 25860 that maycoordinate with analyzed content 25862.

An intelligence server 25864 may provide intelligence services, such asfor deriving intelligence associated with and/or based on informationreceived from analysis server 25858 (e.g., analyzed content 25862, andthe like). The intelligence server 25864 may utilize artificialintelligence capabilities to develop an understanding about data sourcesincluding, among things, uses of data, values of data, applicability ofdata, collection patterns and relevance to intelligence consumption andthe like. Additional intelligence that may be derived by intelligenceserver 25864 may include, without limitation, layer configurationspecific data relevance, relevance of data from one configuration of themulti-use intelligent data layer to another configuration, value ofintelligence to a consumer, such as to a transactor, value chain networkparticipant, transaction marketplace participant and the like. In anexample, intelligence server 25864 may derive intelligence useful forforming new marketplaces from transactional data gathered from anexisting marketplace.

In embodiments, the intelligence server 25864 may be in communicationwith the intelligent applications 25876. The intelligence applications25876 may communicate intelligence algorithms, configuration data (e.g.,sets of data that enable the intelligence server 25864 to performvarious intelligence functions) and the like to the intelligence server25864 as well as control various aspects of activity of the intelligenceserver 25864. In embodiments, the intelligence server 25864 may executeone or more of the intelligence algorithms on one or more processors.

The intelligence apps 25876 may be organized to align with individualconsumers of the layer so that the intelligence server 25864 may beconfigured to perform intelligence functions designed to deliver thetype and form of intelligence consistent with consumer expectations. Inexample embodiments, a first intelligence application of the set ofintelligence applications 25876 may be configured to work cooperativelywith a first set of ingestion control parameters of the plurality ofsets of ingestion control parameters 25868 (and optionally a first setof analysis configuration parameters) that, cooperatively configure theintelligent data layer 25850 to ingest, analyze, and derive intelligenceto meet data intelligence needs of a consumer, such as consumer X of theset of consumers 25866.

In example embodiments, the intelligence server 25864 may be embodied asone or more intelligence services available from sets of configuredintelligence services available for use through a value chain networksystem of systems and/or an autonomous market orchestration system ofsystems, and the like. Further each set of intelligence applications ofthe plurality of sets of intelligence applications 25876 may be embodiedas one or more of the configured intelligence services described herein.To make use of such embodied intelligence applications, the multi-useintelligent data layer 25850, such as through programmatic interfacevariant of the intelligence server 25864 may communicate withintelligence functions of one of the exemplary system of systemsdescribed herein. In embodiments, the intelligence server 25864 and atleast a portion of the plurality of sets of intelligence apps 25876 maybe embodied as one or more intelligence services of such system ofsystems architectures, including without limitations one or more adaptedartificial intelligence modules.

In example embodiments, the multi-use intelligent data layer 25850 mayinclude one or more interfaces 25872 for initiating and/or controllingproduction of intelligent data layer content for consumers, such as theplurality of consumers 25866. Such an IDL interface 25872 may provide auser interface 25870 through which a user may configure and/or adjustconfigurations and operations of various portions of the multi-useintelligent data layer 25850. In an example, a user may review candidatedata sources as may be suggested by the analysis server 25858 and thelike. User review of such candidate data sources may result inacceptance of the source for use by the layer, rejection of the source,or conditional acceptance that is determined during operation of thelayer, such as based on consumer ingestion control parameters and thelike. The user interface 25870 may provide a wide range of user access,control, and monitoring activities, such as monitoring utilization ofthe aspects of the multi-use intelligent data layer 25850, configuringaccess parameters for consumers, responding to requests by consumers forintelligence functions and the like.

The IDL interface 25872 may facilitate interactions between a user andlayer aspects, such as ingestion control parameters 25868, ingestionserver 25856, analysis server 25858, intelligence server 25864,intelligence applications 25876 and the like. The IDL interface 25872may further provide a programmatic interface between aspects of thelayer with robotic process automation capabilities 25874. In exampleembodiments, robotic process automation capabilities 25874 may beutilized to automate development of new operational configurations toprovide intelligence for new consumers and the like. The robotic processautomation capabilities 25874 may identify activities used to configurea plurality of configurations of the layer, determine relevance of suchactivities to producing certain types of intelligence and the like tofacilitate generating automated configuration sets to meet requirementsof new consumers and the like. In a robotic process automation example,configuration steps by a user to configure ingestion control parameters,identify preferred content sources, configure the analysis server,actions for arranging intelligence services including configuringintelligence applications and the like for a plurality of consumers maybe analyzed by the robotic process automation capabilities to determineat least a recommended sequence of actions to meet intelligent datalayer configuration requirements for a new consumer. Likewise, actionsthat may result in identification and/or validation of new contentsources may be automated via robotic process automation.

Intelligent data layers may be configured to provide integralinformation services to marketplace platforms, such as system of systemtransaction architectures, transaction environments, and the like todeliver, among other things a high degree of intelligence for marketparticipants and marketplace systems (e.g., including marketplaceautomation systems, software orchestrated transactions, marketplaceowners, and the like). Marketplace platforms may publish intelligentdata layers based on marketplace activity. Marketplace platforms maysubscribe to intelligent data layers derived from information sources,such as market-centric sources, competitive sources, buyer and sellersources, government and regulatory sources, and a wide range of sourcesthat may be made available for consumption. Buyers and sellers maysubscribe to intelligent data layer-based information sources, thatsupport each marketplace platform participant's role. As a buyerparticipant example, intelligent data layers for buyers may gather andsynthesize pricing trends, alternative sellers and offerings (e.g.,other products or services) and costs through aggregating data fromseveral sources. As a seller participant example, intelligent datalayers for sellers may improve value of a seller's offering to a buyer,revenue to a seller, such as add-ons, cross-market offerings, etc.Financing terms can each be represented by intelligent data layers thatsupply curated, synthesized data to a seller to facilitate offers,counteroffers, financing options, access to funding, and the like. As amarket maker participant example, intelligent data layers may gather andsynthesize market impacting data to help, for example, with establishinga companion market in a foreign jurisdiction for a local (regional,national, etc.) market, and the like.

Referring to FIG. 268 , an intelligence-enabled marketplace deploymentof intelligent data layers is depicted. A marketplace platform 25952 maysubscribe to intelligent data layers of market-centric content 25966,such as marketplace regulating sources, marketplace operational sources,such as smart contracts that may impact automating transactions,companion marketplace platform content (e.g., trade and termsinformation from companion marketplaces and the like), third-partyinformational services, such as marketplace item curators, itemauthenticators, and the like. In an example, marketplace platform 25952may subscribe to an intelligent data layer IDL-MIA (market intelligenceinput layer A) that may capture transaction-related data from aplurality of marketplaces, such as market pricing data, transaction costdata, and the like. In the example, the marketplace platform 25952 mayalso subscribe to an intelligent data layer IDL-MIB (market intelligenceinput layer B) that may capture, analyze, and derive intelligence fromafter-market activity associated with participants and/or products andservices for which the platform 25952 provides transaction services. Aset of intelligence services of the platform 25952, such as one or moreConfigured Intelligence Services described here (e.g., risk analysisintelligence services, machine learning services, digital twin services,and the like) may process information (e.g., raw data, analyzed data,derived intelligence) provided from these market-centric intelligentdata layers (25966) to perform various marketplace platform functions,such as transaction cost optimization, regulatory compliance,cross-market service offerings (e.g., for after-market type activity,such as product customization, archival packaging and the like).

In example embodiments, a marketplace platform 25952 may generate a widerange of transaction-related information and may employ intelligent datalayers to gain value from this information through, for example,offering preconfigured and/or configurable intelligent data layers toprovide data intelligence services based on the generated data. Forexemplary purposes, the embodiments of FIG. 268 include a firstmarketplace platform output data layer IDL-MOA that may be configured toprovide optimized and/or include intelligence 25970 suitable for use bymarketplace buyers 25954. The marketplace platform 25952 may furtherinclude a second marketplace platform output data layer IDL-MON that maybe configured to provide optimized for an/or include intelligence 25972suitable for use by marketplace sellers 25956. The marketplace platform25952 may yet further include a plurality of marketplace platform outputdata layers (e.g., IDL-MOB, and the like) that may offer tothird-parties actionable information and intelligence about the platformand optionally about transactions occurring on the platform. In anexample, after-market service providers (e.g., extended warranty and thelike) may subscribed to a third-party oriented intelligent data layer todetect transaction information useful for providing extended warranty(or other) services. Third-party oriented intelligence provided throughsuch an intelligent data layer may include information that goes beyondtransaction outcomes, such as seller offer trends (which products aresellers more likely to be offering), buyer trends (what types ofproducts are buyers coming to the platform looking to purchase),correlations between ingested market-centric intelligence (e.g.,third-party services being provided to buyer/seller participants of theplatform) and platform transaction metadata (e.g., costs oftransactions, financing of transactions), and the like.

A marketplace platform, such as platform 25952 may be integrated intoand/or be associated with an automated market orchestration system ofsystems as described herein. The marketplace platform 25952 may rely onintelligence and other services of the automated market orchestrationsystem of systems to provide output intelligence data layers, such asthird-party relevant intelligence data layer IDL-MOB, through analysisand intelligence derivation from one or more sources of marketplaceplatform information. To produce, for example, a seller-centric outputintelligent data layer IDL-MOA, the platform 25952 may interface with anintelligence module controller and related intelligence services (e.g.,machine learning, robotic process automation and the like) to harvest,analyze, and derive seller-related information services that can beconsumed by seller participants 25956 of the platform marketplace 25952.Seller-focused intelligence data layer, such as IDL-MON that producesseller intelligence 25972 may be useful to improve value of a seller'soffering to a buyer, increase revenue to a seller, such as throughinclusion of add-ons, enable cross-market offerings among sellers, andthe like.

Market platform participants may include sellers 25956 and buyers 25954.Participants may subscribe to intelligent data layers to facilitatetheir participation in the marketplace, such as by getting advancedinformation and intelligence about items relevant to them. A seller25956 may subscribe to a plurality of seller-centric data sources 25962through a plurality of intelligent data layers 25964. Theseseller-centric intelligent data layers 25964 may be configured and/orinclude features and capabilities of intelligent data layers describedherein, such as, without limitation 26000 of the embodiments of FIG. 259.

Seller entities, such as seller computing systems and the like mayinterface with one or more of these seller intelligent data layersthrough programmatic computer-to-computer interfaces, such asApplication Programming Interfaces and the like, including thosedescribed in association with intelligent data layers described herein,such as the API 25906 of intelligent data layer 25904 of the embodimentsof FIG. 258 . Seller entities 25956 may subscribe to or otherwise accesscontent from the seller-centric intelligent data layers 25964 similarlyto intelligent data layer consumers described herein, including withoutlimitation consumers 26308 of the embodiments of FIG. 262 . Sellerintelligent data layers 25964 may be configured as described herein tomeet one or more information and/or intelligence needs, desires,interests, priorities, preferences, and the like of one or more sellers25956. A seller intelligent data layer of the set of intelligent datalayers 25964 may be configured to provide real-time intelligence andinformation regarding currency exchange rates (e.g., cross-nationalcurrencies, cryptocurrencies, and the like) that may facilitate use ofanalytics and the like by a seller entity to adapt transaction pricing,parameters, and the like. As an example, if a current exchange ratesuggests potentially greater value to a seller who makes thosetransactions today, the seller may adjust terms of items in themarketplace, offering a bonus (e.g., lower price, extra services, freeitem) for buyers who perform an instant payment transaction and/orincreases costs to a buyer who defers payment transaction into thefuture.

A buyer 25954 may subscribe to a plurality of buyer-centric data sources25958 through a plurality of intelligent data layers 25960. Thesebuyer-centric intelligent data layers 25960 may be configured and/orinclude features and capabilities of intelligent data layers describedherein, such as, without limitation 26000 of the embodiments of FIG. 259.

Buyer entities, such as buyer computing systems and the like mayinterface with one or more of these buyer intelligent data layersthrough programmatic computer-to-computer interfaces, such asApplication Programming Interfaces and the like, including thosedescribed in association with intelligent data layers described herein,such as the API 25906 of intelligent data layer 25904 of the embodimentsof FIG. 258 . Buyer entities 25954 may subscribe to or otherwise accesscontent from the buyer-centric intelligent data layers 25960 similarlyto intelligent data layer consumers described herein, including withoutlimitation consumers 26308 of the embodiments of FIG. 262 . Buyerintelligent data layers 25960 may be configured as described herein tomeet one or more information and/or intelligence needs, desires,interests, priorities, preferences, and the like of one or more buyers25954. A buyer intelligent data layer of the set of intelligent datalayers 25960 may be configured to provide real-time intelligence andinformation to a buyer of the set of corporate buyer entities 25954,such as changes in corporate strategy (e.g., acquisition/mergerinsight), updated corporate buying procedures (e.g., information andintelligence on how changes in buying procedures can best be reflectedin current transaction activities), corporate sales outlook (e.g., tofacilitate adjustments in delivery timing and deferral), cash flow ofthe corporation (e.g., ability to offer to pay cash and reduce pricing),available financing options (e.g., status of corporate lines of credit),and the like.

Buyers 25954 and sellers 25956 participants in the marketplace platform25952 may benefit from marketplace contextual updates. However, buyersin the set of marketplace buyers 25954 and sellers in the set ofmarketplace sellers 25956 may develop independent a marketplace contextconsumer profiles that maybe configured into a marketplace intelligentdata layers IDL-MFB (buyer) and IDL-MFS (seller). In exampleembodiments, marketplace contextual information 25970 from a marketplaceoutput intelligent data layer IDL-MOA may be adapted by a first instanceof the buyer intelligent data layer IDL-MFB for a first buyer (e.g., acorporate buyer) and may be adapted differently (at least in part) by asecond instance of the buyer intelligent data layer IDL-MFB for a secondbuyer (e.g., a non-profit buyer) to enrich an experience and/orperformance of each such buyer in the set of buyer participants 25954.Likewise, marketplace contextual information 25972 from a marketplaceoutput intelligent data layer IDL-MON may be adapted by a first instanceof the seller intelligent data layer IDL-MFS for a first seller (e.g., acorporate seller) and may be adapted differently (at least in part) by asecond instance of the seller intelligent data layer IDL-MFB for asecond seller (e.g., a non-profit seller) to enrich an experience and/orperformance of each such seller in the set of seller participants 25956.

Intelligent data layers may play a role in developing new sources ofcontent for enriching utility, value, and relevance of the types andextend intelligence that these layers provide. Source discovery,vetting, and integration are among a plurality of services andcapabilities intelligent data layers may provide. The embodimentsdepicted in FIG. 269 may include intelligent data layer sourcediscovery. An intelligent data layer control tower 26062 may beconfigured with and/or include access to artificial intelligencecapabilities including machine learning and the like. When anintelligent data layer 26050 with the intelligent data layer controltower 26062 depicted in FIG. 269 is deployed with and/or integrated intoa marketplace system of systems, such as an automated marketorchestration system of systems described herein (and/or optionally avalue chain network) an array of intelligence services may be madeavailable for use in source discovery and the like.

The intelligent data layer control tower 26062 may be configured toconfigure, operate, and optimize execution of source discoveryfunctions, such as an ingestion capability server 26056, an analysisserver 26058, and the like. In an example of source discovery, theintelligent data layer control tower 26062 may direct the ingestionserver 26056 to capture information from and/or about candidate sources26052. In this example, the control tower 26062 may direct anadvertising function of the ingestion server 26056 to advertise one ormore requests for content that may be useful to the intelligent datalayer 26050. The ingestion server 26056 may contact a plurality of knowncontent sources with sets of criteria that may be descriptive of a typeof content desired. The ingestion server 26056 may explore, e.g.,through web crawling, crowd sourcing and the like, potential sources ofdata that may comply with the sets of criteria. In example embodiments,the ingestion server 26056 may identify a set of criteria that isdescriptive of a current data source used by the intelligent data layer26050. The ingestion server 26056 may adapt the criteria (e.g., adjust arange of descriptive value, broaden the criteria by abstracting one ormore requirements, vary presence of different aspects of the criteria)and seek for potential sources of new content.

The intelligent data layer control tower 26062 may use artificialintelligence to develop suggestions for source content criteria, such asbased on analysis of existing sources, based on requests for variationof intelligence from consumers of the intelligent data layer, feedbackrelating to usefulness of existing sources, and the like. Thesedeveloped suggestions may further include references to source metadata, such as data format, unit of measure, jurisdiction, accesscriteria, access costs, content availability, content use terms, and thelike. Using any of a range of content discovery methods, including thosedescribed herein, the ingestion server 26056 may capture content fromone or more candidate sources 26052 that may meet at least a portion ofthe source discovery criteria. In an example, the ingestion server 26056may be tasked with capturing content from mobile devices associated withan enterprise, such as mobile phones configured to interface throughsecure means (e.g., a virtual private network) with external sources. Inanother example, the ingestion server may adapt an ingestion profile forone or more existing sources, such as to permit ingestion of contentthat may have been excluded from ingestion under the original ingestionprofile.

Data collected from candidate sources 26052 via the ingestion server26056 may be vetted for compliance with at least a portion of a targetnew content ingestion criteria, such as complying with a data format, alanguage, a minimum precision, and the like. The ingestion server 26056may pass (e.g., stream and/or store for separate access) acceptablecontent to the analysis server 26058. The ingestion server 26056 mayalso provide source discovery status information to the intelligent datalayer control tower 26062, such as source location information (country,jurisdiction, URL, domain, and the like) at least for sources for whichcontent has been passed along to the analysis server 26058. In exampleembodiments, the ingestion severs 26056 may maintain a list, directlyand/or through interaction with the intelligent data layer controltower, of candidate sources accessed and their status. The ingestionserver 26056 and optionally the intelligent data layer control tower26062 may rely on this source status for future source discoveryactivity. As an example, if a result of widening an ingestion profilefor an existing data source X results in little or no data that meets aminimum set of target source discovery criteria, then a record of theexisting source X could be updated to reflect the relevance (or lackthereof) to the desired content. When another source discovery activityis performed, the source relevance records may be examined beforeseeking to pursue different content from this particular currentlyapproved source.

In example embodiments, an analysis server 26058 may be configured toevaluate content ingested from a new candidate source for meeting one ormore aspects of a target new data source discovery criteria. As anexample, criteria for a new source may include consistency ofterminology (content) in the source and optionally consistency ofterminology to existing terminology used to process ingested content. Inexample embodiments, the analysis server 26058 may be artificialintelligence-enabled, which may facilitate use of various artificialintelligence analysis techniques. An example analysis may involveperforming a recursive operation on data values to determine if the dataapproaches zero (indicating that the day may meet a stability criteria),or if the data does not exhibit a minimum degree of stability. The rangeof potential analysis techniques here are essentially unbounded;however, for any given analysis activity of a candidate source, it islikely that a set of criteria for a target use of the data may be usedto identify a subset, optionally a small subset of analysis actions totake on the data.

For content that meets an acceptability criterion based on a result ofanalysis operations on the ingested candidate source data, additionalcandidate source data vetting steps may be applied. In the exampleembodiments of FIG. 269 , similarity of such candidate source data toexisting source data 26054 may be determined, such as via a similarityserver 26060. The similarity server 26060 may evaluate new contentsource data against the existing source data 26054, potentiallyperforming one or more comparisons to determine if the new contentsource data may provide a meaningful contribute to increase intelligencecapabilities of the layer. In example embodiments, the similarity server26060 may determine similarity of candidate source data by generatingone or more values that capture at least one degree of similarity 26064.The similarity server 26060 may determine that data values in thecandidate source may be too similar to existing sources and thereforemay indicate that in the degree of similarity 26064. IN an example, anintelligent data pipeline may facilitate monitoring operation of anautomated welding station. A candidate source of data may includeadditional temperature sensors on the station. If, upon analysis andsimilarity comparison, the candidate source temperature values add nosubstantive new information, the source may be deemed to be lacking inusefulness. For instance, the additional sensors provide a temperatureof a welded piece immediately before welding. However, a currenttemperature sensor provides information that permits determining thisindirectly because it reports an ambient temperature proximal to thepiece to be welded, which suggests that temperature data from thecandidate source(s) does not provide sufficient new information to meetthe usefulness criteria.

Based on this degree of similarity 26064, an estimate of relevanceand/or utility may be generated by a utility/relevance server 26066. Theestimate of relevance may be expressed as a degree of usefulness 26068.An example degree of usefulness 26068 may include a predicted impact onintelligence that may be derived when data from the candidate source isused by one or more intelligence derivation algorithms, examples ofwhich are described herein. If a degree of usefulness 26068 meets ausefulness criterion (e.g., facilitates generating intelligence based onnew types of data sources), the intelligent data layer control tower26062 may add the candidate source to a list of approved sources byissuing a source decision 26070. If the degree of usefulness 26068 doesnot meet the usefulness criteria, the intelligent data layer may issue asupport decision 26070 that instructs the ingestion server 26056 toignore the candidate source, at least temporarily for the currentinstance of source discovery. A candidate source may not meet usefulnesscriteria if, for example, intelligence results from use of the candidatesource data are outside an acceptable range. As an example, intelligencederived from existing sources may include a desired range of trendvalues. If intelligence algorithms applied with the candidate sourcedata results in generation of trend values outside of the desired range,the usefulness may fall short so that the candidate source may beexcluded in the source decision 26070.

Data and Networking Pipeline for Market Orchestration

Referring to FIG. 270 , a block diagram of exemplary features,capabilities, and interfaces of an exemplary deployment environment27000 of a data and network infrastructure pipeline 27004 is depicted.Data network and infrastructure pipelines may be configured as a portion(or portions) of one or more transaction platforms. The exemplaryembodiment in FIG. 270 depicts a data network and infrastructurepipeline 27004 characterized with at least one each of a set ofasset-centric intelligent network resources, a set of intermediateintelligent network resources, and a set of market-centric intelligentnetwork resources (optionally including a set of asset entities, a setof marketplace entities, and associated controllers) interconnected forproviding asset data and asset data-derived content (e.g., intelligence)from, for example one or more set of assets 27002. Exemplary embodimentsof 27004 are depicted and described elsewhere herein. Associated withthe exemplary data network and infrastructure pipeline 27004 of FIG. 270, assets 27002 may represent one or more sources of asset information,such as business data, sensor data, outputs of portions of otherpipelines, virtual data, and the like to which data network andinfrastructure pipeline processes may be applied. In an exemplarytransaction platform deployment of data network and infrastructurepipeline methods and systems, which is described elsewhere herein ingreater detail, asset data 27002 may be applied through the data networkand infrastructure pipeline 27004 to impact transaction outcomes, buyerand/or seller operating environments, market data, and the like,typically through configuring one or more marketplace parameters forconducting marketplace workflows for transactions of the assets.

In embodiments, a data network and infrastructure pipeline, such as27004 may be configured with or operationally connected to a set ofapplication programming interfaces (APIs) 27006 through which, amongother things, asset data may be retrieved and/or received. In exemplaryembodiments, an API 27006 for a data network and infrastructure pipelinemay be an open/standardized API 27006 (e.g., banking/financialinstitution open APIs) that, among other things, may equip the datanetwork and infrastructure pipeline 27004 for integration with and intocurrent and emerging ecosystems. Use of open/standardized APIs 27006,while optional for some data network and infrastructure pipelineembodiments, may further enable these pipelines to integrate into a widerange of transaction workflows, such as corporate internal workflows,inter-jurisdiction transaction workflows, and the like.

A data network and infrastructure pipeline such as 27004 may include,reference, and/or provide market orchestration elements 27008 that mayfacilitate use of data network and infrastructure pipeline capabilitiesfor various aspects of market orchestration, including, withoutlimitations, software orchestrated transactions, software orchestratedmarketplaces, and the like. Market orchestration elements 27008 mayfacilitate deployment of a data network and infrastructure pipeline,such as a web service embodiment, as an integrated function of a marketorchestration platform, such as an automated market orchestration systemof systems as described herein. In embodiments, a data network andinfrastructure pipeline may provide data and network pipelinecapabilities for market orchestration when configured as a portion of adata network and infrastructure pipeline 27004 in association withmarket orchestration elements 27008 and the like.

DP environment 27000 may include, reference and/or provide cross marketinteraction capabilities 27010 that may enable leveraging data networkand infrastructure pipeline principals, computation capabilities,storage and data sourcing capabilities, as well as intelligencecapabilities for cross market interactions. Cross market interactioncapabilities 27010 may include interfaces to one or more marketplaces,transaction environments, and the like, so that, among other things, adata network and infrastructure pipeline may be configured with assetsfrom one market in a cross-market integration deployment as a source ofdata and with another market in the cross-market integration deploymentas a target recipient of the data network and infrastructure pipelineservices. In embodiments, a similar arrangement may be constructedbetween two or more markets so that asset data in either market can beused as a data source and can be influenced by asset data from anothermarket. Cross market interactions 27010 may be accomplished through oneor more market-to-market data network and infrastructure pipelines forintelligent exchange of asset data among markets, such as data aboutassets of buyers in one market and about assets of sellers in another.

In the exemplary data network and infrastructure pipeline embodiment ofFIG. 270 , functions and processes 27012, for an exemplarymarket-oriented deployment may include software-oriented transactionfunctions and processes, automatic transaction transactions andprocesses, and the like. Functions and processes 27012 for a datanetwork and infrastructure pipeline 27004 may include signalingavailability of data (e.g., emergence of an occurrence of asset data)that impacts data provided to a transaction operator from (for example)a data and network infrastructure pipeline. Other exemplary functionsand processes 27012 may include embedding network adaptabilitycapabilities into smart contracts, tokens, publishing data on aschedule, or other occurrences (e.g., an initiation of a smart contractand the like). Yet other functions and processes may include paymentsbetween/among machines and the like.

In embodiments, a data network and infrastructure pipeline may includeand/or be associated with data and network infrastructure pipelinetechnology enablers 27014, such as 5G networking, artificialintelligence, visualization technology (e.g., VR/AR/XR), distributedledger, and the like.

In embodiments, a data network and infrastructure pipeline 27004 mayinclude and/or leverage cloud-based virtualized containerizationcapabilities and services 27016, such as without limitation a containerdeployment and operation controller, such as Kubernetes 27018 and thelike. Cloud-based virtualized containers may facilitate data network andinfrastructure pipeline smart network resources being deployed close toasset data, thereby potentially reducing network bandwidth consumptionor the potential for network disturbances in a data workflow and withoutsubstantive investment in infrastructure by the data network andinfrastructure pipeline operator and/or consumer.

The data network and infrastructure pipeline of FIG. 270 may furtherinclude business system interfaces 27020, such as APIs and the like thatfacilitate adoption of data network and infrastructure pipelines byenterprises for development, among other things of a data-centricbusiness workflow environment that enables cross-functional data use,seamless aggregation, and immediate contextualization of corporate datafor individual departments, enterprise, subsidiary, and the like.Further integration of aspects of the data network and infrastructurepipeline into enterprise systems may include integration with one ormore enterprise databases and the like.

Data network and infrastructure pipeline enabled markets 27022 may beenabled by and/or enhanced through the adoption of data and networkinfrastructure pipeline technology. Markets, such as markets at anintersection of financial service and physical product offerings may berevealed and/or enabled through application of these pipelines to helpparse, analyze, and provide intelligence for a wide range ofmarket-impacting and/or related assets. These emergent markets may besubstantively constructed as a result of intelligence gathered by use ofdata network and infrastructure pipelines within or in association withindividual markets, and the like.

Technologies that may be provided by and/or enabled by a data networkand infrastructure pipeline 27004 may include intelligence services27024, such as artificial intelligence, machine learning and the like.These intelligence services 27024 may be provided in the environment27000, or accessed (e.g., as third-party services) via one or moreinterfaces of the environment 27000. The data network and infrastructurepipeline embodiment 27004 may be provided access to these intelligenceservices 27024. One or more data network and infrastructure pipelineembodiments 27004 may bring to the platform its own set of intelligenceservices, which may be dedicated to the host data network andinfrastructure pipeline, or may be shareable among linked pipelines, forexample.

In the exemplary embodiment of FIG. 270 , transaction/market-orientedcapabilities, services, and deployment may include market platforms27026, transaction flows 27028, buyers 27032, sellers 27031, andtransaction/marketplace specific data network and infrastructurepipelines that enrich transactions, transaction services and the like27030. For multi-party transaction environments, a plurality of datanetwork and infrastructure pipelines may be configured and operated tosatisfy a range of consumer needs for market analysis, transactionefficiencies, cost containment, buy/sell decisions and the like.

Referring to FIG. 271 , a data and network infrastructure pipeline 27104is configured to deliver data from a set of assets 27102 to one or moremarketplace entities for one or more marketplaces 27106 in whichtransactions for portions of the sets of assets 27102 are conducted. Inexample embodiments, the data from the set of assets 27102 is deliveredby the pipeline 27104 to an interface by which an operator orchestratesa set of parameters for a set of transaction workflows involving theassets. The pipeline 27104 may be automatically configured to adjust anetwork path for delivery of data from the set of assets 27102 to theinterface based on the characteristics of the data and at least oneperformance parameter of the network path. In example embodiments, thepipeline 27104 may be automatically configured to adjust timing of assetdata delivery from the set of assets 27102 to the interface based on atleast one of a transaction parameter and a network performanceparameter.

Referring to FIG. 271 , a data and network infrastructure pipeline 27104is configured to deliver data from a set of assets 27102 for use intransactions in one or more marketplaces 27106 in which transactions forportions of the sets of assets 27102 are conducted. In exampleembodiments, the data from the set of assets 27102 is delivered by thepipeline 27104 to a set of smart contracts that include terms,conditions and parameters for a set of transaction workflows involvingthe assets, such as transaction workflows of transactions of the assets27102 in the marketplace 27106. The pipeline 27104 may be automaticallyconfigured to adjust a network path for delivery of data from the set ofassets 27102 to the set of smart contracts based on the characteristicsof the data and at least one performance parameter of the network path.In example embodiments, the pipeline 27104 may be automaticallyconfigured to adjust timing of asset data delivery from the set ofassets 27102 to the set of smart contracts based on at least one of atransaction parameter and a network performance parameter.

Referring to FIG. 272 , the set of assets 27102 may include electronicdevice assets 27202 with electronic (e.g., wired/wireless) interfaces27204 configured to deliver the data from and/or about the asset 27202to an interface of the network pipeline 27104. The network pipeline27104 may communicate with the interfaces 27204 throughcomputer-to-computer networks, such as the internet and the like, usinga range of protocols including TCPIP, and the like. One or more of theelectronic assets in the set of electronic assets 27202 may communicatedirectly (e.g., through an interface) to the network pipeline 27104through its corresponding interface 27204. Alternatively, a portion ofthe set of electronic device assets 27202 may be separated from thepipeline 27104 through an interface 27206, such as a local networkrouter, gateway and the like. In example embodiments, the interface27206 through which the portion of the set of electronic device assets27202 may be a native interface of one of the electronic device assets27202.

Referring to FIG. 273 , the set of assets 27102 may include one or moreassets 27302 that are managed by an asset management interface 27304that is configured to deliver data pertinent to the asset 27302 formanaging transactions of the one or more assets 27302 to the networkpipeline 27104. The asset management interface 27304 may handlecommunication responsibilities associated with pertinent data forassets, such as one or more assets 27302 that do not have acommunication capability, such as non-electronic physical assets. As anexample, a set of assets 27102 may include one or more assets 27302 thatdoes not have an interface through which the asset 27302 couldcommunicate with the network pipeline 27104. In this example, the asset27302 may be a battery, a piece of equipment, a structural member (e.g.,a bridge truss), materials, and the like. The asset management interface27304 may capture, such as through one or more sensors, cameras, humanoperators, and the like information about the asset, such as the assetexternal color, present location, asset weight, and the like. The assetmanagement interface 27304 may capture information about the asset froma third-party information source, such as a warranty manual for theasset 27302, a last user of the asset 27302, and the like. The assetmanagement interface 27304 may provide a capability for enabling use ofthe network pipeline 27104 to facilitate configuring, for example,parameters associated with transaction workflows for the asset 27302.The asset management interface 27304 may be configured to provideasset-relevant data for a single asset, a group of assets, and the like.

The network pipeline 27104 may communicate with the interfaces 27304through computer-to-computer networks, such as the internet and thelike, using a range of protocols including TCPIP, and the like. One ormore of the assets in the set of non-electronic device assets 27302 maybe depicted by its corresponding interface 27304 when communicatingdirectly to the network pipeline 27104. Alternatively, a portion of theset of non-electronic device assets 27302 may be grouped and representedto the network pipeline 27104 through the interface 27304.

Referring to FIG. 274 , the set of assets 27102 may include a pluralityof types of assets 27402 including, without limitation, electronicdevice assets, non-electronic device assets, rights (e.g., digital),services, humans as assets, robotic fleet(s), and the like. The type ofasset may be configured to provide information about aspects thereof,such as performance-related data, physical data, operational data, valuedata, data that defines parameters of use, jurisdictional data, and thelike. As for asset types 27402 without a means of communicating with thenetwork pipeline 27104, a suitable interface/controller 27404 may beconfigured to interface with the network pipeline 27104, similarly, atleast in part, to that described herein for the asset managementinterface 27304 of FIG. 273 . An electronic device type may provideinformation specific to its function, such as a sensor device thatsenses wind speed may provide wind speed data along with informationthat facilitates determining context of the data (e.g., how is itcaptured, capture timing, unit of measure, security data, and the like).A non-electronic device asset type may, such as through a suitablenetwork pipeline interface 27404, expose information pertinent totransacting for the non-electronic device asset, such as physical,operational and other information that may enable an operator and/or asmart contract to configure one or parameters for conductingtransactions of/for the asset. In example embodiments, asset data forservice type assets may include information about the service as well asan indication of how to use the service, such as through a third party,through a network download of service software, through use of a networkportal for receiving the services, and the like. Service-type assets,such as digital services, may be interfaced with the network pipeline27104 through a computing device (e.g., a web server and the like) thatprovides the service. For physical-type service assets, such as in-fieldmaintenance services, meal preparation services, energy supply services,and the like a suitable asset management interface module 27404 mayfacilitate providing information specific to types of physical servicethat can be leveraged by an operator and/or by a smart contract fororchestrating one or more transactions (e.g., including a transactionworkflow) for the physical service assets. One such example includes alist of resources for providing the service(s) called out in theservice-type asset.

An asset management interface, such as interface 27404, or interface27304 may be embodied to provide, for example, asset-specificcapabilities. An asset management system may also be configured toenable use of artificial intelligence and the like when generatingand/or handling data about the asset. In example embodiments, an assetmanagement interface may include a digital twin of the asset that mayinterface with the asset through one or more proprietary interfaces (ormay independently monitor the asset) and provide near-real time data tothe network pipeline 27104 that is consistent with the asset operationalstatus, functionality, and the like. In example embodiments, an assetmanagement interface, such as interface 27304 and/or 27404 may include asmart contract by which the asset is controlled or otherwise transacted.As an example, an asset may be a consumable supply item and acorresponding interface with the network pipeline 27104 may include alogistics service that provides inventory management, warehousing,distribution, and last-line delivery of the asset. Another asset typemay include an on-demand built asset that may be represented to thenetwork pipeline 27104 by one or more systems that produce and/orprovide the on-demand built asset, such as a mobile 3D printing systemthat, based on a result of a transaction for an asset available to bebuilt may build the asset while in transit to a recipient's identifieddestination. Yet another type of asset may include human assets. Inexample embodiments, human assets may be represented in the networkpipeline 27104 through data provided from an asset management interface.An exemplary human asset management interface may include a businessenterprise that employs the humans, such as a consulting agency thatprovides human resources for deployment to business tasks and the like.Another exemplary human asset management interface may include a set ofsensors deployed to capture and provide pertinent information for use inmarketplaces in which the human resources are transacted. As an example,a human may wear a smart watch that monitors a range of aspects,including some health aspects of the human. Data from the smart watchmay be made available to the network pipeline 27104 when transactionsregarding the human are being orchestrated, such as for identifyingcandidates for a sleep study for which a transaction is being conducted.Other monitoring devices, such as an electronic calendar system for thehuman may share data representative of an expected availability of thehuman for a future time frame for which humans (e.g., crowd sourcing)are being solicited in a transaction to perform a task.

Information provided to the network pipeline 27104 from or on behalf ofan asset may cover a range of aspects of the asset. A few exemplaryaspects are described here as examples of this range; however, theseexamples, or other description of such data is not to be dispositive ofthe full range and scope of data of or about an asset that iscontemplated for use in the methods and systems of data network andinfrastructure pipelining for marketplace orchestration and the likedescribed herein. In example embodiments, aspects related to anexpiration of the asset (e.g., use by date) or of an offer associatedwith the asset (time-limited pricing), and the like may be provided.Owners of perishable assets may derive a benefit from network pipelinedelivery of this information being prioritized by the network datapipeline 27104 for urgent delivery, such as by organizing the network(e.g., configuring a route of the network) to prioritize delivery ofinformation while the perishable asset remains usefulness to aperspective recipient in a transaction for the asset. In an example, anowner of a financial instrument that has an expiration date, such as anoption in a stock market, may benefit from prioritization of delivery ofinformation about the asset (e.g., its terms, pricing, expiration, andthe like) based on context of market activity about the underlyingfinancial instrument covered by the option. Owners of ripening assetsmay derive a benefit from network pipeline timing being adjusted basedon a timing of the ripening asset becoming available.

Timing of asset data through a network pipeline 27104 may also beinfluenced by information about the asset, such as when the asset willbe ready for delivery (or when the asset is expected to be deliveredbased on an estimate of delivery delays). Cross market transactions maybenefit from a data network pipeline that adjusts timing of datadelivery for a first asset based on information regarding a secondasset. As an example, the timing of providing information from a serviceprovider-type asset to a smart contract for providing services to ownersof a serviceable asset may be adjusted based on information providedfrom the serviceable asset through the network pipeline 27104. Thenetwork pipeline 27104 may be configured to adjust delivery timing ofdata signaling an activation of a service contract based on informationfrom the serviceable asset being provided to the network and transactionconfirmation information derived from a workflow associated with atransaction for the serviceable asset.

In example embodiments, information relating to asset performance orreliability is another example aspect of asset data that may be providedfrom or on behalf of an asset through the network pipeline 27104. In anexample related to asset reliability, information regarding a measure ofreliability of providing energy from a wall battery in a private homethat is charged by solar power may impact a value of the energy. Intimes of demand for energy, the private wall battery data, such as ahistorical record of timely release of energy may be valuable toorchestrating transactions in an energy marketplace where buyers seekingto purchase stored energy value such information. However, if a networkpipeline 27104 fails to provide information about this potentiallyvaluable source of energy in a timely manner, transaction for the energymay have been satisfied before the information can be used fororchestrating a transaction and the like. Therefore, a network pipeline27104 may be configured to adjust a network path and/or prioritizationof delivery of data through a configured network path based on acombination of a demand for a commodity (e.g., stored energy) and anaspect of performance of energy providing assets (e.g., an energystorage facility).

In example embodiments, information relating to asset capacity, orremaining inventory, may be provided from or on behalf of an assetthrough the network pipeline 27104. Use of a rechargeable servicevehicle may be an asset for which a marketplace exists. In addition tobasic information about the service vehicle (carrying capacity, etc.)and remaining charge of the service vehicle battery, the networkpipeline 27104 may receive information about deployment of the assetthat may impact, for example, pricing of the asset. In this example, useof the vehicle proximal to a charging station may be priced lower thanuse that results in the vehicle being located far from a chargingstation. Therefore, information about deployment of the asset, that maybe sourced from other than the asset may be pertinent to transactionworkflows in a marketplace for the asset. Similarly, information aboutan asset having an upcoming service event may be valuable to be providedthrough the network pipeline 27104. In this service event example, apath of asset data may be adjusted based on the upcoming service need,such as by directing information through the network to potentialproviders of the service. In this way, the network pipeline 27104 mayconfigure a path for information about the asset that gathersinformation about an asset from a related third-party asset provider(e.g., a service provider asset) to improve orchestration of one or moretransactions (e.g., configuring parameters and the like for transactionworkflows) for the asset.

Another type of asset data that may be delivered by and influenceadaptation of paths and/or timing of network infrastructure of networkpipeline 27104 includes rapidly changing information, such as stockpricing, and other real-time impacted data. Such data may be providedfrom an asset as a data stream that includes changes in the asset data,such as bid, ask, and spread information for an electronically tradedfinancial instrument, and the like.

Referring to FIG. 275 , the data and network infrastructure pipeline27104 may include a set of asset-centric intelligent network resources27502 that may be disposed proximal to the set of assets 27102. The dataand network infrastructure pipeline 27104 may further include a set ofintermediate intelligent network resources 27504 that may be configuredto deliver data from the set of assets 27102 through the networkpipeline as described herein. The data and network infrastructurepipeline 27104 may also include a set of marketplace-centric intelligentnetwork resources 27506 that may be disposed proximal to recipients ofthe asset data, such as interfaces associated with a marketplace 27106in which one or more transactions (and associated transaction workflows)for the assets 27102 may be conducted. The one or more sets ofintelligent network resources, such as sets of asset-centric resources27502, intermediate resources 27504, or sets of marketplace-centricresources 27506 may be implemented in or in association with physicalresources of a data communication network, such as the Internet and thelike. Sets of asset-centric resources 27502 and/or sets of marketplaces(e.g., asset data recipient) centric resources 27506 may include networkinfrastructure resources, such as edge computing devices, inter-networkinterface devices (e.g., bridges, routers, and the like), aggregationdevices, such as a distributed antenna system, and the like.

Referring to FIG. 276 , the data and network infrastructure pipeline27104 may include a set of asset-centric intelligent network resources27502 that may be configured to deliver data from one or more sets ofassets 27102 through the network pipeline 27104. These sets ofasset-centric intelligent network resources 27502 may include a set ofasset local resources 27602 that are configured by an asset localresource controller 27604 to work cooperatively with asset-centric datahandling service 27606 to among other things manage use ofasset-localized network storage 27608 to preserve the data deliveredfrom the sets of assets 27102 for supporting network path and networkdelivery timing adaptations described herein. In example embodiments,the asset local resources 27602 may be configured to interface withintelligent assets, such as electronic assets 27202. The asset localresource controller 27604 may automatically determine, such as through aresult of analysis of data from the electronic assets 27202 by theasset-centric data handling system 27606, configuration parameters forone or more of the sets of asset local resources 27602 to interface withone or more corresponding electronic assets of the set of electronicassets 27202. The asset local controller 27604 may retrieve such resultof analysis from the asset-localized network store 27608.

Computing logic associated with an exemplary asset local resource 27602,such as a processing system or circuit thereof and the like, may beconfigured to execute communication protocols suitable for interactingwith the interface 27204 of an electronic asset 27202, such as aspecific asset data transfer protocol, and the like. As an example, acommunication (sub) system of an exemplary local asset resource 27602may be configured to communicate with an electronic device asset 27202(e.g., through a corresponding asset interface 27204 and the like) tofacilitate delivery of asset data over the network pipeline 27104,including without limitation the asset responding to queries for assetdata from the network pipeline 27104. The asset local intelligentresource 27602, optionally in cooperation with the asset local resourcecontroller 27604 and/or the asset-centric data handling system 27606 mayfacilitate processing of asset data and communication with an asset sothat the network pipeline 27104 may be configured to deliver data fromelectronic asset 27202 independently of how a communication system ofthe asset might be programmed.

In other example embodiments, the set of asset local resources 27602that are configured by an asset local resource controller 27604 to workcooperatively with asset-centric data handling service 27606 to amongother things manage use of asset-localized network storage 27608 topreserve the data delivered from non-intelligent assets, such as throughan asset management interface system 27304, including to preserve thedata delivered from the assets for supporting network path and networkdelivery timing adaptations described herein. The asset local resourcecontroller 27604 may automatically determine, such as through a resultof analysis of data from the asset management interface system 27304 bythe asset-centric data handling system 27606, configuration parametersfor one or more of the sets of asset local resources 27602 to interfacewith one or more corresponding asset management interface systems 27304of the set of non-electronic assets 27102. The asset local controller27604 may retrieve such result of analysis from the asset-localizednetwork store 27608.

Computing logic associated with an exemplary asset local resource 27602,such as a processing system or circuit thereof and the like, may beconfigured to execute communication protocols suitable for interactingwith the asset management interface system 27304 of an asset of the setof assets 27102, such as a specific asset data transfer protocol, andthe like. As an example, a communication (sub) system of an exemplarylocal asset resource 27602 may be configured to communicate with anasset management interface system 27304 to facilitate delivery of assetdata over the network pipeline 27104, including without limitation theasset responding to queries for asset data from the network pipeline27104. The asset local intelligent resource 27602, optionally incooperation with the asset local resource controller 27604 and/or theasset-centric data handling system 27606 may facilitate processing ofasset data and communication with an asset so that the network pipeline27104 may be configured to deliver data from the asset managementinterface system 27304 independently of how a communication systemthereof might be programmed.

In example embodiments, the sets of asset-centric intelligent networkresources 27602 may be configured to deliver data from an assetcollection (e.g., a local collection of assets within a facility, suchas a production, warehousing, or other environment) through a set ofasset environment local resources. These resources may be disposedlogically and or physically within or proximal to a deploymentenvironment for asset collection and may be configured by an assetenvironment local resource controller to work cooperatively with anasset-collection centric data handling service to among other thingsmanage use of asset-localized network storage 27608 to preserve the datadelivered from the assets for supporting network path and networkdelivery timing adaptations described herein. In example embodiments, anexemplary asset environment local intelligent resource of the set ofresources may be configured to identify and communicate with at least asubset of individual assets within the collection. The asset environmentcentric data handling system may normalize data that is differentiatedamong the collection of assets to conform to a set of asset datarequirements for format and the like. The asset environment centric datahandling system may further and/or alternatively process data from thecollection, selecting at least in part asset data to be delivered overthe network pipeline 27104 (and/or optionally stored in asset-localizednetwork store 27608) to facilitate adaptation of path and/or timingaspects of the network pipeline 27104 as described herein.

In general, configuration of and processing by asset local intelligentnetwork resources 27602 and/or asset environment local intelligentnetwork resources may be guided by knowledge of source assets tofacilitate adapting aspects of the network pipeline 27104 according tothe methods and systems described herein.

Referring to FIG. 277 , the data and network infrastructure pipeline27104 having a set of intermediate intelligent network resources 27504may be adapted to deliver asset data from the asset local resources27502 on to one or more marketplace related interfaces, such as a userinterface, a smart contract, and the like. The set of intermediateintelligent network resources 27504 of FIG. 277 may include a networkpath adaptation/determination system 27702 that facilitates adapting anetwork path by producing an automatically adapted network route 27704for the asset data. The network path adaptation/determination system27702 may perform network path determination based on characteristics ofthe asset data. Aspects of the data that may impact network pathadaptation/determination may include security requirements. In exampleembodiments, when an aspect of an asset (optionally expressed in orinferred from the data) indicates a high security requirement fortransfer of the data through the network pipeline 27104, a network pathmay be adapted and/or determined by the path determination/adaptationsystem 27702 based on resource reputation. In an example, networkresources identified as other than having a high security integrityscore (e.g., resources that may be suspect for lacking securityintegrity and/or may not be cleared of potential malware) may be avoidedwhen configuring a route through the network for the asset data. Inexample embodiments, resources that are not classified as either secureor suspect may be avoided.

In another example of network adaptation for data securityconsiderations, the network adaptation system 27702 may adjust atransmission protocol to avoid exposing data from the asset in a contextthat gives meaning to the data. In example embodiments, adjusting atransmission protocol may include encrypting the data. In other examplesadjusting a transmission protocol may include processing asset data,such as with the asset-centric data handling system 27606 and the liketo separate data from relevant context, such as by sending a firsttransmission of a first portion of the data and sending a secondtransmission of a second portion of the data. Further, the networkadaptation system 27702 may choose distinct routes for the two portionsof data, thereby further reducing the potential for a resource in theadapted network path substantively compromising security of the data.

In another example of network path adaptation, a network path may beadapted dynamically. The network adaptation system 27702 may maintain arecord of network paths configured for delivery of data from an assetand may adapt a route for the data so that it changes over time and inapparently arbitrary ways to further increase the secure handling of thedata within the network pipeline 27104.

In yet another example of network path adaptation based on aspects ofdata from an asset, a network path may be determined based on one ormore of a location of a source of asset data (e.g., a jurisdiction of acurrent location of the asset) and a location of a destination of theasset data (e.g., a jurisdiction of a marketplace, an operatorinterface, a smart contract, a participant/buyer of the asset, adeployment location of the asset, and the like). The network pipeline27104 may include intermediate resources in a range of jurisdictions.Avoiding jurisdictions when adapting a network path for delivery ofasset data through the network pipeline 27104 may be impacted byrequirements and/or preferences of an asset owner, a recipient of assetdata, an operator of the network pipeline 27104, a government agency,and the like. In example embodiments, although reference is made tojurisdictions, other ways of identifying one or more network resourcesto avoid are contemplated herein, such as based on IP address, costsassociated with use of the network resources, performance (e.g.,historical) of network resources, crowd-based recommendations forresources to avoid, use of artificial intelligence for path generation,and the like. In example embodiments, network pathdetermination/adaptation may include, for example, configuring a VirtualPrivate Network (VPN) for delivery of asset data.

The network path adaptation/determination system 27702 may performnetwork path determination based at least one performance parameter ofthe network path. The network path adaptation/determination system 27702may perform network path determination based on at least one of atransaction parameter and a network performance parameter. The networkpath adaptation/determination system 27702 may perform network pathdetermination based on an aspect of a recipient of the asset data. Anetwork path may be adapted in a first manner for a user interfacerecipient of the asset data, a second manner for a smart contractrecipient of the asset data, a third manner for other recipients of theasset data.

The automatically adapted network route 27704 may facilitate delivery ofasset data to, for example, the marketplace local resources 27506. Theautomatically adapted network route 27704 may be configured to deliverat least a portion of the asset data to one or more asset entities 27706for handling one or more aspects of transactions of the assets, such aspreparation of the assets, preparation of transaction-relevantinformation about/derived from the asset data and the like. Generally,configuring a network pipeline path may include routing asset data tospecific entities, such as carriers, insurance providers, assetappraisers, regulatory agencies and the like. Determination of a paththat may include delivery of at least a portion of asset data to anasset entity 27706 may be based on aspects of the set of assets 27102,such as based on analysis of asset data. In an example, an asset ownermay indicate, such as through asset data delivered over the data networkpipeline 27104, that transactions for the asset are to be performedthrough a specific transaction settlement vendor, such as a bank orother third party. In example embodiments, the network path adaptationsystem 27702 may, based on such an indication, route at least a portionof the asset data to a transaction settlement vendor that may beselected from the set of asset entities 27706 and the like. The networkpath determination system 27702 may further determine, based on insightsgleaned from the asset data being provided to the network pipeline 27104that an appraisal of the corresponding asset is outdated or otherwisemay present a mitigating factor during a transaction for the asset.Based on this assessment, the network path determination system 27702may configure a path that includes providing at least a portion of theasset data to an appraisal resource, optionally selected from the set ofentity assets 27706. In example embodiments, the network pathdetermination system 27702 may prescribe a network path that routes theportion of the asset data to the appraisal resource and workscooperatively with a network timing adjustment system 27708 to adjusttiming of delivery of a portion of the asset data to one or more of themarket-centric resources 27506. The timing may be adjusted so thatdelivery of the asset data to, for example, an interface of amarketplace through which an operator configures parameters fortransaction workflows of the asset, is coordinated with delivery ofasset appraisal results from the asset appraisal resource. Theautomatically adapted network route 27704 may further be configured toroute data produced by or on behalf of the asset entities 27706 to themarketplace local resources 27506.

In example embodiments, adapting a network path may further be based onaspects of workflow steps for transactions of or associated with theassets. In an example of workflow impact on adapting a network pipelinepath, a workflow may include an (optional) appraisal step that, unlessactivated in the workflow, would not be present in the transaction.Therefore, configuring a network path to include routing a portion ofthe asset data to the appraiser may be dependent on activation of anappraisal workflow task as, for example, a requirement of a transactionfor the asset.

The set of intermediate intelligent network resources 27504 of FIG. 277may include a network timing determination or adaptation system 27708 tofacilitate, among other things, asset data or asset-related datadelivery according to one or more time-related aspects of a transactionof the assets 27102. The network timing determination or adaptationsystem 27708 may configure a portion of an interconnected network,optionally including one or more network-accessible computing and/orstorage resource into a network path with automatically adapted timing27710. The network path configured with automatically adapted timing27710 may deliver asset data and/or asset-relevant data (e.g., as may beproduced by the asset entities 27706) to one or more marketplace localresources in the set of intelligent market local resources 27506. Thenetwork timing determination and adaptation system 27708 may performnetwork path timing adaptation in cooperation with one or more of thesets of asset-centric intelligent network resources 27502. In anexample, the network timing determination and adaptation system 27708may determine that asset data is to be scheduled for delivery no soonerthan an earliest delivery date (e.g., after an announcement by the assetowner related to the asset). The network timing determination andadaptation system 27708 may communicate with, for example, the assetlocal resource controller 27604 and/or the asset-centric data handlingsystem 27606 to process and/or store asset data in the asset-localizednetwork store 27608. The network timing determination and adaptationsystem 27708 may, based on a signal that the earliest delivery data hasoccurred, retrieve or request to be retrieve the stored asset data fordelivery through a network path configured for delivery. In exampleembodiments, configuring a network to comply with timing requirementsassociated with delivery of asset data may include configuringintermediate network storage (e.g., other than an asset-localizednetwork storage facility 27608) to store relevant asset data.

In yet another example of automatically adapting an aspect of networktiming for at least a portion of a network pipeline 27104 for providingdata from a set of assets 27102 for use in configuring transactionworkflows for the assets, a priority of network packets comprising thenetwork data may be configured so that the asset data achieves a networkdelivery timing requirement. This may include configuring a network pathbased on known or anticipated performance of resources along thepipeline so that, for asset data that is to be delivered quickly,resources with lower performance scores may be avoided. Similarly, whentiming for delivery of asset data can be extended, a network path may beselected to reduce costs of delivery of the data by choosing, forexample lower cost resources that may induce delays in data delivery.

In example embodiments, configuring a network, such as timing-adaptednetwork 27710 may include parking data for assets until a future timeindicated by, for example a workflow for transacting the assets. As anexample, a transaction workflow for a digital asset (e.g., access to adatabase and the like) may indicate that delivery timing of asset data,such as an access code and the like, is dependent on a transaction forthe asset achieving a stage of transaction progression, such as a stagethat results from a confirmation of payment being received for theasset. The network pipeline 27104 may be configured into a networktiming adapted network configuration 27710 that parks the asset dataconditionally based on the asset transaction workflow timing.

In example embodiments of FIG. 277 , a network adaptation system mayautomatically construct a network infrastructure path in a networkpipeline to deliver data from an asset to a market orchestrationrecipient, the constructed network infrastructure path may beautomatically adapted based on one or more characteristics of the datafrom the asset and at least one performance parameter for the networkinfrastructure path. Further, a network timing adaptation system mayautomatically adapt network infrastructure resources in a networkpipeline that delivers data from the asset to the market orchestrationrecipient for orchestration of a transaction of the asset. The networkinfrastructure resources may be adapted based on at least one of aparameter of the transaction of the asset and a performance parameter ofthe network pipeline. In the example embodiments of FIG. 277 , a set ofasset-centric network resources may facilitate ingestion of the datafrom the asset into the network pipeline. Also, a set ofmarketplace-centric network resources may facilitate delivery of theasset data from the adapted network pipeline to the market orchestrationrecipient. In example embodiments, the network pipeline may deliver thedata from the asset to the market orchestration recipient fororchestration of a transaction of the asset. In an example of timingadaptation, the network timing adaptation system may adapt the networkinfrastructure resources in the network pipeline to satisfy a datadelivery timing requirement associated with a transaction workflow forthe asset. In another example of timing adaptation, the marketorchestration recipient may include a smart contract that includesterms, conditions, and parameters for a set of transaction workflowsinvolving the asset. Also in an example, adapting the networkinfrastructure path may be based on one or more security characteristicsof the asset data, such as configuring a path through the networkpipeline that avoids poor reputation network resources. In exampleembodiments, constructing a network infrastructure path in a networkpipeline may include adjusting a communication protocol that avoidsexposing data from the asset in a context that gives meaning to thedata. This may include delivering a first portion of the asset datathrough a first network path and a second portion of the asset datathrough a second network path. Another example of secure-contextprotecting network path adaption may include adapting the network pathfor delivering the data from the asset so that the network path changesover time. Also, by ensuring that at least one infrastructure node inthe constructed network path is different than infrastructure nodes usedpreviously to deliver the data from the asset. In an example, adaptingthe network infrastructure path based on one or more characteristics ofthe data from the asset may include configuring a plurality ofrecipients for one or more portions of the data from the asset, whereinthe plurality of recipients is determined from a transaction workflowfor the asset.

Referring to FIG. 278 , the data and network infrastructure pipeline27104 may have a set of marketplace entities 27802 that may beconfigured proximal to one or more intelligent resources in the set ofintelligent marketplace local resources 27506. The marketplace entities27802 may be configured as computing modules capable of beinginstantiated with or within one or more computing elements, such as themarketplace local resources 27506, a network-accessible computingdevice, a network routing element, a computing system embodying one ormore aspects of the marketplace 27106, and the like. The marketplaceentities 27802 may include entities that provide services associatedwith performing transaction workflows for one or more of the set ofassets 27102. The entities may provide services including electronicwallet services, digital twin services, enterprise database services,platform as a service, computer aided design services, video gameservices, and the like. Example embodiments of the methods and systemsfor data network and infrastructure pipeline adaptation incorporatingone or more of these services are depicted in figures and described incorresponding disclosure herein.

Referring to FIG. 279 , the set of marketplace-centric intelligentnetwork resources 27506 may facilitate connecting recipients of theasset data to the adapted networks, such as route-adapted network 27704,timing-adapted network 27710, and other networks adapted to satisfy oneor more of the transaction configurations and/or orchestration aspectsdescribed herein. The marketplace-centric intelligent network resources27506 may be disposed variously within a network, such as an enterprisenetwork, the Internet and the like. In example embodiments, resources27506 may be disposed proximal to one or more target recipients of assetdata. Further, independent of a physical location of one or more of themarketplace-centric intelligent network resources 27506 (and/orassociated control systems), resources may be configured withfunctionality that provides marketplace-centric services, optionallyutilizing computing resources of a corresponding asset data recipient,such as a computing system executing a smart contract recipient and thelike. Example locations for marketplace-centric intelligent networkresources 27506 includes locations proximal to a marketplace interface,such as a user interface through which an operator may orchestrate a setof parameters for a set of transaction workflows involving the assets.Another example location for marketplace-centric intelligent networkresources 27506 includes locations that are proximal to a processorexecuting smart contracts that include terms, conditions and parametersfor a set of transaction workflows involving the assets. Yet anotherexample location for marketplace-centric intelligent network resources27506 includes being disposed proximal to transaction workflowresources. In example embodiments, another example location forconfiguring marketplace-centric intelligent network resources 27506includes proximal to asset enabling/utilization resources, such as assetentities 27802 and the like.

In example embodiments, a recipient of asset data may include amarketplace configuration interface, a smart contract interface, and thelike as is described elsewhere herein. The set of marketplace-centricintelligent network resources 27506 may include a marketplace-centricintelligent resource 27902 that facilitates timely and efficient accessby a marketplace 27106 to asset data and/or asset-related data that maybe provided through the intermediate intelligent network resources27504. The marketplace-centric intelligent resource 27902 may coordinateaccess to asset data by an interface of a marketplace, such as aninterface through which an operator may orchestrate a set of parametersfor a set of transaction workflows involving the assets.

The set of marketplace-centric intelligent network resources 27506 mayinclude a set of workflow centric resources 27908 that may facilitatetimely and efficient access by a set of workflow resources 27910 toasset data and/or asset-related data that may be provided through theintermediate intelligent network resources 27504. The set of workflowresources 27910 may be configured with services that manage access by aworkflow for a transaction of an asset to data for the asset. In anexample, a transaction workflow resource 27910 may include a utilizationcharge associated with use of the asset associated with the transaction(e.g., miles driven for a rental car transaction). The set of workflowcentric resources 27908 may determine that this information is useful toa corresponding step in the transaction workflow (e.g., payment to anasset owner for the utilization of the asset). In example embodiments,the set of workflow centric resources 27908 may determine that thisinformation is useful by analysis of the workflow, such as through useof artificial intelligence service analysis. In example embodiments, theset of workflow centric resources 27908 may determine that thisinformation is useful based on one or more workflow parameters, such asone or more workflow parameters in a set of parameters for thetransaction workflow for the asset orchestrated by an operator (e.g., autilization parameter). The set of workflow centric resources 27908 maycoordinate with aspects of the network pipeline 27104 resources, such asa controller 27604 for an asset-localized network store 27608 in whichutilization data for the asset is stored. The set of workflow centricresources 27908 may, based on the utilization parameter for acorresponding transaction workflow may initiate configuration of autilization measure capture function to be performed by an asset localcontroller 27604 that is configured to work cooperatively with theasset, such as intelligent asset 27602. In example embodiments, the setof workflow-centric resources 27908 may further coordinate with anetwork timing determination/adaptation system 27708 to adjust a networkconfiguration for delivery of the utilization data based on requirementsin the workflow. In this example, a step in a transaction workflow mayindicate that utilization data for the asset is to be providedperiodically, such as at the end of a day to be processed by functionsof the workflow step. The set of marketplace-centric intelligentresources 27908 may indicate to the network timingdetermination/adaptation system 27708 to configure a logical connectionbetween the asset and the workflow daily at 12:02 AM to deliverutilization information for the previous day. In example embodiments,the network timing determination/adaptation system 27708 may configure alogical connection between an asset-centric intelligent resource 27502through which the asset connects to the network pipeline 27104. For thisapproach, the asset-centric intelligent resource 27502 can provide theinformation in compliance with the workflow requirements (e.g., 12:02AM) while interfacing to the asset utilizing asset-centric methods, suchas capturing utilization data at a time determined in cooperation withthe asset (e.g., at the end of a work shift that utilizes the asset),which may be different than the time specified by the workflow.

The set of marketplace-centric intelligent network resources 27506 mayinclude a set of smart contract-centric resources 27904 that mayfacilitate timely and efficient access by a set of smart contracts 27906to asset data and/or asset-related data that may be provided through theintermediate intelligent network resources 27504. In exampleembodiments, the smart contracts 27906 may include terms, conditions andparameters for a set of transaction workflows involving the assets. Foran energy storage asset, such as an electricity storage module in adistributed energy storage system, the smart contract-centric resources27904 may facilitate monitoring stored energy in a plurality of storagemodules to satisfy a condition of a smart contract identified in atransaction workflow for use of energy from the distributed energystorage system modules.

The set of marketplace-centric intelligent network resources 27506 mayinclude a set of asset delivery/use-centric resources 27912 that mayfacilitate timely and efficient access by a set of asset use/enablingresources 27914 to asset data and/or asset-related data that may beprovided through the intermediate intelligent network resources 27504.In example embodiments, adapting a route and/or timing of delivery ofdata through the network pipeline 27104 may be based on an intended useand/or deployment of an asset by a corresponding asset use/enablingresource 27914. An asset use/enabling resource 27914 may be designatedby a participant in a transaction for the asset. IN an example, amunicipal agency may be negotiating for use of stored energy. Themunicipal agency may have a contractual agreement (e.g., via a unioncontract and the like) to engage a specific fee-setting third-party foruse of stored energy supplies. Based on this arrangement, a networkpipeline 27104 may include adjusting a path for asset data to includethe specific fee-setting third-party as a recipient of the asset data.In another stored energy transaction scenario, asset use/enablingresources 27914 may include mobile equipment that is dispersed across aplurality of jurisdictions. The asset delivery/use centric resources27912 may include one or more consolidation resources (e.g., a networkinfrastructure edge computing device) that coordinate energy demand fromgroups of the dispersed resources 27914. In example embodiments, such aconsolidation resource may be configured for different jurisdictions tofacilitate compliance with resource 27914 requirements, and also withjurisdiction-specific requirements.

In example embodiments, the set of marketplace-centric intelligentnetwork resources 27506 may facilitate adaptation of a path and/ortiming in a network pipeline 27104 based on requirements of transactionworkflows. A resource providing such a service to the workflow may bedisconnected from (e.g., indirectly associated with) the asset. Furtherthe resource providing the service may be determined by and/or impactaspects of the transaction workflow. In an example, a service providedby resource X causes an impact Y on a workflow for transactions of theasset. As a result, the network pipeline 27104 may response to thisimpact by adjusting a network path for data from the asset to includeresource X.

Referring to FIG. 280 , a data and network infrastructure pipeline 27104is configured to deliver data from a set of assets 27102 to an interface27052 by which an operator orchestrates a set of parameters 27054 for aset of transaction workflows 27056 involving the assets. The data andnetwork infrastructure pipeline 27104 may include a route-determinedpath 27704 that is automatically adapted based on one or morecharacteristics of the asset data and at least one performance parameterof a (model/initial/default/random/third-party prescribed) network path.Techniques for adapting the route-determined path 27704 are depicted inthe figures of this disclosure and described elsewhere herein, includingwithout limitation FIG. 277 and its accompanying descriptions. The dataand network infrastructure pipeline 27104 may include a time-determinedpath 27710 that is automatically adapted based on one or morecharacteristics of the asset data and at least one performance parameterof an (initial/model/default/random/third-party prescribed) networkpath. Techniques for adapting the time-determined path 27710 aredepicted in the figures of this disclosure and described elsewhereherein, including without limitation FIG. 277 and its accompanyingdescriptions.

Adapting one or more of a network path or timing of delivery of datathrough the network pipeline 27104 may include use of an applicationprogramming interface associated with the operator interface 27052.

In example embodiments, the method and systems for adapting routingand/or timing for delivering data through a network pipeline 27104 mayinclude use of a digital twin platform. A digital twin platform mayinclude digital twins of assets, marketplaces, workflows, and the like.Adapting a network path through the network pipeline 27104 may includeadapting a network path to provide data to a digital twin of the asset.Adapting timing of data delivery through the network pipeline 27104 mayinclude adapting timing of delivery of asset and/or asset-related datato the digital twin of the asset.

In example embodiments, the method and systems for adapting routingand/or timing for delivering data through a network pipeline 27104 mayinclude use of robotic process automation of, for example adapting anetwork route and/or adapting timing. Use of robotic process automationmay also include developing automation of operator actions within theoperator interface 27052, such as actions that configure workflowparameters 27054.

Adapting a network path through the network pipeline 27104 may includeadapting a network path to provide data to a digital twin of the asset.Adapting timing of data delivery through the network pipeline 27104 mayinclude adapting timing of delivery of asset and/or asset-related datato the digital twin of the asset.

In example embodiments, an operator may orchestrate workflow parametersthrough the operator interface 27052. Factors that may impact workflowparameters 27054 may include an understanding of a relationship among,for example, an owner of the asset and a recipient of a transaction forthe asset. For close relationships, transaction workflow parameters27054 may be orchestrated by the operator to enable direct transfer ofthe asset and/or asset data. A corresponding adaptation a route forasset data in the network pipeline 27104 may include a route from theasset to the recipient. For indirect relationships, transaction workflowparameters 27054 may be orchestrated to include use of an escrowintermediary. A corresponding adaptation a route for asset data in thenetwork pipeline 27104 may include a route from the asset to an escrowagent and then, conditionally to the recipient.

Asset data received at the operator interface 27052 may includetransaction financial terms. The operator may, based on this data,configure workflow parameters to satisfy the financial terms. As anexample, for asset owners that accept cash, workflow parameter may beconfigured to ensure that a corresponding workflow has access to cashaccounts of the asset owner and asset buyer. In another example, forasset owners that offer funding for a transaction for the asset,workflow parameters may be configured with an indication of a financingset of steps to be included in the corresponding transaction workflow.Financing steps may include, without limitation, an asset valuationstep, a lender solicitation step, a terms negotiation step, and thelike. Another type of impact on workflow parameters may include brokeror other commissions associated with a transaction for the asset. Theoperator may, through the operator interface 27052 determine that theasset transaction includes an affiliated party, such as a broker, whomay be entitled to receive a broker's commission for the transaction.Workflow parameters 27054 for a corresponding asset transaction workflow27056 may indicate the broker and/or a commission structure.

Referring to FIG. 281 , a data and network infrastructure pipeline 27104is configured to deliver data from a set of assets 27102 to an interfacefor a set of smart contracts 27152. The smart contracts may react toasset data in the interface based on a set of terms, conditions,parameters, and the like 27154. The smart contract terms, conditions,parameters and the like may apply to a set of transaction workflows27156 involving the assets. The data and network infrastructure pipeline27104 may include a route-determined path 27704 that is automaticallyadapted based on one or more characteristics of the asset data and atleast one performance parameter of an(initial/model/default/random/third-party prescribed) network path. Thedata and network infrastructure pipeline 27104 may include atime-determined path 27710 that is automatically adapted based on one ormore characteristics of the asset data and at least one performanceparameter of an (initial/model/default/random/third-party prescribed)network path.

In example embodiments, terms of a smart contract 27152 may includecontrolled access to data from the set of assets 27102. A route in thenetwork pipeline 27104 may be adapted to prevent access to the datauntil a term impacting a workflow 27156 is satisfied. The network routemay be adapted so as to deny access to the data based on a condition ofthe workflow. The network route may be adapted so that requests toaccess the data before the condition in the workflow is met may bedeflected (e.g., rerouted) to a resource in the network that provides asuitable response, such as access is withheld due to a condition in theworkflow conditions 27154 not yet being detected by the workflow 27156.In addition to denying access to the asset data based on the unsatisfiedcondition, the network pipeline 27104 may be adapted to prevent changesto the asset data during the time that the workflow condition isunsatisfied, such as by changing privileges for the asset to restrictaccess to reading, but not writing to the asset.

In example embodiments, workflows terms 27154 regarding timing ofcompletion of one or more workflows for a transaction for an asset mayimpact at least one of a route and timing of transfer of data about orof the asset through the network pipeline 27104. A compound transactionor an electronically tradeable asset (e.g., buy and sell) may benefitfrom adjustment of the network, such as to minimize time between a buyportion of the transaction and a sell portion of the transaction to, forexample, mitigate a possibility of an impact on the asset value afterthe asset is bought and before it is sold.

Referring to FIG. 282 , methods and systems are described herein ashaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to at least one of a smart contractinterface 27152 and/or an operator interface 27052 may further have aset of application programming interfaces 27254 to a marketplace 27106that are configured to be integrated into an electronic wallet system27252. In example embodiments, interactions with a set of electronicwallet interfaces 27256 of the wallet system 27252 may automaticallytrigger a set of transaction workflows 27056 within the marketplace27106. In example embodiments, the electronic wallet system 27252 maycontrol an electronic wallet associated with an entity for settlement oftransactions. The electronic wallet system interface 27256 may functionto receive one or more signals for the electronic wallet system 27252about a transaction for the assets 27102. The marketplace API 27254 maybe configured to interface with a computing system executing and/orcontrolling the asset workflows 27056 so that, based on one or moresignals (e.g., from the marketplace indicating transaction success)optionally provided through the electronic wallet system interface27256, a workflow step (e.g., record transfer of ownership of the assetin a distributed ledger) may be automatically activated.

Referring to FIG. 283 , methods and systems are described herein ashaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to at least one of a smart contractinterface 27152 and/or an operator interface 27052 may further have aset of application programming interfaces 27254 to a marketplace 27106that are configured to be integrated into a digital twin platform 27352.In example embodiments, interactions with a set of digital twininterfaces 27356 of the digital twin platform 27352 may automaticallytrigger a set of transaction workflows 27056 within the marketplace27106. In an exemplary embodiment, the digital twin platform 27352 mayexecute a digital twin of the asset. Activity of the asset may becaptured by the asset digital twin in the digital twin platform 27352.The marketplace 27106 may interact with the asset digital twin in thedigital twin platform 27352 through the marketplace API 27254 that maybe integrated in the digital twin platform 27352. The asset digital twinmay be informed by the marketplace 27106 that the assets is presentlybeing transacted. The asset digital twin may signal, through themarketplace API 27254 to activate a step in an asset workflow 27056 thatincludes authentication of the present transaction for the asset.

Referring to FIG. 284 , methods and systems are described herein ashaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to at least one of a smart contractinterface 27152 and/or an operator interface 27052 may further have aset of application programming interfaces 27254 to a marketplace 27106that are configured to be integrated into an enterprise databaseplatform 27452. In example embodiments, interactions with a set ofenterprise database interfaces 27456 of the digital enterprise databaseplatform 27452 automatically trigger a set of transaction workflows27056 within the marketplace 27106. Through integration into anenterprise database platform 27452, the methods and systems of networkpipeline 27104 adaptation for routing and/or timing may enableintegration into business applications, methods and processes of theenterprise. In example embodiments, business workflows, such asinventory replenishment may include a workflow step that signals to theenterprise database platform 27452 (e.g., through the set of enterprisedatabase interfaces 27456) to conduct a transaction for initiating thereplenishment. Through the set of application programming interfaces27254, the marketplace workflows 27056 a transaction may be conducted(and or an existing transaction may be continued), such as releasing anorder for inventory material from a supplier.

Referring to FIG. 285 , methods and systems are described herein ashaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to at least one of a smart contractinterface 27152 and/or an operator interface 27052 may further have aset of application programming interfaces 27254 to a marketplace 27106that are configured to be integrated into a platform as a serviceplatform 27552. In example embodiments, interactions with a set ofservice platform interfaces 27556 of the platform as a service platform27552 automatically trigger a set of transaction workflows 27056 withinthe marketplace 27106. In this example embodiment, the platform as aservice platform 27552 may receive a request for performing a platformservice through the set of service platform interfaces 27556. Responsiveto this request, the platform as a service platform may indicate,through the set of marketplace APIs 27254 to activate a transactionworkflow 27056 that correlates to a type of service indicated in therequest.

Referring to FIG. 286 , methods and systems are described herein ashaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to at least one of a smart contractinterface 27152 and/or an operator interface 27052 may further have aset of application programming interfaces 27254 to a marketplace 27106that are configured to be integrated into a computer aided designplatform 27652. In example embodiments, interactions with a set of CADinterfaces 27656 of the computer aided design platform 27652automatically trigger a set of transaction workflows 27056 within themarketplace 27106. In example embodiments, a computer aided designplatform 27652 may represent a resource through which an asset-specifictransaction workflow might be fulfilled, such as designing the asset,designing aspects of a deployment environment for the asset, and thelike. In example embodiments, the computer aided design platform 27652may perform automated design using a set of criteria provided through,for example the set of CAD interfaces 27656. The automated design mayinclude following a set of workflow steps for producing the automateddesign that are based on information provided from the computer aideddesign platform 27652 through the marketplace APIs 27254 to a workflowselector mechanism for adapting a workflow to fulfill the designrequirement.

Referring to FIG. 287 , methods and systems are described herein ashaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to at least one of a smart contractinterface 27152 and/or an operator interface 27052 may further have aset of application programming interfaces 27254 to a marketplace 27106that are configured to be integrated into a video game 27752. In exampleembodiments, interactions with a set of game interfaces 27756 of thevideo game 27752 automatically trigger a set of transaction workflows27056 within the marketplace 27106. Integration of a set of marketplaceAPIs AP1204 into a video game 27752 may facilitate presentation of setsof assets 27102 available for transaction in the marketplace 27106 in auser interface of the video game 27752. As an example, during use of thevideo game 27752 a user may identify an asset to be transacted, such asto be purchased in the marketplace 27106 by the user. Through use of themarketplace APIs 27254, a workflow of the marketplace may be activatedto fulfill a transaction for the asset on behalf of the game user.

Methods and systems of a route and timing adaptable network pipeline fordelivering data from an asset may include a data and networkinfrastructure pipeline 27104 that is configured to deliver data from aset of assets 27102 to an interface 27052 by which an operatororchestrates a set of parameters 27054 for a set of transactionworkflows 27056 involving the assets, wherein the pipeline 27104 isautomatically configured to adjust a network path 27704 based on thecharacteristics of the data and at least one performance parameter ofthe network path.

Methods and systems of a route and timing adaptable network pipeline fordelivering data from an asset may include a data and networkinfrastructure pipeline 27104 that is configured to deliver data from aset of assets 27102 to a set of smart contracts 27906 that includeterms, conditions and parameters 27154 for a set of transactionworkflows 27156 involving the assets, wherein the pipeline 27104 isautomatically configured to adjust a network path 27704 based on thecharacteristics of thedata/contracts/terms/conditions/parameters/workflows/assets and at leastone performance parameter of the network path. In example embodiments, apath may be changed based on meeting one or more conditions of the smartcontract. In a production system that produces one or more assets in theset of assets 27102, meeting one or more conditions, such as a level ofproduction quality, may satisfy a quality condition regarding use of aquality monitoring service (e.g., auditing quality of a productionsystem). Based on achieving a level of production quality, sample assets(and/or quality data for assets) may no longer be routed to the qualityauditing service/network resource. This change in path of asset data maybe affected by the method and systems for network pipeline 27104operation as described herein.

Methods and systems of a route and timing adaptable network pipeline fordelivering data from an asset may include a data and networkinfrastructure pipeline 27104 that is configured to deliver data from aset of assets 27102 to an interface 27052 by which an operatororchestrates a set of parameters 27054 for a set of transactionworkflows 27056 involving the assets, wherein the pipeline 27104 isautomatically configured to adjust timing of data delivery based on atleast one of a transaction parameter and a network performanceparameter. A network performance parameter may include an expecteddelivery timing for data delivery, a range of data delivery timing fordelivering the asset, and the like. A transaction parameter may includea target data delivery time frame, a maximum data delivery time (so thatdata is received timely), a minimum data delivery time (so that data isreceived after a minimum time, such as after a specific date/time, andthe like).

Methods and systems of a route and timing adaptable network pipeline fordelivering data from an asset may include a data and networkinfrastructure pipeline 27104 that is configured to deliver data from aset of assets 27102 to set of smart contracts 27906 that include terms,conditions and parameters 27154 for a set of transaction workflows 27156involving the assets, wherein the pipeline 27104 is automaticallyconfigured to adjust timing of data delivery based on at least one of atransaction parameter and a network performance parameter.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems having a data and network infrastructure pipelinethat is configured to deliver data from a set of assets to an interfaceby which an operator orchestrates a set of parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust a network path based on thecharacteristics of the data and at least one performance parameter ofthe network path. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust a network path based on the characteristics of the data and atleast one performance parameter of the network path and having a dataand network infrastructure pipeline that is configured to deliver datafrom a set of assets to set of smart contracts that include terms,conditions and parameters for a set of transaction workflows involvingthe assets, wherein the pipeline is automatically configured to adjust anetwork path based on the characteristics of the data and at least oneperformance parameter of the network path. In embodiments, such methodsand systems are provided having a data and network infrastructurepipeline that is configured to deliver data from a set of assets to aninterface by which an operator orchestrates a set of parameters for aset of transaction workflows involving the assets, wherein the pipelineis automatically configured to adjust a network path based on thecharacteristics of the data and at least one performance parameter ofthe network path and having a data and network infrastructure pipelinethat is configured to deliver data from a set of assets to an interfaceby which an operator orchestrates a set of parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust timing of data delivery based on atleast one of a transaction parameter and a network performanceparameter. In embodiments, such methods and systems are provided havinga data and network infrastructure pipeline that is configured to deliverdata from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust a network path based on the characteristics of the data and atleast one performance parameter of the network path and having a dataand network infrastructure pipeline that is configured to deliver datafrom a set of assets to set of smart contracts that include terms,conditions and parameters for a set of transaction workflows involvingthe assets, wherein the pipeline is automatically configured to adjusttiming of data delivery based on at least one of a transaction parameterand a network performance parameter.

In embodiments, such methods and systems are provided having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to an interface by which an operator orchestrates a setof parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path and having a set ofapplication programming interfaces 27254 to a marketplace 27106 that areconfigured to be integrated into an electronic wallet system 27252, suchthat interactions with a set of interfaces 27256 of the wallet systemautomatically trigger a set of transaction workflows 27056 within themarketplace 27106. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust a network path based on the characteristics of the data and atleast one performance parameter of the network path and having a set ofapplication programming interfaces 27254 to a marketplace 27106 that areconfigured to be integrated into a digital twin platform 27352, suchthat interactions with a set of interfaces 27356 of the digital twinplatform 27352 automatically trigger a set of transaction workflows27056 within the marketplace 27106. In embodiments, such methods andsystems are provided having a data and network infrastructure pipelinethat is configured to deliver data from a set of assets to an interfaceby which an operator orchestrates a set of parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust a network path based on thecharacteristics of the data and at least one performance parameter ofthe network path and having a set of application programming interfaces27254 to a marketplace 27106 that are configured to be integrated intoan enterprise database platform 27452, such that interactions with a setof interfaces 27456 of the enterprise database platform 27452automatically trigger a set of transaction workflows 27056 within themarketplace 27106. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust a network path based on the characteristics of the data and atleast one performance parameter of the network path and having a set ofapplication programming interfaces 27254 to a marketplace 27106 that areconfigured to be integrated into a platform-as-a-service platform 27552,such that interactions with a set of interfaces 27556 of theplatform-as-a-service platform 27552 automatically trigger a set oftransaction workflows 27056 within the marketplace 27106. Inembodiments, such methods and systems are provided having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to an interface by which an operator orchestrates a setof parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path and having a set ofapplication programming interfaces 27254 to a marketplace 27106 that areconfigured to be integrated into a computer-aided design platform 27652,such that interactions with a set of interfaces 27656 of thecomputer-aided design platform 27652 automatically trigger a set oftransaction workflows 27056 within the marketplace 27106. Inembodiments, such methods and systems are provided having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to an interface by which an operator orchestrates a setof parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path and having a set ofapplication programming interfaces 27254 to a marketplace 27106 that areconfigured to be integrated into a video game 27752, such thatinteractions with a set of interfaces 27756 of the video game 27752automatically trigger a set of transaction workflows 27056 within themarketplace 27106.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems having a data and network infrastructure pipelinethat is configured to deliver data from a set of assets to set of smartcontracts that include terms, conditions and parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust a network path based on thecharacteristics of the data and at least one performance parameter ofthe network path. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to set of smart contracts that includeterms, conditions and parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust a network path based on the characteristics of the data and atleast one performance parameter of the network path and having a dataand network infrastructure pipeline that is configured to deliver datafrom a set of assets to an interface by which an operator orchestrates aset of parameters for a set of transaction workflows involving theassets, wherein the pipeline is automatically configured to adjusttiming of data delivery based on at least one of a transaction parameterand a network performance parameter. In embodiments, such methods andsystems are provided having a data and network infrastructure pipelinethat is configured to deliver data from a set of assets to set of smartcontracts that include terms, conditions and parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust a network path based on thecharacteristics of the data and at least one performance parameter ofthe network path and having a data and network infrastructure pipelinethat is configured to deliver data from a set of assets to set of smartcontracts that include terms, conditions and parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust timing of data delivery based on atleast one of a transaction parameter and a network performanceparameter. In embodiments, such methods and systems are provided havinga data and network infrastructure pipeline that is configured to deliverdata from a set of assets to set of smart contracts that include terms,conditions and parameters for a set of transaction workflows involvingthe assets, wherein the pipeline is automatically configured to adjust anetwork path based on the characteristics of the data and at least oneperformance parameter of the network path and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into an electronic wallet system, such thatinteractions with a set of interfaces of the wallet system automaticallytrigger a set of transaction workflows within the marketplace. Inembodiments, such methods and systems are provided having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to set of smart contracts that include terms, conditionsand parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a digital twin platform, such that interactionswith a set of interfaces of the digital twin platform automaticallytrigger a set of transaction workflows within the marketplace. Inembodiments, such methods and systems are provided having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to set of smart contracts that include terms, conditionsand parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust a networkpath based on the characteristics of the data and at least oneperformance parameter of the network path and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into an enterprise database platform, such thatinteractions with a set of interfaces of the enterprise databaseplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to set of smart contracts that includeterms, conditions and parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust a network path based on the characteristics of the data and atleast one performance parameter of the network path and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a platform-as-a-service platform, such thatinteractions with a set of interfaces of the platform-as-a-serviceplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to set of smart contracts that includeterms, conditions and parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust a network path based on the characteristics of the data and atleast one performance parameter of the network path and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a computer-aided design platform, such thatinteractions with a set of interfaces of the computer-aided designplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to set of smart contracts that includeterms, conditions and parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust a network path based on the characteristics of the data and atleast one performance parameter of the network path and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a video game, such that interactions with a set ofinterfaces of the video game automatically trigger a set of transactionworkflows within the marketplace.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems having a data and network infrastructure pipelinethat is configured to deliver data from a set of assets to an interfaceby which an operator orchestrates a set of parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust timing of data delivery based on atleast one of a transaction parameter and a network performanceparameter. In embodiments, such methods and systems are provided havinga data and network infrastructure pipeline that is configured to deliverdata from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust timing of data delivery based on at least one of a transactionparameter and a network performance parameter and having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to set of smart contracts that include terms, conditionsand parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust timing ofdata delivery based on at least one of a transaction parameter and anetwork performance parameter. In embodiments, such methods and systemsare provided having a data and network infrastructure pipeline that isconfigured to deliver data from a set of assets to an interface by whichan operator orchestrates a set of parameters for a set of transactionworkflows involving the assets, wherein the pipeline is automaticallyconfigured to adjust timing of data delivery based on at least one of atransaction parameter and a network performance parameter and having aset of application programming interfaces to a marketplace that areconfigured to be integrated into an electronic wallet system, such thatinteractions with a set of interfaces of the wallet system automaticallytrigger a set of transaction workflows within the marketplace. Inembodiments, such methods and systems are provided having a data andnetwork infrastructure pipeline that is configured to deliver data froma set of assets to an interface by which an operator orchestrates a setof parameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust timing ofdata delivery based on at least one of a transaction parameter and anetwork performance parameter and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a digital twin platform, such that interactions with aset of interfaces of the digital twin platform automatically trigger aset of transaction workflows within the marketplace. In embodiments,such methods and systems are provided having a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to an interface by which an operator orchestrates a set ofparameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust timing ofdata delivery based on at least one of a transaction parameter and anetwork performance parameter and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into an enterprise database platform, such that interactionswith a set of interfaces of the enterprise database platformautomatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust timing of data delivery based on at least one of a transactionparameter and a network performance parameter and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a platform-as-a-service platform, such thatinteractions with a set of interfaces of the platform-as-a-serviceplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust timing of data delivery based on at least one of a transactionparameter and a network performance parameter and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a computer-aided design platform, such thatinteractions with a set of interfaces of the computer-aided designplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to an interface by which an operatororchestrates a set of parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust timing of data delivery based on at least one of a transactionparameter and a network performance parameter and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a video game, such that interactions with a set ofinterfaces of the video game automatically trigger a set of transactionworkflows within the marketplace.

In embodiments, provided herein are computer-implemented methods andsystems for automated orchestration of one or more marketplaces, suchmethods and systems having a data and network infrastructure pipelinethat is configured to deliver data from a set of assets to set of smartcontracts that include terms, conditions and parameters for a set oftransaction workflows involving the assets, wherein the pipeline isautomatically configured to adjust timing of data delivery based on atleast one of a transaction parameter and a network performanceparameter. In embodiments, such methods and systems are provided havinga data and network infrastructure pipeline that is configured to deliverdata from a set of assets to set of smart contracts that include terms,conditions and parameters for a set of transaction workflows involvingthe assets, wherein the pipeline is automatically configured to adjusttiming of data delivery based on at least one of a transaction parameterand a network performance parameter and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into an electronic wallet system, such that interactions witha set of interfaces of the wallet system automatically trigger a set oftransaction workflows within the marketplace. In embodiments, suchmethods and systems are provided having a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to set of smart contracts that include terms, conditions andparameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust timing ofdata delivery based on at least one of a transaction parameter and anetwork performance parameter and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into a digital twin platform, such that interactions with aset of interfaces of the digital twin platform automatically trigger aset of transaction workflows within the marketplace. In embodiments,such methods and systems are provided having a data and networkinfrastructure pipeline that is configured to deliver data from a set ofassets to set of smart contracts that include terms, conditions andparameters for a set of transaction workflows involving the assets,wherein the pipeline is automatically configured to adjust timing ofdata delivery based on at least one of a transaction parameter and anetwork performance parameter and having a set of applicationprogramming interfaces to a marketplace that are configured to beintegrated into an enterprise database platform, such that interactionswith a set of interfaces of the enterprise database platformautomatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to set of smart contracts that includeterms, conditions and parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust timing of data delivery based on at least one of a transactionparameter and a network performance parameter and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a platform-as-a-service platform, such thatinteractions with a set of interfaces of the platform-as-a-serviceplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to set of smart contracts that includeterms, conditions and parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust timing of data delivery based on at least one of a transactionparameter and a network performance parameter and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a computer-aided design platform, such thatinteractions with a set of interfaces of the computer-aided designplatform automatically trigger a set of transaction workflows within themarketplace. In embodiments, such methods and systems are providedhaving a data and network infrastructure pipeline that is configured todeliver data from a set of assets to set of smart contracts that includeterms, conditions and parameters for a set of transaction workflowsinvolving the assets, wherein the pipeline is automatically configuredto adjust timing of data delivery based on at least one of a transactionparameter and a network performance parameter and having a set ofapplication programming interfaces to a marketplace that are configuredto be integrated into a video game, such that interactions with a set ofinterfaces of the video game automatically trigger a set of transactionworkflows within the marketplace.

Cross-Market Transaction Engine

There is a huge proliferation of data resulting from the IoT, edge anddistributed storage and computation, but more data is not useful if thedata overwhelms storage systems, networks for transmission, or humanoperators with too much noise to be useful. Referring to FIGS. 288 and289 in embodiments, the platform 100 may include a cross-markettransaction engine 28800 configured to enable markets 28802, marketplatforms 28804, buyers 28806, and sellers 28808 participating in a widevariety of markets to execute transactions and share data viaintelligent agents and intelligent data layers 25904. The engine 28800may discover new data source feeds (including alternative data likecrowdsourced data, IoT and sensor data, edge and cloud, and websites),automatically ingest, cleanse, and normalize the feeds such that theycan be processed, organize storage of the data (includingself-organization based on feedback on outcomes, such as utilization,impact, for example whether new data helps AI perform better, etc.),deduplicate and/or prune the data, summarize or compress the data (suchas to optimize storage and/or network transmission), handle accesscontrols, including role-based permissions and appropriate encryption orobfuscation), as well as handling permissions in multi-tenancysituations, perform opportunity mining (such as by testing the impact ofadding new data sources on the performance of AI and expert systems),factor in the cost/benefit of adding the data source, performnetwork-condition-sensitive data acquisition and storage (such asassisting with not collecting more sensitive data than one can transmit,collecting data at the right time of day to transmit, etc.), and/orperform intelligent data location (such as putting the data where thedata belongs for an AI model to operate on the data, e.g., as where alocal AI system can operate a market function with very low latency onthe local data that is “good enough” to get to a good answer, such asthe right price for a bid/ask situation. The engine 28800 may perform orfacilitate performing one or more of reducing data-sharing risk,securing data provenance, facilitating transaction autonomy, drivingecosystem interoperability, securely augmenting AI, translating value,and parking value. The engine 28800 may reduce data-sharing risk bycreating combined sources of information that can be queried andanalyzed without providing all users with full access to the underlyingdata sources or otherwise revealing the underlying data, for example viadistributed data storage and associated technologies (e.g., cloud,distributed ledgers, smart contracts, blockchains, and access controlsystems) with privacy enhancing techniques (PETs). This may includeproviding aggregated results that do not provide access to individualdata elements (such as to protect the privacy of a user or individualproprietary units), providing obfuscated results, providing summaries,providing partial results (e.g., reduced-granularity image or sensordata), and/or tuning results to roles, permissions, or other parametersof a requesting party or system.

The engine 28800 may secure data provenance by facilitating creationand/or management of a blockchain-based distributed ledger, therebycreating immutable sources of information that allow for tracing theprovenance of information (e.g., data from IoT devices sent over 5Gnetworks), more easily reconciling data with collaborators, andminimizing the risk of information being doctored by malicious actors.Data provenance may also be facilitated by embedding or wrapping dataelements in metadata that indicates or validates provenance, such as adigital fingerprint or signature that is unique to the data source(e.g., wrapping sensor data for a time period with a digital signaturethat embeds a unique characteristic of the device or system that wasused to capture the data, the environment in which it was captured, oran item about which the data was collected).

The engine 28800 may facilitate transaction autonomy by facilitatingcreation and/or management of smart contacts on one or more distributedledgers, thereby reducing the effort spent manually reconcilingagreements and processing transactions (as they happen automatically)and allowing for real-time and autonomous payments to flow to differenttypes of partners upon completion of specific contractual terms.Transaction autonomy may also be facilitated by a set of autonomousagents, such as robotic process automation systems that are trained on aset of user actions (such as a training set of user interactions withone or more software systems that are used to enable transactions), suchthat elements of a transaction that historically required manual orhuman interactions are undertaken by the agents.

The engine 28800 may drive ecosystem interoperability by standardizingvarious connectivity methods (e.g., Open API initiative, NACHA's APIstandardization), outsourcing to as-a-service providers (e.g., thoseaccessible via the cloud), sending financial instructions, and/orembedding products into non-financial contexts.

The engine 28800 may securely augment AI by employing privacy-enhancingtechniques to allow AI models to be trained (using task-specifichardware) on sensitive information without exposing training, which maybe beneficial, for instance, in creating collective transactionmonitoring models without exposing sensitive transaction data tocompetitors. The privacy-enhanced training data communication allows forbuilding robust artificial intelligence models using large amounts ofdata from partners without compromising privacy and security. AI modelsmay be augmented by a governance system, such as one that governs thetraining data set and/or a set of outcomes that results from applicationof the AI to the training data set, such as to avoid replicating bias intraining data sets in the outcomes of models, to provide expert checksand balances on automated systems, to ensure compliance with regulations(such as privacy regulations, data location regulations, and operationalregulations that apply to activities that are undertaken based on theoutputs of the AI models).

The engine 28800 may facilitate translation and parking of value byproviding a plurality of markets with access to and sharing oftechnologies that provide insight into the “right” exchange rate, e.g.,between native currencies of respective markets and exchange ratesacross markets, as well as provide insight into the value (as expressedin various units of value) of in-market entities like data, lifetimevalue of a customer, cost to acquire a customer, targeted advertisingrates, goods, services, credits, offsets, tokens, loyalty points,collateral, labor, work products, credit costs (borrowing or lending),and many others. The engine 28800 provides such exchanges with bettersolutions that can be enabled by more/richer data (including inintelligent data layers), better automation (including using agents andmodels that are trained on data and that use AI), and/or better analyticand AI systems that facilitate insight into transactions, workflows,value-creation and other elements of a successful marketplace.

In embodiments, the engine 28800 may facilitate one or more processes bywhich market values of a set of tokens (such as NFTs, fungible tokens,or others) may provide a translation function between markets. The tokenmarket values may be measured according to one or more cryptocurrencies,fiat currencies, points systems (e.g., rewards or loyalty points), or acombination thereof. For example, value of a painting may be measuredversus value of an event ticket by their respective cost as reflected inthe same token, cryptocurrency, or fiat currency. In embodiments, a setof tokens may be designed to provide better insight about exchange valuethan traditional currency or generalized cryptocurrency, such as where atoken is managed by a set of market orchestration rules and/or smartcontracts that take real-time data from a set of market entities thatindicate value. In embodiments, this may include populating the systemsthat govern a token with data from a digital twin that reflects thecurrent or real-time condition of a set of real-world assets, such asbased on sensor data, human-entered observational data, or any otherdata.

In embodiments, the engine 28800 may facilitate one or more processes bywhich a parameterized smart contract may be configured to define one ormore value ranges for in-kind exchanges across markets. The engine 28800may periodically spider available marketplaces to find a preferred setof potential exchanges.

In embodiments, the engine 28800 may be configured to embed financialproducts in non-financial contexts, and/or embed non-financial productsin financial contexts. Non-financial players can be incentivized tocreate exclusive arrangements to embed only a particular institution'sproduct, giving them direct access to captive customer pools andexclusive data. This addresses a challenge that financial institutionsface when financial products are embedded into a third-party platform,in which case the institution may risk losing direct customertouchpoints, limiting the ability to build deeper relationships (e.g.,advisory relationships between a bank and its high-value customers). Inembodiments, advances in connectivity technology and standardizationallow financial products to be natively integrated into non-financialcontexts. For instance, a financial product may be deliveredsimultaneously with a non-financial one (e.g., parametric insurance maybe built into a home purchase contract) or offered natively on theplatform of a partner (e.g., gig-work app making short-term loans).Examples include machinery as a service, printing as a service, andother subscription models. Integration or embedding of financial andnon-financial products and services with or into each other may beimproved by combination with any of the elements described herein, suchas: access to a set of intelligent data layers that provide automated,high-quality, real-time data to parameterize financial or non-financialdata, such as to inform a smart contract relating to the embedded orintegrated offering; presentation of offerings in improved interfaces orformats, such as digital twins, enhanced wallets; augmented, mixed, orvirtual reality systems; and/or digital transaction interfaces embeddedin or on physical goods or at a point-of-sale; and/or automation,orchestration and/or optimization (such as by AI) of offer andacceptance, contract terms, fulfillment, and other workflows involved ina transaction.

In embodiments, the engine 28800 may be configured to embed data aboutphysical processes into financial products to improve risk and valueassessment, assure the identity of transaction participants, validateprovenance of physical information, and optimize product and informationdistribution. This may include sensor data, data collected frominfrastructure elements (such as the environment of storage,transportation, sale or use of a product), data collected from operatorsor observers, market data (including reviews, surveys, ratings andrecommendations), demographic data, and many others as disclosedthroughout this disclosure and the documents incorporated herein byreference.

The engine 28800 may embed data about financial products into physicalproducts, infrastructure, and processes. For example, in a projectfinance or reinsurance transaction, one or more parties can be informedby performance metrics and associated costs related to real assets. Theengine 28800 may employ a blockchain to circumvent and democratizeproject finance and insurance. As such, the engine 28800 may provideinsurance and project planning to one or more parties, thereby supplyingreal data that can be used to determine risk and risk management, e.g.,as part of a reinsurance mechanism.

In embodiments, the engine 28800 may be configured to validate and/orauthenticate data or specific types of software code beforeincorporating the data with a larger data set (e.g., personal data) oradding the code to a platform (e.g., validating software code). With theincreased security issues such as identity theft and use of false onlineidentities as well as malicious actors trying to hack or input viruses,there is a need for validation provenance of physical information. Thereis a need to use various external information (from user associateddata, user's device associated data, or external data related toparticular user). External data can be, for example, various personaldata at any level such as home address, phone number, car, where is userborn, SSN, etc. External information may be biometric. Such examplesinclude, but are not limited to fingerprint, palm veins, facerecognition, DNA, palm print, hand geometry, iris recognition, retinaand odor/scent. The engine 28800 may identify information to be usedfrom users and/or user devices randomly. The information may be acombination of both user data and user device data. Process ofauthentication may be checked frequently from users either randomly orfor each transaction, thereby potentially eliminating the ability for arobot (e.g., script, program, RPA, AI, and the like) to falsify userdata to be incorporated, e.g., in cases where the robot cannot bevalidated and, and may eliminate the robot user from attempting tocommunicate with the system, e.g., indefinitely.

By way of example, the engine 28800 may be configured to provideautomobile dealers with data associated with vehicle maintenance,service intervals, costs, etc. based in part on real-time or downloadeddata. The models may be fed into vehicle pricing, warranty costs, andrisk assessments, extended warranty offerings, etc. conducted by automanufacturers and dealers. Similar models can be applied to anyconnected device/product, where specific cost, types of risk, use cases,user profiles, and similar data and analysis models can be applied andpriced to offer customized services that are much better than thestandard question at the cash register and/or at an electronic point ofsale, such as extended warranties, insurance options, and the like.

By way of another example, the engine 28800 may use embedded data tofacilitate the availability of reduced auto insurance rates based ondriving habits. The engine may make available and/or create independentdata streams, and/or develop independent risk assessments and costmodels, which in turn may create a new market and/or enhance existingmarkets warranty, purchase negotiation, insurance, and other services,as well as making these services more transparent. The engine 28800 mayapply datasets and models to nested products or parts for more accuratecost and risk models, and may, in embodiments, add in customer inputdata collected from connected devices for richer data.

The engine 28800 may provide notifications to customers for maintenance,upgrades, services, etc. based on real-time status of theproduct/device. The engine 28800 may present and/or visualize options ascost-tradeoffs (e.g., risk, cost of replacement, etc.) associated with aproduct or service. The engine 28800 may provision related smartcontracts with product users based on how a product is maintained, used,e.g., the type of data supplied, etc. Also, the engine 28800 may providetransparent consumer or other product ratings.

In embodiments, the engine 28800 may be configured to provide trustlessdisjointed data pools and/or disjointed data pools that meet high trustdemand. Shifts in attitudes towards data privacy are causing areassessment of the major institutions and a growing number of dataregulations are requiring institutions to give back control overinformation to consumers. This creates an explicit trust gap that willdrive fundamental reshaping of how individuals and businesses managetheir data and creates an open opportunity about who may help themsimplify and orchestrate this management: generally financialinstitutions are well-poised to play this role. Other than in atrustless environment (e.g., DLT) a trusted intermediary (e.g.,financial institution) with high security standards will have a distinctadvantage by helping customers manage access to disjointed (i.e., heldwith multiple different parties), highly sensitive data. Examplesinclude consumer banks increasingly integrating with other apps andpersonalization services

In embodiments, the engine 28800 may be configured to provide proxies,i.e., “back doors,” into data sources. ISPs, exchanges, etc. will lookfor “back doors” or proxies to data sources that might becomeincreasingly regulated and/or customer controlled. For example, theright collection of IoT data sources, aggregated intelligently, mighttell one as much about a customer's behavior and interests as wouldhistorical data sets like surveys and POS data.

In embodiments, the engine 28800 may develop ways to automaticallycalculate performance metrics of a particular product based not only onits use, but how it is used in conjunction with other connected productsin a home or personal environment. The engine 28800 may thereby producea rich and valuable set of available data which can be part of anavailable data stream subscription asset.

In embodiments, the engine 28800 may facilitate gathering, sharing,marketing, transacting for, and/or securing aggregate and/or individualmedical data. Overall healthcare relies on this data; for example,testing results with COVID are widely available through the test labs.This information has been central to the management of the deployment oftreatment and disease management programs. For example, the engine 28800may facilitate analysis of outliers for vaccine administration. Thereare small numbers of people who may develop side effects, and theseindividuals need careful medical study to discover if there may be cluesto other risk factors. Individuals may share health information and/orbecome part of a study to find clues to additional risk factors. Theengine 28800 may facilitate acquisition of HIPAA compliance and otherprivacy concerns

In embodiments, the engine 28800 may facilitate gathering, sharing,marketing, transacting for, and/or securing data used to determineeffectiveness of a treatment program.

In embodiments, the engine 28800 may facilitate using pharmaceutical,supplements, devices, sales, demand data, etc. in geographic area toinfer protected health data for a population. For example, if an areahas increased insulin sales, it can be assumed that diabetes cases arehigher in that area than average areas. The data may be obtained frommanufacturers (e.g., via quarterly reports). Other types of data may beindicative of overall health of a population or demographic, such asprofitability or revenue of “bad” for health products v. “good” forhealth products. For example, alcohol, nicotine, fast food, streamingservices, etc. v. health food stores, gym memberships, etc. The engine28800 may implement machine learning, AI, and/or intelligent agents toidentify more latent products that correlate with “good” and “bad”health outcomes and use the identifications to infer health data ofpopulations.

In embodiments, the engine 28800 may facilitate gathering, managing,and/or transacting of personal location data from cell phone traces.Personal location information can be highly sensitive, and increasinguse of that data by, for example, social media communities, may lead toa counter-response to further limit use of that data. If cell phonelocation tracking data becomes constrained, or even barred, for somepurposes, proxies may be needed for a wide range of location-basedapplications. The engine 28800 may create and/or manage proxiesincluding one or more of ride sharing, delivery services, location-basedpromotions and advertising, navigation, routing, and the like. Proxiesfor the data may include aggregation and averaging of location dataacross time or groups of people, such as to provide a statisticalprediction of the most likely location. The engine 28800 may determinelocations by infrastructure, such as image classifiers, such as forrecognizing public location events by camera, while keeping privatelocation information secret. The engine 28800 may gather, analyze,and/or disseminate transaction data and/or metrics related thereto toprovide insight into patterns of location, such as in-store purchases.Data about job location, school location, and the like can be used topredict location, including based on patterns.

In embodiments, the engine 28800 may facilitate managing and/ortransacting for data from a visual edge and/or data inferred fromcameras. The engine 28800 may gather information from camera data andextract and use the data without revealing underlying pictures. The datamay be abstracted for specific purposes, thereby preserving privacy orregulated data and information. Cameras are everywhere and the abilityto sell abstracted data may be used by users for, for example, trucksleaving warehouses, patrons visiting retail locations, foot traffic,assets moving inside facilities or mining/construction sites.

In embodiments, the engine 28800 may facilitate managing and/ortransacting for universal data contracts that apply to online servicescompanies.

In embodiments, the engine 28800 may include an obfuscation module 28810configured to obfuscate aggregated, anonymized data. What is consideredanonymized or blinded data is likely to change, and narrow, in the nearfuture because data that has historically been blinded in isolation(such as by removing personally identifiable information [PIT]), is nowincreasingly used in combination with other data sources, and whencombined with other data, the aggregate contains sufficient information,and is so granular in some respects, that the anonymity is lost. Forexample, location data, occupational data, age data, and the like for agroup of individuals may be anonymized, such as by removing names, birthdates, and the like; however, at some point the threshold is crossedwhen there is probabilistically, for example, only a single lawyer atlocation X who could have traveled from Y, is age Z and so forth, sothat determining personal information via aggregated data can beaccomplished by ascribing to that single lawyer all of the attributescontained in the aggregated data set for lawyers at location X, from Yof age Z, etc. This has lots of implications for basic privacy lawcompliance, such as with respect to HIPAA, European data privacy laws,and many others. For example, data brokerage practices are likely tochange, as data brokers may run afoul of privacy regulations dependingon how they are aggregating “anonymous” data. In embodiments, this maybe handled by machine learning and other tools (trained by deep learningon outcomes, learning on labels or feedback, supervised learning,semi-supervised learning, training on expert human interactions (such asusing robotic process automation, or other techniques), such as toautomatically undertake under supervision, or to recommend how to do oneor more of the following: partition data into partitions that satisfythreshold requirements for maintenance of anonymity, segregate sets ofdata or otherwise prevent combination of such data sets (or elementstherein) to the extent that the data set tends to expose informationthat is required to be kept private; aggregate data sets into largerpools from which it is more difficult to infer private information; setthresholds of granularity (such as location groupings) at sufficientlylow settings such as to make it sufficiently difficult to infer specificinformation that is required to be held private; test data sets (such aswith respect to parameters of aggregation, granularity, partitioning,and the like) to produce metrics of security and/or vulnerability toprivacy invasion; introduce elements that increase uncertainty as towhether individual information can be correctly inferred (such asfictional names, occupations and the like that are unlikely to diminishperformance by systems that are undertaking appropriate uses of the dataset, but that would tend to interfere with uses that are seeking toinfer private information); and the like. These actions can be appliedto various data sets, data pools and types that containprivacy-protected information or that should not be combined for otherreasons. In embodiments, a set of methods, systems and models may beprovided, which in each case may be used as a foundation or seed fordevelopment of a set of automated agents (such as through roboticprocess automation), to undertake various actions that may support orenable more sophisticated and/or compliant usage of aggregated data,such as to: predict what missing data sets or types would be needed inorder to approximate, or to directly infer identifying information orother sensitive attributes of an individual person, a type of person ofinterest, or the like; provide a set of incentives, or presentappropriate disclosures, terms, conditions and the like, in order toinduce a person of interest to consent to inclusion of personal data indata sets and/or to consent to aggregation of personal data into largerdata sets; refine or regulate usage of aggregate data sets, andpermissions thereof, such as to refine for what purposes aggregate datamay be used when there is some risk of inference of personal or privateinformation (such as where aggregate data is permitted for medicalresearch, but not for marketing); and others.

Increasingly, a wide variety of activities and systems involvingpersonal data sources, such as targeted advertising, workflows involvingconsumer IDs, web cookie acceptance, location data gathering/retention,health care activities, human resources activities, website utilization,online personal data gathering, surveys and research activities, aresubject to regulation. The EU has been leading the world in passingregulations governing which types of data businesses can collect. Sometypes of data are allowed with consent by the consumer, while others(such as data regarding minors) are prohibited altogether or requireconsent by a parent or guardian. Businesses and advertisers may usegamification, promotional offers and/or reward/loyalty programstrategies to incentivize consumers to give consent to having theirbrowsing habits tracked by an ad/consumer ID and/or have data alreadylinked to their ad/consumer ID be used to modify their web browsingand/or app using experience. For example, a consumer may be offeredcredits for browsing one or more websites while having personalized datacollection enabled. These credits may be redeemable by the consumer fordigital and/or physical goods. Businesses and/or websites maycollaborate with one another to offer unified incentive systems acrosseach of them.

Further examples of data which may be obfuscated via the obfuscationmodule 28810 may include: the signature of power usage acrosshome/business/enterprise premises and/or systems from the panel (such aswith the ability to see each appliance/computer/AC-system/generator comeonline, such as based on the signature of these premises and/or systemactivities existing in the waveform of AC consumption relative to otherunits); wearable data (such as by using augmenting data frominfrastructure and cameras to serve as proxies for activity and energy,etc.); ethnicity and/or race data (which may be used to anonymouslyidentify these groups of people and trends related to transactions theypursue/involved in and/or how they may be impacted by transactions in avariety of fields); political affiliation and/or religious/philosophicalaffiliation data (such as by determining whether associations betweenthese groups of people relate to any number of viewpoints frompolitical/religious/philosophical affiliations); social media data (suchas by using one or more AI agents to classify or otherwise characterizesocial media presence of an individual and/or a cohort around theindividual); medical or health data (such as by inferring a diagnosisfrom a combination of image or camera data with other data, such aswearable device data, shopping data, behavioral data, or the like); andmany others. While there may be benign or beneficial uses of suchinferences, there are potentially harmful effects as well, such thatmany users may wish to be shielded from usage of such inferences exceptwhere they are aware of the usage and have given consent.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the obfuscation module 28810 may process and/or analyze one ormore data models for one or more markets, goods, services, users, marketmakers, market datasets, etc., to determine data instances, aggregates,etc. of the data models that may be sensitive for regulatory purposes.The obfuscation module 28810 may, for example, determine one or moredatasets, instances, aggregates, etc. that may be or include personallyidentifiable information relevant to HIPAA, financial privacyregulations, European data privacy regulations, California data privacyregulations, etc. The determination of personally identifiable orotherwise sensitive information may be performed via one or morerule-based systems and/or intelligent systems (e.g., AI, ML, ANN,intelligent agents, via proxies, patterns, teachings, etc.). Forexample, the obfuscation module 28810 may include an artificial neuralnetwork configured to train on one or more datasets and thereby learntypes of data that are sensitive. The obfuscation module 28810 may thenidentify sensitive data and perform one or more obfuscation operationsand/or classify or flag the data as sensitive data.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the obfuscation module 28810 may include an obfuscation moduleconfigured to perform one or more obfuscation operations to obfuscatedata identified as, determined to be, flagged as, and/or classified assensitive data. The obfuscation operations may be or include one or moreof redaction, replacement, addition (such as of fictional elements),partitioning, separation, augmentation, aggregation, noise addition,random rounding, and/or encryption. Redaction may include, for example,eliminating identifiable elements from datasets and/or models.Replacement may include, for example, replacing sensitive data withproxy data, random data, hypothetical data, and/or anonymized datarepresentative of the replaced data. Partitioning may include, forexample, adjusting partitions, increasing or decreasing number orgranularity of partitions, and the like. Separation may include, forexample, separating data elements that are sensitive, such that they areexcluded from aggregation. Augmentation may include tagging dataelements that are particularly sensitive, adding metadata to dataelements, or the like, such as to flag them to aggregation programs, toset parameters for aggregation, to set policies for aggregation, or thelike. For example, data elements in a data set may be augmented with aset of policy metadata governing how those elements are permitted to beused, such that data aggregation systems or other systems may consumethe policy elements and undertake aggregation consistent with thepolicies. In embodiments, policies may be maintained and automaticallyupdated by a policy engine, such that metadata elements areautomatically updated when policies change, such as governing rules of aregion, nation, state, or other entity. Aggregation may include, forexample, aggregating data to meta-level content from which determiningpersonally identifying information is difficult or impossible. Noiseaddition may include, for example, adding an amount of noise to thesensitive data such that the sensitive portions of the sensitive dataare difficult and/or impossible to identify, trace, determine, parse,etc. Random rounding may include, for example, randomly rounding values,including totals, either up or down (e.g., to a multiple of ‘5’ or‘10.’) To understand randomly rounded data, one must be aware that eachindividual value is rounded. As a result, when the randomly rounded dataare summed or grouped, the total value may not match the individualvalues, since totals and sub-totals are independently rounded.Similarly, percentages calculated based on rounded data may notnecessarily add up to 100%. Encryption may include, for example,applying one or more cryptographic processes to the sensitive data suchthat the sensitive data cannot be accessed, read, modified, marketed,etc. by untrusted parties.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the obfuscation module may perform the aggregation obfuscationoperation according to one or more aggregation hierarchies having aplurality of aggregation levels. The obfuscation module 28810 maydetermine one or more aggregation levels that may be appropriate for anaggregation obfuscation operation according to rules, regulations,preferences, etc., that are applicable to and/or appropriate for thesensitive data being aggregated. Aggregation levels may include, forexample, ZIP Code+4, ZIP code, neighborhood, city, state, county,province, department, country, global region, etc. Determination ofappropriate aggregation level for a particular instance of sensitivedata may be performed by one or more of the intelligent systems based onone or more attributes of the sensitive data. This may includedetermination by a robotic process automation system that is trained ona training set of interactions by a set of human experts and/or by adeep learning system. Aggregation levels may be tested, such as by adeep learning system or other learning system that is trained to attemptto infer sensitive data from data sets, including by seeking additionaldata sets that, with further aggregation, facilitate inference ofprivate information. Such as testing system may provide a set ofrecommendations, or inform a model, such as one that indicates relativerisks for permitting aggregation of a given type of data set, aparticular data set, a level of granularity, or the like.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the obfuscation module 28810 may use one or more intelligentsystems (e.g., AI, ML, ANN, DPANN/MEANN, intelligent agents, viaproxies, patterns, teachings, etc.) to determine whether sensitive dataon which one or more obfuscation operations have been performed has beensatisfactorily obfuscated. Upon determining that the sensitive data hasnot been satisfactorily obfuscated, the obfuscation module 28810 mayperform one or more further obfuscation operations on the sensitivedata. For example, the obfuscation module 28810 may determine that dataincludes personally identifiable information that violates one or moreregulations. The obfuscation module 28810 may perform one or moreobfuscation operations on the data to obfuscate the personallyidentifiable information via the obfuscation module, and then determinewhether the personally identifiable information remains determinablewithin the data after the obfuscation operation has been performed. Ifthe personally identifiable information remains determinable, theobfuscation module 28810 may perform one or more further obfuscationoperations via the obfuscation module on the data and repeat thedetermination process.

In embodiments, the engine 28800 may be configured to provide tools tohelp customers make better financially linked decisions. White-labeledplayers across industries are driving standardization of, and margincompression in, core product lines; in this environment, advice andancillary services can become key differentiating factors, helpingcustomers conveniently make important decisions. Ecosystem connectivitywill help institutions access non-standard data about customers tofurther tailor and improve offerings. Institutions can work withecosystem partners to deliver personalized tools, recommendations, andaccess to ancillary services to help clients make non-financialdecisions that are linked to their financial well-being. Examplesinclude consumerization of business, and consumer examples such asautomated financial tracking, credit awareness, subscriptionoptimization, couponing, recommendations, and the like.

In embodiments, the engine 28800 may provide tools to help commercialcustomers and consumers alike make better financially linked decisions.On the consumer side, a consumer may provide spending data and/orinvestment data as well as non-financial data to the tool, which maythen analyze non-financial data with spending data and/or investmentdata to provide recommendations that improve the financial situation ofa user. For example, a customer may provide a monthly spending report,which may show a monthly car lease payment, mortgage payments, etc. Thecustomer may have also provided non-financial data, such as travelplans, vacation days, work travel, lease agreements. The tool may runmodels that optimize a monetary parameter given the information providedby the user. By way of further example, the tool may determine that theconsumer should either fly or rent a car rather than drive their car totheir vacation location, as the mileage costs may exceed the price of aflight or a rental. In another example, the tool may recommend the bestdates that a user should request for vacation and/or locations to travelto so that the user may reduce the cost of a vacation. In anotherexample, a user may provide information relating to their televisionviewing habits as well as their subscription services, and the tool maydetermine a schedule when the user may cancel certain subscriptionsand/or add new ones to ensure that they are watching the content theylike, but not paying for subscriptions that the user does not need at aparticular time.

For commercial users, the engine 28800 may identify measures that anorganization may undertake to reduce burn rate. For example, if multiplebusiness units are subscribing to different but overlapping services,the tool may recommend that the business units use a different productthat covers all the services or may recommend that one unit switchservice providers to another service provider. In this example, the toolmay calculate the savings over a period of time to show the overall costto the business by accepting the recommended action. In another example,an organization may provide addresses of its employees (or generallocations), a role/team of each employee, and public calendar items ofthe organization/employees. The tool may identify employees who mayprovide more output if they are allowed to work from home, flexscheduling for employees in the office by team/meeting schedules, or thelike. Using this tool, the organization may improve productivity forcertain employees or teams, while reducing their office space whichprovides financial savings.

In embodiments, in order to identify financial outcomes linked tonon-financial actions, the engine 28800 may employ different means ofidentifying the links. For example, the system may include an“expert-sourced” library that provides non-financial actions that can beperformed in different scenarios to obtain better financial outcomes.Additionally or alternatively, the system may employ machine-learningtechniques to identify more latent non-financial actions that are tiedto verifiably “better” financial outcomes. In these embodiments, thesystem may use data from a collection of different sources (which maydepend on the “class of user”), where the data includes financialsignals as well non-financial signals. The engine 28800 may useappropriate AI/ML, algorithms, such as those described herein, toidentify the non-financial signals that are most strongly correlatedwith the positive financial signals and/or the negative financialsignals. The identified signals and/or correlations may be applied intoAI systems to assist clients in making decisions. For example, a usermay provide non-financial data to the engine 28800, and the engine 28800may identify scenarios where the user may take certain non-financialactions that were determined to be linked to positive outcomes and/ornon-actions that could be taken by the user to avoid negative financialscenarios.

In embodiments, the engine 28800 may link data gathered about a customerin one market to a related market. For example, data related to customerpreferences about a house may be extended to a marketplace for homedesign services.

In embodiments, the engine 28800 may project long-term financialoutcomes for decisions that provide a clearer “lifetime projection”across outcomes (e.g., by factoring in the costs of emergency care for apet or non-routine maintenance for a system, averaged across apopulation and with indicators of the “worst case” situations). As such,the engine 28800 may help users understand “ratchet” effects, orimplications of commitment or situational dependence on one or moremarket conditions.

In embodiments, the engine 28800 may be configured to facilitatesecuritization of future revenue streams. Securitization of futurerevenue streams allows companies to grow without shareholder dilution byfinancing their recurring receivables, provides for capital infusion byexchanging predictive, recurring revenue streams in exchange fordiscounted advance funding, and allows companies an alternative to thesignificant discounting found in other forms of single payment used asan inducement for customers to switch from recurring monthly to singlepayment. The engine 28800 may facilitate offering an instant cashadvance against the full annual value of a company's subscriptions orsource of recurring revenue. Examples of sources include real estate,SaaS or any as-a-service business model, advertising slots, and anyother suitable business sector or model that allows for monetizing longterm revenue streams without offering discounted incentives.

In embodiments, the engine 28800 may automatically configure a set ofsmart contracts to calculate a revenue share related to a set of assetsand allocate a fraction, such as fixed, formulaic (e.g., based on timeor other variables, including dynamically determined) to a distributedledger, whereby the revenue share is redeemable, such as based on atoken that represents the right to redeem the revenue share. The revenueshare calculation may be based on available data, including accountingdata or utilization data (e.g., as computational cycles used if theasset is a server, views or clicks if the asset is an advertising spot,salary/bonus for an individual, royalties for an item of IP, rent for anitem of real property, dividends for securities, etc.). A tokenrepresenting the redemption right in the smart contract may trade in amarketplace, the price of which may reflect a set of parties' predictedvalue of the future revenue stream. The engine 28800 may employ AI, RPA,intelligent agents, or a combination thereof, to operate on disparatedata sets across markets to predict future revenue, adjust initialpricing, recommend trading activities, automate arbitrage activities,aggregate positions (e.g., to hedge risk and/or optimize a risk/returnprofile), and govern trading rules with respect to the token. Custody ofassets may be governed by a secure set of automated, custodial agents.Sets of assets may be virtualized into working groups, with revenueshare being allocated at the group level.

In embodiments, the engine 28800 may facilitate one or more processes bywhich a basket of revenue shares may be constructed and automaticallymaintained by an intelligent agent, such as a basket of rents,royalties, dividends, interest payments, annuity payments, and others.

In embodiments, the engine 28800 may facilitate one or more processes bywhich an AI system may optimize the basket, such as based on a targetrisk/reward profile or asset allocation, feedback on outcomes, (e.g.,yields), contextual data (e.g., on market conditions, asset conditions,process states, and other data about marketplace entities), and otherfactors.

In embodiments, the engine 28800 may perform contract enforcement anddispute resolution services in cross-market environments, such asenvironments where physical assets or revenues from physical assets areinvolved in a smart contract or where revenues are received in oneenvironment while those entitled to the revenues (and the smartcontract) operate in a different environment. The engine 28800 mayperform covenant enforcement, where covenants may apply to multipleparties of an agreement, may monitor for compliance, may determine thatthere is a breach, and/or may determine that one or more breaches havebeen cured. Examples of cross-market environments that may involvedisjointed data include involuntary collections, collateral repossessionand disposition (e.g., mortgage foreclosures, auto repossessions,recoveries from sources not directly involved in the smart contract(e.g., deficiency balance collections), collections from co-applicantsor guarantors or credit insurers, tax calculation, collection, andremittance, and the like.

In embodiments, the engine 28800 may facilitate enforcement in anenvironment where multiple parties contribute to creating a token orseries of tokens, such as borrower and guarantor or co-applicants for aloan. Such environments may include cases where multiple parties pooltheir assets (or revenue streams) in a distributed environment in orderto gain scale and efficiencies (lower pricing), such as lenders poolingresources. Further examples include transactions including creditinsurance and other credit enhancement providers, such as guarantorsand/or unrelated parties. The insurers, enhancement providers,guarantors, etc. may receive payment corresponding to risk.

In embodiments, such as those including structured finance environments,the engine 28800 may create a token or series of tokens based on anasset or asset pool. The token or series of tokens may be broken intocredit tranches (e.g., AAA, AA, A . . . tokens) and marketed atdifferent price points (and assume different risk levels) with the lowercredit quality tokens dependent on the higher credit quality ones.

In embodiments, the engine 28800 may be configured to function as atrusted data steward, thereby ensuring secure transfer, use, andexchange of only necessary information between trusted parties. Theengine 28800 may provide a data sharing platform employing one or moreof blockchains, smart contracts, tokenization, double-blind methodology,and smart encryption. The smart encryption may be configured such thatthe data sharing platform or one or more users, clients, and/or partiesmay monitor service providers to ensure data is being used and storedappropriately, such as according to one or more contracts, smartcontracts, laws, regulations, and/or the like. The data sharing platformmay link to one or more financial institutions and/or other stakeholdersand use data therefrom to effectively monitor service providers. Thedata sharing platform may secure data transmission approaches, such as5G and IoT, employ one or more authentication models, facilitateexchange and consent management, and/or provide continuousauthentication of parties (e.g., by using extended data sets).

In embodiments, the engine 28800 may perform data minimization withregard to the collection of personally identifiable information. Theengine 28800 may use transaction history and related metadata, such asin a distributed ledger, to authenticate a person, entity or othertransaction source (e.g., a virtual entity), thereby reducing the needfor the solicitation, transmission, and/or storage of additionalsensitive information. The engine 28800 may authenticate for a currenttransaction using information based on a prior transaction, such asinformation stored in a ledger. Devices associated with a transactionentity may be authenticated, at least in part, for a current transactionusing information based on a prior transaction, such as informationstored in a ledger.

In embodiments, the engine 28800 may facilitate one or more processes bywhich authentication may itself be a transaction recorded in a ledger.For example, the engine 28800 may justify a payment processor based on aprior authentication in collecting a reduced set of personallyidentifiable data for a current transaction.

In embodiments, a data purge may be a ledger transaction. For example,the engine 28800 may create an audit trail including data that an entityin a transaction chain had, but no longer stores, such as sensitive datathat may be required to maintain in the ledger only for the purposes ofprocessing a transaction. The engine 28800 may establish an audit trailfor post-hoc verification that data collection was appropriate in scaleand substance (e.g., only required and pertinent information wascollected; unnecessary data was not stored, and the like).

In embodiments, the engine 28800 may use trusted data and/or datasteward processes to rate entities that are would-be parties to atransaction. Entities may be at the device level, such that the ratingmay be a surrogate of how trusted or not trusted a device is based onits prior transaction/data collection record. The rating may be used asa selection criterion (including automated) for allowing participationof an entity, device and the like in a transaction, or to collect data,collect a particular type of data, and the like, for example todetermine a reputation measurement derived from the blockchain or othertransaction and data collection history.

In embodiments, the engine 28800 may use trusted data and/or datasteward processes to determine potential transaction pathways availablefor a given transaction. For example, pathways may be determined for atransaction involving purchasing cryptocurrency using fiat currency froma banking institution and transferring the cryptocurrency to a personalwallet and to a vendor from the personal wallet to affect thetransaction. The chain of transaction and/or data collection may beviewed as a single transaction for the purpose of rating the security,necessity of the contemplated data collection, and the like.

In embodiments, the engine 28800 may set a threshold that must be met ina rating to proceed with a contemplated transaction.

In embodiments, the engine 28800 may determine a measure of datacollection behaviors, and/or may push behaviors, tracking mechanisms andthe like.

In embodiments, the engine 28800 may include a verifiable action tokenmodule 28814 configured to create, manage, and/or facilitate thecreation/management of verifiable action tokens. The verifiable actiontokens may be tokens that are linked to data (e.g., operational,financial, etc.) and can be accessed securely to ensure agreed uponactions are taken. With the rise of alternative asset classes (e.g.,crowdfunding, real estate), investments tied to verifiable actionsprovide investors additional transparency for their investment. Equityand/or debt tokens may be linked to real-time sources of data. The datasources may include, for example, IoT data, operational spending data,and resource allocation data. Actions may be taken based on verifiabletriggers, such as regulatory, legal, financial, buying events, sellingevents, penalties, etc. In some embodiments, the verifiable action tokenmodule 28814 may facilitate management of custody, and/or trading, ofverifiable tokens.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the verifiable action token module 28814 is configured toimplement verifiable action tokens with assets such as securities,investments, tangible assets, intangible assets, and value chaincomponents. The verifiable action token module 28814 contains aGUI-enabled interface that allows an owner of a security, an investor, aparty to an M&A and/or a contributor to or investor in a value chain toacquire, view, manage, and make transactions related to verifiabletokens for their assets. The owner may view their tokens and see whichassets are related to the tokens. The owner may also view any smartcontracts related to the tokens, and logs of past, current, and futureactions taken according to such smart contracts. The actions may includerelated triggering events, which may also be displayed with relatedmetrics. The metrics may be pulled from databases and real-time datasources. The databases and real-time data sources may include IoT sensordata, AI predictions and evaluations, operational spending metrics,resource allocations databases. A GUI interface may be provided fortransferring custody of the verifiable tokens, such as via a marketplaceand/or via private transactions. The tokens may have associated rewardsand/or penalties, the rewards and/or penalties triggering automaticallyaccording to encoded triggering events, smart contracts, innateattributes, templates, etc. The verifiable action token module 28814 mayinclude templates for types of securities (stock, bond, etc.),transactions (M&A, short, auction, etc.), assets (tangible, intangible,IP, inventory, security, etc.), and/or overseeing bodies (regulatory,insurance, etc.). The verifiable action token module 28814 may include adata stories system and related AI system configured to present the userwith a human-readable data story in relatively plain language related totheir verifiable tokens. The verifiable action token module 28814 mayalso include a marketplace integration system that allows the user tomake transactions related to the tokens on one or more marketplaceand/or auction platforms, or within one or more blockchains/distributedledgers.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the verifiable action token module 28814 may associate securities,such as stocks and bonds, with verifiable tokens recorded on a ledger.The recordation on the ledger may reduce the fees associated with themiddlemen of a traditional securities exchange, as no middleman isrequired to maintain the integrity and value of thesecurities-associated tokens. The tokens may be signed, verifiable asunique and valid, and even have logic and/or smart contracts associatedtherewith. The logic and/or smart contracts may include automaticallytriggered events for transactions such as shorts. IPOs and othersecurities creation events may be managed via tokens and logic/smartcontracts associated with the tokens.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the verifiable action token module 28814 may facilitate backinginvestments of investors, such as private investors and VCs, byverifiable tokens. The verifiable tokens may be generated for businessesin which investors have invested, such as in assets of the business, orpartial ownership of the business. Smart contracts and other logic maybe employed with regard to the investment agreements. For example, aninvestor may receive an automatic payout upon verification of revenue,sale, license, etc. by the invested-in business.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the verifiable action token module 28814 may manage merger andacquisition transactions via verifiable tokens recorded on a ledger. Thetokens may be associated with assets of an entity to be acquired. Theassets may include tangible assets such as inventory, equipment,infrastructure, etc. The assets may also or alternatively includeintangible assets such as intellectual property, debts, securities,deeds, and liabilities such as contractual obligations, regulatoryobligations, insurance obligations, etc. The verifiable action tokenmodule 28814 may associate smart contracts and/or logic the tokens toverify the integrity of each and facilitate transfer from one entity toanother as the merger or acquisition transaction is completed.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the verifiable action token module 28814 may tokenizemanufacturing and/or value chain systems by verifiable tokens. Thetokens may be associated with each of products produced, factorymachines on a manufacturing floor, units of inventory in warehouses, ortransport vehicles such as robots, trucks, trains, ships, etc. Differentbusinesses that participate in a value chain may transfer verifiabletokens to one another via the verifiable action token module 28814 asresources are expended, materials and/or products are moved, contractualconditions are met, etc. AI systems of the platform 100 may beconfigured to automatically predict and/or validate conditions inmanufacturing and/or value chain environments. The AI systems maytokenize predictions and validations. These tokens may be recorded,traded, and may even have value in secondary markets.

Examples of verifiable action tokens include green action tokens,production tokens, contract tokens, governance tokens, and energytokens. The green tokens may include tokens to not cut down trees,tokens to not drive vehicles, tokens to use public transit, verifiedcarbon capture tokens, verified non-pollution tokens, tokens for solarpower usage, recycling tokens, weekly shipment/fewer packages deliveredtokens, minimum plastic usage tokens, tokens for not dumping industrialeffluent, and tokens for planting saplings and/or trees, and the like.Production tokens may include tokens for production of a product unit,tokens for mining of a mineral/metal, and the like. Contract tokens mayinclude tokens for satisfaction of a payment obligation, tokens forsatisfaction of a covenant, tokens for compliance with a permit'slimitations, tokens for compliance with construction codes, and thelike. Energy tokens may include tokens for energy usage within limits,tokens for saving energy, and the like. Further examples of tokensinclude guarantor action tokens, such as tokens for bearing risk and/orresponsibility if a guaranteed action does not take place. Furtherexamples of tokens include tokens verifying that one has not used botsand/or scripts to mass purchase/scalp a product having limited stock forthe purpose of reselling on a secondary market, tokens verifying thatone has not sold a product to more than one buyer 28806, tokensverifying that one has not sold to a prohibited buyer 28806 (such as apolitically/statutorily prohibited foreign or criminal buyer 28806),tokens verifying that a politician or business executive has not takenmoney from illegal or unconscionable sources, tokens verifying that apolitician has not illegally collaborated with a private business formarketing gain of the business or financial gain of the politician inexchange for political favors, and the like.

Further examples of verifiable action tokens include tokens for using anamount of electricity, tokens for air conditioner settings, tokens forheating settings, tokens for using an amount of water, tokens for lengthof shower, tokens for amounts of emissions, tokens for not eating meat,tokens for not slaughtering animals, tokens for plastic usage ornon-usage, tokens for recycling, tokens for creating an amount ofgarbage, tokens for refraining from flying on planes and/or privatejets, tokens for washing hands, tokens for wearing masks, tokens forbeing vaccinated, tokens for remaining at home and/or indoors, tokensfor having fewer than an amount of people in a residence and/or on apremises, tokens for verifying crowdfunding projects and/or reachinglandmarks thereof, tokens for attending school and/or classes, tokensfor completing homework, tokens for performing community services,tokens for not smoking, tokens for not smoking in particular locations,public health tokens issues based on diet, speeding, not drinking, etc.,tokens for managing consumption of clothing, potable fluids, foods,space, etc., tokens for mileage per week of transit, parental tokensbased on spending time with children, reading with children, spendingtime outdoors with children, etc., philanthropic tokens based onperforming charity work and/or contributions, tokens for takingmedication, such as during a testing trial or while undergoing a therapyprogram, tokens for social media interaction, tokens for call centerworkers, tokens for calling a call center, tokens for making a salescontract, tokens for contacting customers and/or prospective customers,tokens for means of contact, e.g., voicemail, email, etc., tokens formeeting with persons, tokens for reducing disease spreading risk, tokensfor potential interaction tracking, tokens for conditional probabilityof related actions, tokens for dispute resolution, tokens for proof ofstake, tokens for insurance verification, tokens for cardiovascularactivity, e.g., exercise classes, jogging habits, etc., tokens forservicing of warranties for cars, houses, rental properties, and thelike, tokens for partaking in marketing activities, e.g., social mediapost interactions, tokens for employment conditions, tokens for collegeadmissions criteria, tokens for community service work, tokens for lawenforcement activities, tokens for maintenance of public spaces, and thelike.

In embodiments, the engine 28800 may be configured to provide borderlessasset enablement. Data is an asset and there are regulatory, compliance,security, cost and other reasons for data to remain in-house or not bemoved outside of certain jurisdictions or locations. As the data economygrows with increased sharing of data, there needs to be a mechanism thatallows data to be transacted and exchanged across borders, therebybecoming borderless. The engine 28800 may provide an asset-backedtokenization platform. The engine 28800 may provide connecting dataowners with nodes to DLT networks via the asset-backed tokenizationplatform. The DLT networks are connected to other DLT networks via theasset-backed tokenization platform. As such, data is moved as atransaction on a DLT network as opposed to direct exchange. Architecturefor borderless transactions may be one or more of proof-of-work,delegated proof-of-stake, bonded proof-of-stake, pure proof-of-stake,etc. In some embodiments, the asset-backed tokenization platform mayprovide decentralized financing of other non-traditional assets, e.g.,balance sheets, knowledge, brand, workflows (crypto mining,verifications, oversight), and the like.

In embodiments, the engine 28800 may facilitate data to be transactedand exchanged across borders, including jurisdiction, industry,location, and the like. The engine 28800 may provide for a curatedand/or regulated nested data pool with provenance that includes partsand assemblies, single and multiple users, etc., that can supporttransactions for analysis that support anonymous, personal, aggregated,fractional, specific, or other types of arrangements.

In embodiments, the engine 28800 may facilitate one or more processes bywhich borderless transactions may be provided according to one or morearchitectures, such as proof-of-work, delegated proof-of-stake, bondedproof-of-stake, pure proof-of-stake and/or any other suitablearchitecture.

In embodiments, the engine 28800 may facilitate decentralized financingof non-traditional assets, such as balance sheets, knowledge, brand,workflows (crypto mining, verifications, oversight, etc.).

In embodiments, the engine 28800 may facilitate protection ofintellectual property.

In embodiments, the engine 28800 may be configured to perform one ormore market-making enablement operations. Individual market participantsor member firms of an exchange may buy and sell securities for their ownaccounts, at prices displayed in the exchange trading system, with theprimary goal of profiting on a bid-ask spread (e.g., the amount by whichthe ask price exceeds the bid price of a market asset). Enablingdetailed price data and price discovery to establish a fair price thatsatisfies both buyers 28806 and sellers 28808 with detailed data. Themarket-making enablement operations may include one or more of enablingfractional ownership, improving auction/trading mechanisms, and assetlinking. Enabling fractional ownership may include, for example, movingfrom a bid process that enforces a winner-take-all outcome to fractionalownership, the fractional ownership process supporting buying andselling shares of illiquid assets. Improving auction/trading mechanismsmay include, for example, facilitating trading of illiquid assets byproviding connections to digitalized markets, enhancing trading ofilliquid assets with emerging technology (e.g., AI, distributed ledgers,smart contracts, etc.), aggregating liquidity, and enabling newecosystems. Asset linking may include, for example, establishing,managing, and/or facilitating strong information flows about physicaland/or financial statuses of assets.

Examples of market makers include brokerage houses or traders thatprovide trading services for a marketplace with a goal of keepingfinancial markets liquid. Market makers may be or be enabled byartificial intelligence and/or intelligent agents. A market maker may bean individual trader, such as a cryptocurrency trader.

In embodiments, the engine 28800 may create liquidity in fragmentedmarkets. For example, a market for trading future price of organic beansprouts, and/or a market for trading future price of organic carrots.The engine 28800 may relate the prices of the organic bean sprouts andthe organic carrots to one another, and generally related the prices ofthe sprouts and the carrots to the overall price of vegetables via oneor more market-making operations.

In embodiments, the engine 28800 may facilitate cross-markettransactions. For example, the engine 28800 may facilitate carbontrading (such as paying owners of trees not to cut them down), and/orencourage personal behavior (such as not to drive car or to only eatvegetables). The engine 28800 may also or alternatively allow one ormore guarantors or insurers to bear costs of actions taken.

In embodiments, the engine 28800 may facilitate creation of liquidity inilliquid markets. For example, market makers may take large positionsand hold them for longer periods of time to manage the liquidity ofmarkets via one or more market-making operations. Further examplesinclude the selling of super yachts or art, where there is a verylimited market and buyers 28806 are very limited. In such markets,buyers 28806 and/or sellers 28808 may find themselves holding positionsin large expensive vessels or pieces that are difficult to sell. Theengine 28800 may facilitate sharing of fragmented ownership of thevessels or pieces, thereby creating liquidity in the market.

In embodiments, the engine 28800 may facilitate making of markets acrosstime zones, such as within a group of cryptocurrency markets where thesecurities can move between time zones. For example, the engine 28800may allow a market maker to move the token between markets in differenttime zones to manage liquidity and price stability.

In embodiments, the engine 28800 may facilitate the making of markets inmarkets having slow trading times. For example, in a housing marketplacea market maker may use the engine 28800 to buy and sell housesimmediately, such as wherein a seller 28808 creates a position wherethere is a continuous availability of inventory and a potential forimmediate resale of the property.

In embodiments, the engine 28800 may facilitate trading between marketmakers in fragmented markets. The market makers may collaborate via theengine 28800 tooling to ensure that the market makers are providingliquidity and are not driving the overall market position. For example,in a fragmented crypto marketplace with multiple securities, the marketmakers may collaborate on trades to manage liquidity via the engine28800. The engine 28800 may then introduce liquidity as a commodity ormetric that can be measured, and the value associated with theseliquidity producing activities are valued by the market and can becommunicated between market makers.

In embodiments, the engine 28800 may facilitate trading between AI-basedmarket makers. AI-based market makers are increasingly becoming acentral part of managing the marketplace and creating liquidity. AIengines can be subject to adversarial neural network attacks and otherinformation attack mechanism to force loss making positions, rather thancreating liquidity. For example, an AI agent may be responsible formanagement of liquidity in an extremely high fragmented marketplace fortrading cards. The challenge is that trading cards have many types, andeach has an associated value (e.g., individual cards may have a value ofhundreds of thousands of dollars). The AI agents can access the qualityof the card via the engine 28800 and establish liquidity in trading inpart or whole of a card providing for verifiable trading events andreliability of trade.

In embodiments, the engine 28800 may enable market makers to find pricein a highly distributed marketplace. Establishment of price in a highlydistributed market may require creation of indices that span multiplemarkets and allow for management of rational (if markets can berational) pricing levels. For example, the engine 28800 may facilitateanalysis of market functions in indices. The indices may includemultiple securities and a basket analysis of price of the securities. Ina highly distributed marketplace, the indices may need to be vastly morecomplex providing for assessment market positions and variations. Theengine 28800 allows the market maker to manage the indices and ensurethat the indices are stable and price fluctuations are within reasonablemarket boundaries.

In embodiments, the engine 28800 facilitates community market makers andmarket making by consensus. Market making by consensus allows a socialmedia based market maker to create community pools of resources across asocial network, thereby creating liquidity in a broader market, therebyresulting in a decentralized set of arbitrage opportunities. Forexample, in a cryptocurrency marketplace, there is a potential for agroup of individuals to collaborate to maintain liquidity in themarketplace. The collaboration may require each member to contributecapital to a trusted market maker that manages positions and sharesgains (or losses).

In embodiments, the engine 28800 may facilitate market makers to managereinsurance. Market makers may have reinsurance companies behind themwhile the market makers take market positions that enable theestablishment of true liquidity while having risk levels managed bythird parties. For example, a futures market in cryptocurrency tokensfor pork bellies may have a high-risk exposure to future climate events.The reinsurance company can stand behind the high-risk exposure via theengine 28800, thereby providing for a price guarantee in the event of aclimatic disaster impacting pork belly prices.

Cross markets are a natural and powerful way of market building acohesive and real-world marketplace. By linking markets together, crossmarket operations create an environment where smaller marketplacesbecome viable and efficient. For example, a new home housing marketplaceand a marketplace for plumbers may be linked via cross-marketoperations. The new housing creates demand for plumbers, and a tradermay recognize and analyze the demand via the engine 28800. The tradermay create cross market values for the localization of plumbing servicesthat are associated with marketplaces for the construction of new homeunits.

Some common factors in the cross-market enablement include arbitrage andinternational market management. Traders may seek out arbitrageopportunities via the engine 28800, as the arbitrage opportunities oftenrepresent one of the best ways of building value. The engine 28800 mayemploy machine learning and/or AI/intelligent agents to identifyopportunities for the trader to find and drive value through the act ofbuying and selling in both marketplaces. These actions drive liquidityin both marketplaces as traders drive volume and establish consistencyof price. With assets that span nations, there is a potential forinternational marketplace activities. For example, the engine 28800 mayidentify and/or measure an amount of human resources required for abuild of a new product. A marketplace that spans nations and is able tohandle the complexities of currency exchange in the buying process canenable buyers 28806 to establish a price for goods that combinescurrency exchange considerations with the ability to deploy localresources via the cross-market operations. Further traders may seekarbitrage activities against the currency fluctuations. Theinternational cross-market operations may include identifying and/ormanaging regulatory properties, roles for AI-based markets,globalization factors, time zone management, and liquidity acrossnational markets.

In embodiments, the engine 28800 may facilitate investing and/ormarket-making in a human resources asset, such as in finding, managing,gaining, and/or allocating manpower for manufacturing, sales, serviceperformance, and the like. The engine 28800 may perform cross-marketoperations such as improving auction/trading mechanisms, asset linking,performing human asset equalization, enabling investment in a person,and/or representing one or more human assets as intelligent agentsand/or digital twins.

In embodiments, the engine 28800 may facilitate managing risk (withhuman or other assets) based on performance, such as via testimonialsbased on verifiable performance data and metrics. The testimonials maybe created over time and/or augmented by risk insurers. The testimonialsmay be provided as a service to a range of newer platforms.

In embodiments, the engine 28800 may facilitate representing people asdigital twins, investing in human capital, and assisting with managinghuman capital as part of an investment strategy.

In embodiments, the engine 28800 may facilitate development of humancapital, such as by gathering data related to and quantifying and/orqualifying impacts of education and/or experience human capital. Forexample, the engine 28800 may gather data relating and quantify and/orqualify the impacts of different skills sets on risk and insurancemitigation. If a team is lacking skill in regulatory governance, the28800 may identify whether there is a real risk to regulatoryinitiatives. By way of further example, if there is a skills shortage,the engine 28800 may identify projects that could provide trainingexperience to allow for human capital development.

In embodiments, the engine 28800 may be configured to perform ordermatching via an order matching system. The order matching system is anelectronic system that matches buy and sell orders for a stock market,commodity market or other financial exchange. When market making, theway in which order matching operates can be crucial to how firms marketmatch.

In embodiments, the engine 28800 may facilitate matching orders byconsidering price, timing, and/or quantity of goods and/or services. Theengine 28800 may employ traditional matching, price-time-priority,and/or pro-rata priority systems, and/or any other suitable prioritysystem for matching orders. Traditional matching may prioritize volume,benefiting buyers 28806 (bidders) and sellers 28808 (askers).Price-time-priority may match an earliest bid at a highest price beforeany similar bids at the same price that entered after, such as via aFIFO system. Pro-rata priority may match equivalently priced bids to amatching ask proportional to an amount of active bids.

In embodiments, the engine 28800 may facilitate order matching fortransactions involving a plurality of blockchains and/or distributedledgers. A smart contract may be stored on a single chain and/or ledger.As such, cross-chain or buy-into/selling-out-of actions may oftenrequire interacting with multiple blockchains.

In embodiments, the engine 28800 may facilitate creations and/ormanagement of a parent contract that launches on several blockchainsand/or ledgers, thereby allowing for transacting between the blockchainsand/or ledgers. Each blockchain and/or ledger may require an exchangeratio, and/or may exist on a stand-alone chain that can host parentcontracts. Buying-into/selling-out-of transactions may occur withstablecoins.

In some embodiments, the engine 28800 may facilitate one or moreprocesses by which the order matching system may operate using a timepriority. The principle of price/time priority refers to how orders areprioritized for execution. Orders are first ranked according to theirprice; orders of the same price are then ranked depending on when theywere entered. Network based time speed priority allows for levelingplaying field, e.g., via clock synchronization across parties.

In some embodiments, the engine 28800 may facilitate one or moreprocesses by which the order matching system may operate using a paritypriority. The parity priority rewards those who set the best price, thenallocates the remaining shares to other orders that match that price. Bysharing the allocation among those who post the best price, rather thanbased on how quickly they place the order, institutional investorsbenefit from better fill rates, execution costs, and the ability toshare executions at the same price as faster participants.

In embodiments, the engine 28800 may include a or interface with arobotic process automation (RPA) module configured to performhigh-frequency, repeatable tasks that would otherwise be performed by ahuman. The RPA module may operate by consistently applying rules andadherence to control frameworks, thereby reducing processing time of thetasks.

In embodiments, the engine 28800 may include an intelligent agent moduleconfigured to create, configure, and manage one or more intelligentagents. The intelligent agents may make decisions and/or perform one ormore services based on an environment, user input, and experiences. Theintelligent agents can autonomously gather information (e.g., on aregular, programmed schedule or upon being prompted by a user). Examplesof tasks that the engine 28800 may perform via one or more intelligentagents include automating interactive and sophisticated processes,performing front office business operations, performing intelligent,contextual, updated client outreach, performing communication via email,text, other messaging platforms, performing negotiation, performing RPAassisted negotiation, provide negotiation terms and alternatives,performing full negotiation based on a gaming/logic engine, performingsocial media interaction, responding to client comments on social media,“liking” or otherwise interacting with relevant social media posts,performing content reposting and/or generation, improving end-userexperience, monitoring and/or shadowing human-human exchanges, performactions based human-human exchanges, preparing packages, accounts and/orloans for opening, delivering and/or interacting with off-channelcontent and/or services, automating accounting for transactions,automating execution, providing analytics that create sophisticated andaccurate frameworks, automating pricing of cross-market products basedon comparable prices for direct services from competitors, execution ofcontract terms, etc. For example, the engine 28800 may employ anintelligent agent to perform mortgage cross-selling enhanced by IoT dataand AI. The intelligent agent may perform enterprise churn predictionand determine preventative negotiated rates to minimize customer loss.By way of another example, in a healthcare environment includingregulations, insurance, and/or finance management, the engine 28800 mayemploy an intelligent agent to assist with determining and analyzing achoice of banks, bank accounts, and bank features, facilitate regulatoryhandoffs and self-validation. The intelligent agent may assist a serviceprovider with a portal for deposits, withdrawals, and/or compliancereporting. The intelligent agent may merge health insurance claimstreams with bank account activity data and user actions. Theintelligent agent may facilitate creation and management of a healthportal for a health insurance provider. The health portal may containhighly confidential information which may be managed via blockchainand/or distributed ledger. The intelligent agent may assist with billpayment services, such as by handling direct payments and/or automaticpayments for approved claims. The intelligent agent may assist withadd-on financial and/or investments services, such as HSA spendingmanagement. The intelligent agent may be configured to create and/ormanage a smart wallet. The smart wallet may be configured to manage oneor more actions related to a regulated HAS, such as policy andgovernance of data presentation, validation without invading privacy,and/or payments for health services.

In embodiments, the engine 28800 may facilitate one or more processes bywhich the RPA module, coupled with intelligent agents, can automateinteractive and sophisticated processes, as well as perform front-officebusiness operations. As such, the RPA module can operate withhigh-intensity workloads that seamlessly integrate for improved end-userexperience. The intelligent agents can work in synergy with otherdigital and automation technologies, such as IoT (Internet of Things),and analytics, to create sophisticated and accurate frameworks. Forexample, the engine 28800 may enable mortgage cross-selling enhanced byIoT data and AI via the RPA module and the intelligent agent module.Thereby the engine 28800 may perform enterprise churn prediction andpredict preventative negotiated rates to minimize customer loss.

In embodiments, the engine 28800 may be configured to, via one or bothof the RPA module and the one or more intelligent agents, dynamicallyoptimize market conditions, such as prices, liquidity, availability,etc. of traded assets and/or currencies (cryptocurrencies, fiatcurrencies) based on real-time intelligence. For example, on the lendingside, cost of acquisition of customers and type of loan and quality ofunderwriting (e.g., filters to the incoming funnel) can be adjustedbased on current market conditions of those in the funnel (e.g., datafrom the funnel). Moreover, the need to discount the sale of servicingcan be tied to the acquisition.

In embodiments, the engine 28800 may be configured to performgamification of internal burn rate. The engine 28800 may cross-referenceinternal burn rate with third-party bandwidth, such as bandwidth of atitle company, and provide incentives to move a closing to accommodateneeds.

In embodiments, the engine 28800 may perform or facilitate adjustment ofunderlying insurance contracts to ensure beneficiaries of policy aremade whole upon default of the asset owner.

In embodiments, the engine 28800 may be configured to create, manage,and/or facilitate transactions for NFT-based titles of real estate. Theengine 28800 may facilitate crowdsourcing the confidence in tracingtitle, and, in embodiments, may build a token based on crowdsourcedinformation, especially where underlying records are gone.

Market Prediction System

Referring to FIG. 290 , the present disclosure relates to a marketprediction system platform 29000 that is configured to generate marketpredictions (e.g., a prediction about a set of markets, a predictionabout market share, a prediction about a set of marketplaces, aprediction about a set of assets, a prediction about the pricing of aset of assets, a prediction about a set of transactions, a predictionabout a parameter of demand, a prediction about a parameter of supply, aprediction about a set of contracts, a prediction about a set of smartcontracts, a prediction about the terms or conditions of a smartcontract, a prediction about a party in a market, and many others),referred to herein in the alternative as the “platform,” the “system” orthe like, with such terms comprising various alternative embodimentsinvolving various sets of components, modules, systems, sub-systems,processes, services, methods, and other elements described herein and inthe documents incorporated herein by reference.

According to embodiments herein, a market or a marketplace may refer toan environment configured to facilitate transactions related to a set ofassets. Assets may refer to commodities, physical assets, products,digital assets, services, stocks, bonds, marketplace-traded funds (ETF),mutual funds, currencies, foreign exchange (FX), artwork and other worksof authorship, alternative assets, recycled plastics, digital 3Ddesigns, digital gaming assets, virtual goods, real estate, placementrights (such as for advertising), cryptocurrencies, metals and alloys,energy resources, derivatives (such as futures, forwards, options, puts,calls, and swaps), 3D printing capacity, digital twins, storage,intellectual property (e.g., trade secrets, patents, trademarks,designs, know how, privacy rights, publicity rights, and others),instruction sets, hybrid instruments, synthetic instruments, tranches ofassets (including similar and mixed-asset tranches), streams of value(such as of interest), certificates of deposit (CDs), and the like, aswell as portions of the above (such as divisible and undividedinterests), hybrids of the above, and aggregates of the above (includingtranches of securities, mutual funds, index funds, and others).

In some embodiments, a marketplace may be a forward marketplace. Inembodiments, a forward marketplace may refer to an electronicmarketplace that provides a medium for counterparties to negotiate andengage in forward contracts. A forward contract may refer to acustomized contract between two parties to buy/sell a negotiatedquantity of an asset at a negotiated price on a negotiated date.Examples of assets that may be sold using forward contracts includeagricultural commodities (e.g., wheat, corn, oranges, cotton, and/or thelike), natural resources (e.g., natural gas, oil, gold, silver,platinum, or the like), financial instruments (e.g., stocks, bonds,currencies, or the like), non-traditional assets and/or other suitablecommodities (e.g., fuel, electricity, energy, computational resources(e.g., quantum computational resources), storage capacity, networkcapacity, network spectrum, advertising, attention resources,cryptocurrencies, defined income streams, data streams (such as sensordata, network data and the like), knowledge structures, and many others.

In embodiments, the market prediction system may be configured topredict a parameter of demand for a set of assets. In embodiments, theparameter of demand may be a transaction parameter, a price, a totalcontract value, a profit margin value, a timing parameter, and manyothers.

In embodiments, the platform 29000 includes an API system thatfacilitates the transfer of data between a set of external systems andthe platform 29000. In some embodiments, the platform 29000 includesdatabases that store data relating to markets, marketplaces,transactions, contracts (e.g., smart contracts), assets, predictions,and the like.

In embodiments, the platform 29000 includes and/or integrates with anintelligence services system (also referred to as “intelligenceservices”), described throughout this document and by documentsreferenced herein. In embodiments, the intelligence services systemprovides a framework for providing intelligence services to the marketprediction system platform 29000. In some embodiments, the intelligenceservices framework may be adapted to be at least partially replicated inthe market prediction system platform 29000. In these embodiments, themarket prediction system platform 29000 may include some or all of thecapabilities of the intelligence services, whereby the intelligenceservices is adapted for the specific functions performed by thesubsystems of the intelligence client. Additionally or alternatively, insome embodiments, the intelligence services may be implemented as a setof microservices, such that the market prediction system platform 29000may leverage the intelligence services via one or more APIs exposed tothe platform 29000. In embodiments, the market prediction systemplatform 29000 may provide an intelligence request to the intelligenceservices, whereby the request is to perform a specific intelligence task(e.g., a prediction). In some embodiments, the market prediction systemplatform 29000 may request non-prediction intelligence tasks, includingdecisions, recommendations, reports, control instructions,classifications, training actions, NLP requests, digital twin requests,RPA requests, or the like. In response, the intelligence servicesexecutes the requested intelligence task and returns a response to themarket prediction system platform 29000. Additionally or alternatively,in some embodiments, the intelligence services may be implemented usingone or more specialized chips that are configured to provide AI assistedmicroservices such as image processing, diagnostics, location andorientation, chemical analysis, data processing, and so forth.

In embodiments, the platform 29000 includes and/or integrates with aquantum computing system (also referred to as “quantum services”),described throughout this document and by documents referenced herein.In embodiments, the quantum computing system provides a framework forproviding a set of quantum computing services to the market predictionsystem platform 29000. In some embodiments, the quantum computing systemframework may be at least partially replicated in the market predictionsystem platform 29000. In these embodiments, the market predictionsystem platform 29000 may include some or all of the capabilities of thequantum computing system, whereby the quantum computing system isadapted for the specific functions performed by the subsystems of thequantum computing client. Additionally, or alternatively, in someembodiments, the quantum computing system may be implemented as a set ofmicroservices, such that the market prediction system platform 29000 mayleverage the quantum computing system via one or more APIs exposed tothe platform 29000. In these embodiments, the quantum computing systemmay be configured to perform various types of quantum computing servicesthat may be adapted for different quantum computing clients. In eitherof these configurations, a quantum computing client may provide arequest to the quantum computing system, whereby the request is toperform a specific quantum computing task (e.g., a quantum prediction).In response, the quantum computing system executes the requested taskand returns a response to the quantum computing client.

In embodiments, a market prediction system platform 29000 is providedhaving a crowdsourcing system for obtaining information that may berelevant to generating market predictions (e.g., a prediction about aset of markets, a prediction about market share, a prediction about aset of marketplaces, a prediction about a set of assets, a predictionabout the pricing of a set of assets, a prediction about a set oftransactions, a prediction about a parameter of demand, a predictionabout a parameter of supply, a prediction about a set of contracts, aprediction about a set of smart contracts, a prediction about the termsor conditions of a smart contract, a prediction about a party in amarket, and many others), including information related to productavailability, product pricing, delivery timing, need for refill, needfor replacement, manufacturer recall, need for upgrade, need formaintenance, need for update, need for repair, need for consumable,taste, preference, inferred need, inferred want, group demand,individual demand, family demand, business demand, need for workflow,need for process, need for procedure, need for treatment, need forimprovement, need for diagnosis, compatibility to system, compatibilityto product, compatibility to style, compatibility to brand, demographic,psychographic, geolocation, indoor location, destination, route, homelocation, visit location, workplace location, business location,personality, mood, emotion, customer behavior, business type, businessactivity, personal activity, wealth, income, purchasing history,shopping history, search history, engagement history, clickstreamhistory, website history, online navigation history, group behavior,family behavior, family membership, customer identity, group identity,business identity, customer profile, business profile, group profile,family profile, declared interest, inferred interest factors, componentavailability, material availability, component location, materiallocation, component pricing, material pricing, taxation, tariff, impost,duty, import regulation, export regulation, border control, traderegulation, customs, navigation, traffic, congestion, vehicle capacity,ship capacity, container capacity, package capacity, vehicleavailability, ship availability, container availability, packageavailability, vehicle location, ship location, container location, portlocation, port availability, port capacity, storage availability,storage capacity, warehouse availability, warehouse capacity,fulfillment center location, fulfillment center availability,fulfillment center capacity, asset owner identity, system compatibility,worker availability, worker competency, worker location, goods pricing,fuel pricing, energy pricing, route availability, route distance, routecost, route safety, and many others.

A blockchain, such as optionally embodying a distributed ledger, may beconfigured with a set of smart contracts to administer a reward for thesubmission of information. In embodiments, a blockchain, such asoptionally distributed in a distributed ledger, may be used to configurea request for information along with terms and conditions related to theinformation, such as a reward for submission of the information, a setof terms and conditions related to the use of the information), andvarious parameters, such as timing parameters, the nature of theinformation required (such as independently validated information likevideo footage, photographs, witnessed statements, or the like), andother parameters.

In embodiments, the market prediction system collects data from a set ofInternet of Things systems that collect information about a set ofentities in a set of environments. In embodiments, the Internet ofThings systems may include smart home Internet of Things devices,workplace Internet of Things devices, Internet of Things devices tomonitor a set of consumer goods stores, and many others, including anyof the Internet of Things devices described throughout this document andthe documents incorporated by reference herein. In embodiments, theInternet of Things systems may be configured to collect information(e.g., behavioral information) about a set of entities in a set ofenvironments.

In embodiments, the entities may include products, suppliers, producers,manufacturers, retailers, businesses, owners, operators, operatingfacilities, customers, consumers, workers, mobile devices, wearabledevices, distributors, resellers, supply chain infrastructurefacilities, supply chain processes, logistics processes, reverselogistics processes, demand prediction processes, demand managementprocesses, demand aggregation processes, machines, ships, barges,warehouses, maritime ports, airports, airways, waterways, roadways,railways, bridges, tunnels, online retailers, ecommerce sites, demandfactors, supply factors, delivery systems, floating assets, points oforigin, points of destination, points of storage, points of use,networks, information technology systems, software platforms,distribution centers, fulfillment centers, containers, containerhandling facilities, customs, export control, border control, drones,robots, autonomous vehicles, hauling facilities, drones/robots/AVs,waterways, port infrastructure facilities, and many others.

In embodiments, the environments may include the home of a consumer,retail facilities, manufacturing facilities, supply chain facilities,ship containers, ship, boat, barge, maritime port, crane, container,container handling facilities, shipyard, maritime dock, warehouse,distribution facilities, fulfillment facilities, fueling facilities,refueling facilities, nuclear refueling facilities, waste removalfacilities, food supply facilities, beverage supply facilities, dronefacilities, robot facilities, autonomous vehicle, aircraft, automotive,truck, train, lift, forklift, hauling facilities, conveyor, loadingdock, waterway, bridge, tunnel, airport, depot, vehicle station, trainstation, weigh station, inspection station or point, roadway, railway,highway, customs house, border control facilities, and many others.

In embodiments, the market prediction system platform 29000 leveragesthe intelligence services system to generate a prediction (e.g., aprediction about a set of markets, a prediction about market share, aprediction about a set of marketplaces, a prediction about a set ofassets, a prediction about the pricing of a set of assets, a predictionabout a set of transactions, a prediction about a parameter of demand, aprediction about a parameter of supply, a prediction about a set ofcontracts, a prediction about a set of smart contracts, a predictionabout the terms or conditions of a smart contract, a prediction about aparty in a market, and many others). In these embodiments, thepredictions may be based on many different sources of data, includingcrowdsourced data, data collected from IoT systems, simulation data(e.g., such as from simulations performed by digital twins), externaldata (e.g., social media data, news data, and the like), and manyothers. In embodiments, the market prediction system platform 29000leverages the intelligence services system for non-predictionintelligence tasks.

In examples, a set of machine-learned models may be used to predict theprice of an asset at some future point in time. In embodiments, a “set”of machine-learned models may include a set with one member. Inembodiments, a “set” of machine-learned models may include a set withmultiple members. In embodiments, a “set” of machine-learned models mayinclude hybrids of different types of models (e.g., hybrids of RNN andCNN). In this example, the intelligent services may receive a request togenerate a prediction and asset data, historical pricing data,discussion board data, and news data from the market prediction systemplatform 29000 and may generate a set of feature vectors based on thereceived data. The intelligent services system may input the featurevector into the set of machine-learned models trained specifically forthe asset (e.g., using a combination of simulation data and real-worlddata) to generate a prediction of the price of the asset at the futurepoint in time and return the prediction to the market prediction systemplatform 29000. In embodiments, the feature vector may include a set ofpredictions, such as ones made by human experts, by other systems,and/or by other models. Such artificial intelligence systems used forprediction (in this example and other examples described throughout thisdisclosure) may include a recurrent neural network (including a gatedrecurrent neural network), a convolutional neural network, a combinationof a recurrent neural network and a convolutional neural network, orother types of neural network or combination or hybrid of types ofneural network described herein or in the documents incorporated byreference herein.

In examples, a set of machine-learned models may be used to predict aparameter of demand for an asset in a forward marketplace usingcrowdsourced data. In this example, the intelligent services may receiveasset data, historical pricing data, and data collected by thecrowdsourcing system from the market prediction system platform 29000and may generate a set of feature vectors based on the received data.The intelligent services system may input the feature vector into theset of machine-learned models trained specifically for the asset (e.g.,using a combination of simulation data and real-world data) to predict aparameter of demand related to that asset and return that prediction tothe market prediction system platform 29000. In embodiments, the featurevector may include a set of predictions, such as ones made by humanexperts, by other systems, and/or by other models. Such artificialintelligence systems used for prediction (in this example and otherexamples described throughout this disclosure) may include a recurrentneural network (including a gated recurrent neural network), aconvolutional neural network, a combination of a recurrent neuralnetwork and a convolutional neural network, or other types of neuralnetwork or combination or hybrid of types of neural network describedherein or in the documents incorporated by reference herein.

In examples, a set of machine-learned models may be used to predict aparameter of demand for an asset in a forward marketplace using datacollected by a set of Internet of Things systems collecting informationfrom a set of entities in a set of environments. In this example, theintelligent services may receive asset data, news data, and datacollected by the set of Internet of Things systems from the marketprediction system platform 29000 and may generate a set of featurevectors based on the received data. The intelligent services system mayinput the feature vector into the set of machine-learned models trainedspecifically for the asset (e.g., using a combination of simulation dataand real-world data) to predict a parameter of demand related to thatasset and return the prediction to the market prediction system platform29000. In embodiments, the feature vector may include a set ofpredictions, such as ones made by human experts, by other systems,and/or by other models. Such artificial intelligence systems used forprediction (in this example and other examples described throughout thisdisclosure) may include a recurrent neural network (including a gatedrecurrent neural network), a convolutional neural network, a combinationof a recurrent neural network and a convolutional neural network, orother types of neural network or combination or hybrid of types ofneural network described herein or in the documents incorporated byreference herein.

In examples, a set of machine-learned models may be used to predict theterms and/or conditions of a smart contract for a transaction related toa set of assets. In this example, the intelligent services may receivepublic smart contract data, data collected by the crowdsourcing system,and data collected from a set of Internet of Things systems about a setof entities in a set of environments from the market prediction systemplatform 29000 and may generate a set of feature vectors based on thereceived data. The intelligent services system may input the featurevector into the set of machine-learned models trained specifically forthe smart contracts related to that set of assets (e.g., using acombination of simulation data and real-world data) to predict the termsand/or conditions of a smart contract related to a transaction for thatset of assets and return the prediction to the market prediction systemplatform 29000. In embodiments, the feature vector may include a set ofpredictions, such as ones made by human experts, by other systems,and/or by other models. Such artificial intelligence systems used forprediction (in this example and other examples described throughout thisdisclosure) may include a recurrent neural network (including a gatedrecurrent neural network), a convolutional neural network, a combinationof a recurrent neural network and a convolutional neural network, orother types of neural network or combination or hybrid of types ofneural network described herein or in the documents incorporated byreference herein.

In embodiments, the market prediction system platform 29000 leveragesthe quantum computing system to generate a quantum prediction (e.g., aprediction about a set of markets, a prediction about market share, aprediction about a set of marketplaces, a prediction about a set ofassets, a prediction about the pricing of a set of assets, a predictionabout a set of transactions, a prediction about a parameter of demand, aprediction about a parameter of supply, a prediction about a set ofcontracts, a prediction about a set of smart contracts, a predictionabout the terms or conditions of a smart contract, a prediction about aparty in a market, and many others). In these embodiments, thepredictions may be based on many different sources of data, includingcrowdsourced data, data collected from IoT systems, external data (e.g.,social media data, news data, and the like), and many others. Inembodiments, the market prediction system platform 29000 leverages thequantum computing system for non-prediction quantum computing tasks. Ineither of these configurations, a quantum computing client may provide arequest to the quantum computing system, whereby the request is toperform a specific quantum computing task. In response, the quantumcomputing system executes the requested task and returns a response tothe quantum computing client.

Quantum Computing Systems

FIG. 291 illustrates an example quantum computing system 29100 accordingto some embodiments of the present disclosure. In embodiments, thequantum computing system 29100 provides a framework for providing a setof quantum computing services to one or more quantum computing clients.In some embodiments, the quantum computing system 29100 framework may beat least partially replicated in respective quantum computing clients.In these embodiments, an individual client may include some or all ofthe capabilities of the quantum computing system 29100, whereby thequantum computing system 29100 is adapted for the specific functionsperformed by the subsystems of the quantum computing client.Additionally, or alternatively, in some embodiments, the quantumcomputing system 29100 may be implemented as a set of microservices,such that different quantum computing clients may leverage the quantumcomputing system 29100 via one or more APIs exposed to the quantumcomputing clients. In these embodiments, the quantum computing system29100 may be configured to perform various types of quantum computingservices that may be adapted for different quantum computing clients. Ineither of these configurations, a quantum computing client may provide arequest to the quantum computing system 29100, whereby the request is toperform a specific task (e.g., an optimization). In response, thequantum computing system 29100 executes the requested task and returns aresponse to the quantum computing client.

Referring to FIG. 291 , in some embodiments, the quantum computingsystem 29100 may include a quantum adapted services library 29102, aquantum general services library 29104, a quantum data services library29106, a quantum computing engine library 29108, a quantum computingconfiguration service 29110, a quantum computing execution system 29112,and quantum computing API interface 29114.

In embodiments, the quantum computing engine library 29108 includesquantum computing engine configurations 29116 and quantum computingprocess modules 29118 based on various supported quantum models. Inembodiments, the quantum computing system 29100 may support manydifferent quantum models, including, but not limited to, the quantumcircuit model, quantum Turing machine, adiabatic quantum computer,spintronic computing system (such as using spin-orbit coupling togenerate spin-polarized electronic states in non-magnetic solids, suchas ones using diamond materials), one-way quantum computer, quantumannealing, and various quantum cellular automata. Under the quantumcircuit model, quantum circuits may be based on the quantum bit, or“qubit”, which is somewhat analogous to the bit in classicalcomputation. Qubits may be in a 1 or 0 quantum state or they may be in asuperposition of the 1 and 0 states. However, when qubits have measuredthe result of a measurement, qubits will always be in is always either a1 or 0 quantum state. The probabilities related to these two outcomesdepend on the quantum state that the qubits were in immediately beforethe measurement. Computation is performed by manipulating qubits withquantum logic gates, which are somewhat analogous to classical logicgates.

In embodiments, the quantum computing system 29100 may be physicallyimplemented using an analog approach or a digital approach. Analogapproaches may include, but are not limited to, quantum simulation,quantum annealing, and adiabatic quantum computation. In embodiments,digital quantum computers use quantum logic gates for computation. Bothanalog and digital approaches may use quantum bits, or qubits.

In embodiments, the quantum computing system 29100 includes a quantumannealing module 29120 wherein the quantum annealing module may beconfigured to find the global minimum or maximum of a given objectivefunction over a given set of candidate solutions (e.g., candidatestates) using quantum fluctuations. As used herein, quantum annealingmay refer to a meta-procedure for finding a procedure that identifies anabsolute minimum or maximum, such as a size, length, cost, time,distance or other measure, from within a possibly very large, butfinite, set of possible solutions using quantum fluctuation-basedcomputation instead of classical computation. The quantum annealingmodule 29120 may be leveraged for problems where the search space isdiscrete (e.g., combinatorial optimization problems) with many localminima, such as finding the ground state of a spin glass or thetraveling salesman problem.

In embodiments, the quantum annealing module 29120 starts from aquantum-mechanical superposition of all possible states (candidatestates) with equal weights. The quantum annealing module 29120 may thenevolve, such as following the time-dependent Schrödinger equation, anatural quantum-mechanical evolution of systems (e.g., physical systems,logical systems, or the like). In embodiments, the amplitudes of allcandidate states change, realizing quantum parallelism according to thetime-dependent strength of the transverse field, which causes quantumtunneling between states. If the rate of change of the transverse fieldis slow enough, the quantum annealing module 29120 may stay close to theground state of the instantaneous Hamiltonian. If the rate of change ofthe transverse field is accelerated, the quantum annealing module 29120may leave the ground state temporarily but produce a higher likelihoodof concluding in the ground state of the final problem energy state orHamiltonian.

In embodiments, the quantum computing system 29100 may includearbitrarily large numbers of qubits and may transport ions to spatiallydistinct locations in an array of ion traps, building large, entangledstates via photonically connected networks of remotely entangled ionchains.

In some implementations, the quantum computing system 29100 includes atrapped ion computer module 29122, which may be a quantum computer thatapplies trapped ions to solve complex problems. Trapped ion computermodule 29122 may have low quantum decoherence and may be able toconstruct large solution states. Ions, or charged atomic particles, maybe confined and suspended in free space using electromagnetic fields.Qubits are stored in stable electronic states of each ion, and quantuminformation may be transferred through the collective quantized motionof the ions in a shared trap (interacting through the Coulomb force).Lasers may be applied to induce coupling between the qubit states (forsingle-qubit operations) or coupling between the internal qubit statesand the external motional states (for entanglement between qubits).

In some embodiments of the invention, a traditional computer, includinga processor, memory, and a graphical user interface (GUI), may be usedfor designing, compiling, and providing output from the execution andthe quantum computing system 29100 may be used for executing the machinelanguage instructions. In some embodiments of the invention, the quantumcomputing system 29100 may be simulated by a computer program executedby the traditional computer. In such embodiments, a superposition ofstates of the quantum computing system 29100 can be prepared based oninput from the initial conditions. Since the initialization operationavailable in a quantum computer can only initialize a qubit to eitherthe |0> or |1> state, initialization to a superposition of states isphysically unrealistic. For simulation purposes, however, it may beuseful to bypass the initialization process and initialize the quantumcomputing service QNTM1114 directly.

In some embodiments, the quantum computing system 29100 provides variousquantum data services, including quantum input filtering, quantum outputfiltering, quantum application filtering, and a quantum database engine.

In embodiments, the quantum computing system 29100 may include a quantuminput filtering service 29124. In embodiments, quantum input filteringservice 29124 may be configured to select whether to run a model on thequantum computing system 29100 or to run the model on a classiccomputing system. In some embodiments, quantum input filtering service29124 may filter data for later modeling on a classic computer. Inembodiments, the quantum computing system 29100 may provide input totraditional compute platforms while filtering out unnecessaryinformation from flowing into distributed systems. In some embodiments,the platform 29100 may trust through filtered specified experiences forintelligent agents.

In embodiments, a system in the system of systems may include a model orsystem for automatically determining, based on a set of inputs, whetherto deploy quantum computational or quantum algorithmic resources to anactivity, whether to deploy traditional computational resources andalgorithms, or whether to apply a hybrid or combination of them. Inembodiments, inputs to a model or automation system may include demandinformation, supply information, financial data, energy costinformation, capital costs for computational resources, developmentcosts (such as for algorithms), energy costs, operational costs(including labor and other costs), performance information on availableresources (quantum and traditional), and any of the many other data setsthat may be used to simulate (such as using any of a wide variety ofsimulation techniques described herein and/or in the documentsincorporated herein by reference) and/or predict the difference inoutcome between a quantum-optimized result and a non-quantum-optimizedresult. A machine learned model (including in a DPANN system) may betrained, such as by deep learning on outcomes or by a data set fromhuman expert decisions, to determine what set of resources to deploygiven the input data for a given request. The model may itself bedeployed on quantum computational resources and/or may use quantumalgorithms, such as quantum annealing, to determine whether, where andwhen to use quantum systems, conventional systems, and/or hybrids orcombinations.

In some embodiments of the invention, the quantum computing system 29100may include a quantum output filtering service 29126. In embodiments,the quantum output filtering service 29126 may be configured to select asolution from solutions of multiple neural networks. For example,multiple neural networks may be configured to generate solutions to aspecific problem and the quantum output filtering service 29126 mayselect the best solution from the set of solutions.

In some embodiments, the quantum computing system 29100 connects anddirects a neural network development or selection process. In thisembodiment, the quantum computing system 29100 may directly program theweights of a neural network such that the neural network gives thedesired outputs. This quantum-programmed neural network may then operatewithout the oversight of the quantum computing system 29100 but willstill be operating within the expected parameters of the desiredcomputational engine.

In embodiments, the quantum computing system 29100 includes a quantumdatabase engine 29128. In embodiments, the quantum database engine 29128is configured with in-database quantum algorithm execution. Inembodiments, a quantum query language may be employed to query thequantum database engine 29128. In some embodiments, the quantum databaseengine may have an embedded policy engine 29130 for prioritizationand/or allocation of quantum workflows, including prioritization ofquery workloads, such as based on overall priority as well as thecomparative advantage of using quantum computing resources versusothers. In embodiments, quantum database engine 29128 may assist withthe recognition of entities by establishing a single identity for thatis valid across interactions and touchpoints. The quantum databaseengine 29128 may be configured to perform optimization of data matchingand intelligent traditional compute optimization to match individualdata elements. The quantum computing system 29100 may include a quantumdata obfuscation system for obfuscating data.

The quantum computing system 29100 may include, but is not limited to,analog quantum computers, digital computers, and/or error-correctedquantum computers. Analog quantum computers may directly manipulate theinteractions between qubits without breaking these actions intoprimitive gate operations. In embodiments, quantum computers that mayrun analog machines include, but are not limited to, quantum annealers,adiabatic quantum computers, and direct quantum simulators. The digitalcomputers may operate by carrying out an algorithm of interest usingprimitive gate operations on physical qubits. Error-corrected quantumcomputers may refer to a version of gate-based quantum computers mademore robust through the deployment of quantum error correction (QEC),which enables noisy physical qubits to emulate stable logical qubits sothat the computer behaves reliably for any computation. Further, quantuminformation products may include, but are not limited to, computingpower, quantum predictions, and quantum inventions.

In some embodiments, the quantum computing system 29100 is configured asan engine that may be used to optimize traditional computers, integratedata from multiple sources into a decision-making process, and the like.The data integration process may involve real-time capture andmanagement of interaction data by a wide range of tracking capabilities.In embodiments, the quantum computing system 29100 may be configured toaccept cookies, email addresses and other contact data, social mediafeeds, news feeds, event and transaction log data (including transactionevents, network events, computational events, and many others), eventstreams, results of web crawling, distributed ledger information(including blockchain updates and state information), results fromdistributed or federated queries of data sources, streams of data fromchat rooms and discussion forums, and many others.

In embodiments, the quantum computing system 29100 includes a quantumregister having a plurality of qubits. Further, the quantum computingsystem 29100 may include a quantum control system for implementing thefundamental operations on each of the qubits in the quantum register anda control processor for coordinating the operations required.

In embodiments, the quantum computing system 29100 is configured tooptimize the pricing of a set of goods or services. In embodiments, thequantum computing system 29100 may utilize quantum annealing to provideoptimized pricing. In embodiments, the quantum computing system 29100may use q-bit based computational methods to optimize pricing.

In embodiments, the quantum computing system 29100 is configured toautomatically discover smart contract configuration opportunities.Automated discovery of smart contract configuration opportunities may bebased on published APIs to marketplaces and machine learning (e.g., byrobotic process automation (RPA) of stakeholder, asset, and transactiontypes.

In embodiments, quantum-established or other blockchain-enabled smartcontracts enable frequent transactions occurring among a network ofparties, and manual or duplicative tasks are performed by counterpartiesfor each transaction. The quantum-established or other blockchain actsas a shared database to provide a secure, single source of truth, andsmart contracts automate approvals, calculations, and other transactingactivities that are prone to lag and error. Smart contracts may usesoftware code to automate tasks, and in some embodiments, this softwarecode may include quantum code that enables extremely sophisticatedoptimized results.

In embodiments, the quantum computing system 29100 or other system inthe system of systems may include a quantum-enabled or other riskidentification module that is configured to perform risk identificationand/or mitigation. The steps that may be taken by the riskidentification module may include, but are not limited to, riskidentification, impact assessment, and the like. In some embodiments,the risk identification module determines a risk type from a set of risktypes. In embodiments, risks may include, but are not limited to,preventable, strategic, and external risks. Preventable risks may referto risks that come from within and that can usually be managed on arule-based level, such as employing operational procedures monitoringand employee and manager guidance and instruction. Strategy risks mayrefer to those risks that are taken on voluntarily to achieve greaterrewards. External risks may refer to those risks that originate outsideand are not in the businesses' control (such as natural disasters).External risks are not preventable or desirable. In embodiments, therisk identification module can determine a predicted cost for manycategories of risk. The risk identification module may perform acalculation of current and potential impact on an overall risk profile.In embodiments, the risk identification module may determine theprobability and significance of certain events. Additionally, oralternatively, the risk identification module may be configured toanticipate events.

In embodiments, the quantum computing system 29100 or other system ofthe platform 29100 is configured for graph clustering analysis foranomaly and fraud detection.

In some embodiments, the quantum computing system 29100 includes aquantum prediction module, which is configured to generate predictions.Furthermore, the quantum prediction module may construct classicalprediction engines to further generate predictions, reducing the needfor ongoing quantum calculation costs, which, can be substantialcompared to traditional computers.

In embodiments, the quantum computing system 29100 may include a quantumprincipal component analysis (QPCA) algorithm that may process inputvector data if the covariance matrix of the data is efficientlyobtainable as a density matrix, under specific assumptions about thevectors given in the quantum mechanical form. It may be assumed that theuser has quantum access to the training vector data in a quantum memory.Further, it may be assumed that each training vector is stored in thequantum memory in terms of its difference from the class means. TheseQPCA algorithms can then be applied to provide for dimension reductionusing the calculational benefits of a quantum method.

In embodiments, the quantum computing system 29100 is configured forgraph clustering analysis for certified randomness for proof-of-stakeblockchains. Quantum cryptographic schemes may make use of quantummechanics in their designs, which enables such schemes to rely onpresumably unbreakable laws of physics for their security. The quantumcryptography schemes may be information-theoretically secure such thattheir security is not based on any non-fundamental assumptions. In thedesign of blockchain systems, information-theoretic security is notproven. Rather, classical blockchain technology typically relies onsecurity arguments that make assumptions about the limitations ofattackers' resources.

In embodiments, the quantum computing system 29100 is configured fordetecting adversarial systems, such as adversarial neural networks,including adversarial convolutional neural networks. For example, thequantum computing system 29100 or other system of the platform 29100 maybe configured to detect fake trading patterns.

In embodiments, the quantum computing system 29100 includes a quantumcontinual learning (QCL) system 29132, wherein the QCL system 29132learns continuously and adaptively about the external world, enablingthe autonomous incremental development of complex skills and knowledgeby updating a quantum model to account for different tasks and datadistributions. The QCL system 29132 operates on a realistic time scalewhere data and/or tasks become available only during operation. Previousquantum states can be superimposed into the quantum engine to providethe capacity for QCL. Because the QCL system 29132 is not constrained toa finite number of variables that can be processed deterministically, itcan continuously adapt to future states, producing a dynamic continuallearning capability. The QCL system 29132 may have applications wheredata distributions stay relatively static, but where data iscontinuously being received. For example, the QCL system 29132 may beused in quantum recommendation applications or quantum anomaly detectionsystems where data is continuously being received and where the quantummodel is continuously refined to provide for various outcomes,predictions, and the like. QCL enables asynchronous alternate trainingof tasks and only updates the quantum model on the real-time dataavailable from one or more streaming sources at a particular moment.

In embodiments, the QCL system 29132 operates in a complex environmentin which the target data keeps changing based on a hidden variable thatis not controlled. In embodiments, the QCL system 29132 can scale interms of intelligence while processing increasing amounts of data andwhile maintaining a realistic number of quantum states. The QCL system29132 applies quantum methods to drastically reduce the requirement forstorage of historic data while allowing the execution of continuouscomputations to provide for detail-driven optimal results. Inembodiments, a QCL system 29132 is configured for unsupervised streamingperception data since it continually updates the quantum model with newavailable data.

In embodiments, QCL system 29132 enables multi-modal-multi-task quantumlearning. The QCL system 29132 is not constrained to a single stream ofperception data but allows for many streams of perception data fromdifferent sensors and input modalities. In embodiments, the QCL system29132 can solve multiple tasks by duplicating the quantum state andexecuting computations on the duplicate quantum environment. A keyadvantage to QCL is that the quantum model does not need to be retrainedon historic data, as the superposition state holds information relatingto all prior inputs. Multi-modal and multi-task quantum learning enhancequantum optimization since it endows quantum machines with reasoningskills through the application of vast amounts of state information.

In embodiments, the quantum computing system 29100 supports quantumsuperposition, or the ability of a set of states to be overlaid into asingle quantum environment.

In embodiments, the quantum computing system 29100 supports quantumteleportation. For example, information may be passed between photons onchipsets even if the photons are not physically linked.

In embodiments, the quantum computing system 29100 may include a quantumtransfer pricing system. Quantum transfer pricing allows for theestablishment of prices for the goods and/or services exchanged betweensubsidiaries, affiliates, or commonly controlled companies that are partof a larger enterprise and may be used to provide tax savings forcorporations. In embodiments, solving a transfer pricing probleminvolves testing the elasticities of each system in the system ofsystems with a set of tests. In these embodiments, the testing may bedone in periodic batches and then may be iterated. As described herein,transfer pricing may refer to the price that one division in a companycharges another division in that company for goods and services.

In embodiments, the quantum transfer pricing system consolidates allfinancial data related to transfer pricing on an ongoing basisthroughout the year for all entities of an organization wherein theconsolidation involves applying quantum entanglement to overlay datainto a single quantum state. In embodiments, the financial data mayinclude profit data, loss data, data from intercompany invoices(potentially including quantities and prices), and the like.

In embodiments, the quantum transfer pricing system may interface with areporting system that reports segmented profit and loss, transactionmatrices, tax optimization results, and the like based on superpositiondata. In embodiments, the quantum transfer pricing system automaticallygenerates forecast calculations and assesses the expected local profitsfor any set of quantum states.

In embodiments, the quantum transfer pricing system may integrate with asimulation system for performing simulations. Suggested optimal valuesfor new product prices can be discussed cross-border via integratedquantum workflows and quantum teleportation communicated states.

In embodiments, quantum transfer pricing may be used to proactivelycontrol the distribution of profits within a multi-national enterprise(MNE), for example, during the course of a calendar year, enabling theentities to achieve arms-length profit ranges for each type oftransaction.

In embodiments, the QCL system 29132 may use a number of methods tocalculate quantum transfer pricing, including the quantum comparableuncontrolled price (QCUP) method, the quantum cost plus percent method(QCPM), the quantum resale price method (QRPM), the quantum transactionnet margin method (QTNM), and the quantum profit-split method.

The QCUP method may apply quantum calculations to find comparabletransactions made between related and unrelated organizations,potentially through the sharing of quantum superposition data. Bycomparing the price of goods and/or services in an intercompanytransaction with the price used by independent parties through theapplication of a quantum comparison engine, a benchmark price may bedetermined.

The QCPM method may compare the gross profit to the cost of sales, thusmeasuring the cost-plus mark-up (the actual profit earned from theproducts). Once this mark-up is determined, it should be equal to what athird party would make for a comparable transaction in a comparablecontext with similar external market conditions. In embodiments, thequantum engine may simulate the external market conditions.

The QRPM method looks at groups of transactions rather than individualtransactions and is based on the gross margin or difference between theprice at which a product is purchased and the price at which it is soldto a third party. In embodiments, the quantum engine may be applied tocalculate the price differences and to record the transactions in thesuperposition system.

The QTNM method is based on the net profit of a controlled transactionrather than comparable external market pricing. The calculation of thenet profit is accomplished through a quantum engine that can consider awide variety of factors and solve optimally for the product price. Thenet profit may then be compared with the net profit of independententerprises, potentially using quantum teleportation.

The quantum profit-split method may be used when two related companieswork on the same business venture, but separately. In theseapplications, the quantum transfer pricing is based on profit. Thequantum profit-split method applies quantum calculations to determinehow the profit associated with a particular transaction would have beendivided between the independent parties involved.

In embodiments, the quantum computing system 29100 may leverage one orartificial networks to fulfill the request of a quantum computingclient. For example, the quantum computing system 29100 may leverage aset of artificial neural networks to identify patterns in images (e.g.,using image data from a liquid lens system), perform binary matrixfactorization, perform topical content targeting, performsimilarity-based clustering, perform collaborative filtering, performopportunity mining, or the like.

In embodiments, the system of systems may include a hybrid computingallocation system for prioritization and allocation of quantum computingresources and traditional computing resources. In embodiments, theprioritization and allocation of quantum computing resources andtraditional computing resources may be measure-based (e.g., measuringthe extent of the advantage of the quantum resource relative to otheravailable resources), cost-based, optimality-based, speed-based,impact-based, or the like. In some embodiments the hybrid computingallocation system is configured to perform time-division multiplexingbetween the quantum computing system 29100 and a traditional computingsystem. In embodiments, the hybrid computing allocation system mayautomatically track and report on the allocation of computationalresources, the availability of computational resources, the cost ofcomputational resources, and the like.

In embodiments, the quantum computing system 29100 may be leveraged forqueue optimization for utilization of quantum computing resources,including context-based queue optimizations.

In embodiments, the quantum computing system 29100 may supportquantum-computation-aware location-based data caching.

In embodiments, the quantum computing system 29100 may be leveraged foroptimization of various system resources in the system of systems,including the optimization of quantum computing resources, traditionalcomputing resources, energy resources, human resources, robotic fleetresources, smart container fleet resources, I/O bandwidth, storageresources, network bandwidth, attention resources, or the like.

The quantum computing system 29100 may be implemented where a completerange of capabilities are available to or as part of any configuredservice. Configured quantum computing services may be configured withsubsets of these capabilities to perform specific predefined function,produce newly defined functions, or various combinations of both.

FIG. 292 illustrates quantum computing service request handlingaccording to some embodiments of the present disclosure. A directedquantum computing request 29202 may come from one or more quantum-awaredevices or stack of devices, where the request is for known applicationconfigured with specific quantum instance(s), quantum computingengine(s), or other quantum computing resources, and where dataassociated with the request may be preprocessed or otherwise optimizedfor use with quantum computing.

A general quantum computing request 29204 may come from any system inthe system of systems or configured service, where the requestor hasdetermined that quantum computing resources may provide additional valueor other improved outcomes. Improved outcomes may also be suggested bythe quantum computing service in association with some form ofmonitoring and analysis. For a general quantum computing request 29204,input data may not be structured or formatted as necessary for quantumcomputing.

In embodiments, external data requests 29206 may include any availabledata that may be necessary for training new quantum instances. Thesources of such requests could be public data, sensors, ERP systems, andmany others.

Incoming operating requests and associated data may be analyzed using astandardized approach that identifies one or more possible sets of knownquantum instances, quantum computing engines, or other quantum computingresources that may be applied to perform the requested operation(s).Potential existing sets may be identified in the quantum set library29208.

In embodiments, the quantum computing system 29100 includes a quantumcomputing configuration service 29110. The quantum computingconfiguration service may work alone or with the intelligence service29134 to select a best available configuration using a resource andpriority analysis that also includes the priority of the requestor. Thequantum computing configuration service may provide a solution (YES) ordetermine that a new configuration is required (NO).

In one example, the requested set of quantum computing services may notexist in the quantum set library 29208. In this example, one or more newquantum instances must be developed (trained) with the intelligenceservice 29134 using available data. In embodiments, alternateconfigurations may be developed with assistance from the intelligenceservice 29134 to identify alternate ways to provide all or some of therequested quantum computing services until appropriate resources becomeavailable. For example, a quantum/traditional hybrid model may bepossible that provides the requested service, but at a slower rate.

In embodiments, alternate configurations may be developed withassistance from the intelligence service 29134 to identify alternate andpossibly temporary ways to provide all or some of the requested quantumcomputing services. For example, a hybrid quantum/traditional model maybe possible that provides the requested service, but at a slower rate.This may also include a feedback learning loop to adjust services inreal time or to improved stored library elements.

When a quantum computing configuration has been identified andavailable, it is allocated and programmed for execution and delivery ofone or more quantum states (solutions).

Trust Networks

Although cryptocurrencies have experienced growth, the mainstreamutility of cryptocurrencies as a medium of exchange may be more limiteddue to lack of payer protections. For example, cryptocurrency funds sentto a fraudulent party may not be readily recovered. A trust network29300 of the present disclosure generates consensus trust scores forcryptocurrency transactors. The consensus trust scores can offercryptocurrency transactors a safeguard against fraud while preservinguser anonymity and autonomy. The consensus trust scores may provide abaseline level of trust upon which other security layers can be built,including cryptocurrency payment insurance, protection, and restitution.

A trust network 29300 generates consensus trust scores forcryptocurrency transactors. For example, for a cryptocurrency based onblockchain technology, the trust network 29300 can generate consensustrust scores for different blockchain addresses that interact on theblockchain. The trust network 29300 may determine the consensus trustscores based on data retrieved from various data sources (e.g.,fraud/custody data) along with blockchain data upon which thecryptocurrency is based. A trust score (e.g., a consensus trust score)may be a number (e.g., a decimal or integer) that indicates a likelihoodthat the blockchain address is involved in fraudulent activity. Putanother way, a trust score can represent the propensity of a blockchainaddress to be involved with fraudulent activity.

A cryptocurrency transactor can request consensus trust scores from thetrust network 29300 before engaging in a cryptocurrency blockchaintransaction in which funds (e.g., cryptocurrency blockchain tokens) aretransacted on the blockchain. In general, a cryptocurrency transactorcan use a consensus trust score to determine whether the blockchainaddress with which they are transacting is trustworthy. For example, atransactor that intends to send funds to a receiving party may request aconsensus trust score for the receiving party. In this example, thetransactor can use the consensus trust score for the intended receiverin order to evaluate the likelihood that the intended receiver is afraudulent party.

Transactors can use consensus trust scores to take a variety of actions.For example, transactors may use consensus trust scores to determinewhether to proceed with or cancel a blockchain transaction. As anotherexample, transactors (e.g., digital exchanges) can use consensus trustscores to determine whether to insure a transaction. As another example,organizations can use consensus trust scores to decide whether to acceptfunds from a blockchain address. As such, the consensus trust scoresdescribed herein can help protect transactors from falling victim tofraud or from receiving fraudulent funds. Note that the consensus trustscores inform the transactors of the degree to which any cryptocurrencyaddress may be trusted without requiring the transactor to know theidentity of the party behind the address. As such, the consensus trustscores may preserve transactor anonymity.

FIG. 293 illustrates an example trust network 29300 in communicationwith cryptocurrency transactor computing devices 29302, 29304, 29306(hereinafter “transactor computing devices”) via a communication network29308. The network 29308 may include various types of computer networks,such as a local area network (LAN), wide area network (WAN), and/or theInternet. The trust network 29300 may include a plurality of trust nodes29300-1, 29300-2, . . . , 29300-N (referred to herein as “nodes”). Eachof the nodes 29300 may include one or more node computing devices (e.g.,one or more server computing devices) that implement a variety ofprotocols described herein.

The nodes 29300 may acquire data associated with cryptocurrencyblockchain addresses and determine a variety of trust scores based onthe acquired data. A trust score determined locally at a node based onthe acquired data may be referred to as a “local node trust score” or a“local trust score.” The nodes 29300 may be configured to communicatetheir local trust scores among one another such that each node may haveknowledge of local trust scores associated with other nodes. After anode acquires a plurality of local trust scores, the node may determinea candidate consensus trust score (hereinafter “candidate trust score”)based on the plurality of local trust scores. One or more nodes maydetermine a consensus trust score based on the plurality of candidatetrust scores. The consensus trust score may indicate a consensus valuefor a local trust score among a plurality of nodes. The consensus trustscore for a cryptocurrency address can be written to a distributedconsensus ledger and later retrieved from the trust network 29300 (e.g.,in response to a trust request).

The trust scores described herein (e.g., local, candidate, or consensus)can be calculated/provided in a variety of formats. In someimplementations, a trust score may be an integer value with a minimumand maximum value. For example, a trust score may range from 1-7, wherea trust score of ‘1’ indicates that the blockchain address is likelyfraudulent. In this example, a trust score of ‘7’ may indicate that theblockchain address is not likely fraudulent (i.e., very trustworthy). Insome implementations, a trust score may be a decimal value. For example,the trust score may be a decimal value that indicates a likelihood offraud (e.g., a percentage value from 0-100%). In some implementations, atrust score may range from a maximum negative value to a maximumpositive value (e.g., −1.00 to 1.00), where a larger negative valueindicates that the address is more likely fraudulent. In this example, alarger positive value may indicate that the address is more likelytrustworthy. The customer may select the trust score format they prefer.

The distributed trust network 29300 described herein distributes thetrust score computational workload across a plurality of nodes toproduce a resilient network that is resistant to failure/outage andattack. In some implementations, the trust network 29300 may include abuilt-in transactional autonomy moderated by a token (e.g., UTOKEN) thatallows the trust network 29300 to distribute the computational workload.Additionally, distributing trust calculations throughout the network mayprovide a resistance to fraud/conspiracy intended to corrupt thenetwork.

The transactor computing devices 29302, 29304, 29306 include computingdevices that can interact with the trust network 29300. Exampletransactor computing devices may include user transactor devices 29302,such as smartphones, tablets, laptop computers, desktop computers, orother computing devices. A user transactor device 29302 may include anoperating system 29310 and a plurality of applications, such as a webbrowser application 29312 and additional applications 29314.

A user transactor device 29302 can include a transaction application29316 that can transact with a cryptocurrency blockchain network 29318(hereinafter “cryptocurrency network 29318”) to perform blockchaintransactions. The transaction application 29316 can also requestconsensus trust scores from the trust network 29300. Some exampletransaction applications may be referred to as “wallet applications.” Insome cases, a transaction application may be referred to as a“decentralized wallet application” if the decentralized walletapplication does not interact with centralized server-side components.

Additional example transactor devices may be included in intermediatetransaction systems 29304. An intermediate transaction system 29304(e.g., one or more server computing devices) may communicate with thecryptocurrency network 29318, user transactor devices 29302, and thetrust network 29300. An intermediate transaction system 29304 canperform cryptocurrency transactions on behalf of the user transactordevices 29302. The intermediate transaction system 29304 can alsoacquire consensus trust scores from the trust network 29300 on behalf ofthe user transactor devices 29302. In some implementations, theintermediate transaction system 29304 can provide a user interface forthe user transactor devices 29302 (e.g., via a web-based interfaceand/or an installed transaction application 29316). An exampleintermediate transaction system 29304 may include a digital currencyexchange (e.g., Coinbase, Inc. of San Francisco CA). In someimplementations, exchanges may be decentralized.

Additional example transactor devices may be included in automatedtransaction systems 29306. An automated transaction system 29306 (e.g.,one or more server computing devices) may communicate with the trustnetwork 29300 and the cryptocurrency network 29318. Example automatedtransaction systems 29306 may include payment systems, such as a paymentsystem or gateway that makes recurring payments (e.g., Stripe, Inc. ofSan Francisco CA or Plaid Inc. of San Francisco CA).

The transactor devices 29302, 29304, 29306 can engage in transactions onthe cryptocurrency network 29318. A cryptocurrency network 29318 may beformed by a network of computing devices that each operate according tocryptocurrency blockchain protocols 29320. The cryptocurrency network29318 may control a cryptocurrency blockchain transaction ledger 29322(hereinafter “cryptocurrency ledger 29322”). The cryptocurrency ledger29322 includes a list of transactions between different cryptocurrencyblockchain addresses. The cryptocurrency ledger 29322 may also includeadditional data, such as transaction metadata. Example cryptocurrencynetworks 29318 may include, but are not limited to, Bitcoin, BitcoinCash, Ethereum, and Litecoin. Although a single cryptocurrency networkis illustrated in FIG. 293 , the trust network 29300 can provideconsensus trust scores for addresses on multiple differentcryptocurrency blockchain networks using the techniques describedherein.

A cryptocurrency ledger 29322 may include cryptocurrency blockchainaddresses that identify transactors on the cryptocurrency network 29318.A transactor may refer to a party that controls transactions for acryptocurrency blockchain address. For example, a transactor may includean individual or an organization, such as a business, a non-governmentalorganization, or a decentralized autonomous organization. A transactorcan control one or more cryptocurrency blockchain addresses on a singlecryptocurrency network. A transactor can also have one or morecryptocurrency blockchain addresses on different cryptocurrencynetworks.

A transactor can initiate a blockchain transaction in which thetransactor's blockchain address sends/receives funds to/from anotherblockchain address. A blockchain address that sends funds to anotherblockchain address may be referred to herein as a “blockchain senderaddress” or a “sender address.” The blockchain address that receivesfunds may be referred to herein as a “blockchain receiver address” or a“receiver address.”

The transactor devices 29302, 29304, 29306 can send trust requests tothe trust network 29300 and receive trust responses from the trustnetwork 29300 (e.g., see FIGS. 294-296 ). The trust request may indicateone or more cryptocurrency blockchain addresses for which the transactorwould like a trust report (e.g., one or more consensus trust scores). Insome implementations, the trust request can include a request payment,such as a blockchain token and/or fiat currency (e.g., United StatesDollars). The request payment may be distributed to nodes in the trustnetwork 29300 as payment for providing the consensus trust score(s).

In one example, a transactor device can send a trust request to thetrust network 29300 and receive a trust response (e.g., trust report)from the trust network. The transactor device and trust network 29300may communicate via an application programming interface (API). Thetrust request may include a cryptocurrency blockchain address for thetransactor on the other side of the transaction. For example, a trustrequest from a sender may request a trust report for the receiver'sblockchain address. The sender may make a decision based on the receivedtrust report, such as whether to engage in the cryptocurrency blockchaintransaction with the receiver.

FIGS. 294-296 illustrate interactions between different transactordevices/systems 29302, 29304, 29306, the cryptocurrency network 29318,and the trust network 29300. In FIG. 294 , the user transactor device29302 includes a transaction application 29316 (e.g., a walletapplication) that transacts with the cryptocurrency network 29318. Thetransaction application 29316 includes a trust request module 29326 thatinterfaces with the trust network 29300. For example, the trust requestmodule 29326 can generate the trust request 29330 (e.g., a web request).The trust request module 29326 can also receive the trust response 29332from the trust network 29300. In some implementations, the trust requestmodule 29326 can generate a graphical user interface (GUI) that the usermay interact with in order to send the trust request 29330 and view thetrust report 29332.

In FIG. 295 , a transactor device 29302 can transact on thecryptocurrency network 29318 via an intermediate transaction system29304. For example, in FIG. 295 , the transactor device 29302 caninclude a web browser application 29312 that interacts with theintermediate transaction system 29304. The intermediate transactionsystem 29304 (e.g., a web server) can provide an interface to the webbrowser 29312 for transacting on the cryptocurrency network 29318. Theintermediate transaction system 29304 may also provide an interface(e.g., a web-based interface) for the user to select whether the userwants a trust report before engaging in the blockchain transaction.

In FIG. 296 , an automated transaction system 29306 controlstransactions on the cryptocurrency network 29318. The automatedtransaction system 29306 can also request trust reports from the trustnetwork 29300. In some implementations, the transactions engaged in bythe automated transaction system 29306 may depend on the consensus trustscores reported by the trust network 29300. For example, the automatedtransaction system 29306 can engage in transactions if the trust scoreindicates that the address is unlikely to be engaged in fraudulentactivity.

Although the devices/systems described herein may make a trust request29330 in order to receive consensus trust scores before making acryptocurrency blockchain transaction, in some implementations, otherdevices/systems may request consensus trust scores in other scenarios.For example, compliance officers at an exchange may request consensustrust scores for compliance reasons.

Referring to FIG. 297 , in some implementations, the trust network 29300may implement a fraud alert protocol that can automatically notifynetwork participants (e.g., fraud alert requesting devices) ofpotentially fraudulent cryptocurrency blockchain addresses. For example,a node may include a fraud alert module 29334 that is configured toprovide fraud alerts 29336 under a set of fraud alert criteria that maybe configured by a user. In one example, a fraud alert module 29334 maymonitor one or more cryptocurrency addresses and provide a fraud alert29336 if the consensus trust score for any address drops below athreshold level of trustworthiness (e.g., as set by a user). In anotherexample, a fraud alert 29336 may be sent if a monitored trust scorechanges by more than a threshold percentage. In some implementations, afraud alert protocol may be implemented using a smart contract thatmonitors a consensus trust score and provides alerts according to a setof business rules that may be defined by a user. In someimplementations, a node may be required to stake an amount of UTOKEN tobe eligible to receive fraud alerts.

In some implementations, nodes may be connected via a network of statechannels. When a cryptocurrency transactor issues a trust request andpayment (e.g., UTOKEN), the request can be gossiped until it reaches anode that has the requested consensus trust score. This node can returnthe consensus trust score to the cryptocurrency transactor. Payment canthen be granted according to the reward protocol.

Example transactors may include, but are not limited to, a custodialexchange, a non-custodial exchange, a custodial wallet, a non-custodialwallet, a new token developer and seller, decentralized applications,blockchain enabled merchants, node operators, algorithm suppliers, andproof of work security providers.

A custodial exchange may refer to an entity (e.g., a company) thatenables the exchange of cryptoassets while holding the assets on behalfof their customers. Custodial exchanges may use the consensus trustscore to evaluate whether depositing cryptoassets are fraudulent,helping to ensure that their service is not used to launder money.Additionally, a custodial exchange may receive alerts to monitor theblockchain addresses they have in custody. A non-custodial exchange mayrefer to an entity (e.g., company) that enables the exchange ofcryptoassets without holding the cryptoassets on behalf of the tokenpurchaser or seller. Non-custodial exchanges may use the consensus trustscore to evaluate the trustworthiness of a counterparty.

A custodial wallet may refer to an entity (e.g., a company) that holdsprivate keys on behalf of customers and enables them to send and receivecryptoassets. Custodial wallets may use the consensus trust score toevaluate whether cryptoassets being deposited are fraudulent and toreceive fraud alerts. They may also use the consensus trust score toprotect their users from sending cryptoassets to fraudulent addresses. Anon-custodial wallet may refer to an entity (e.g., a company) that makessoftware that allows individuals to hold and transact cryptoassetslocally on personal devices. Non-custodial wallets may use the consensustrust score to protect their users from sending cryptoassets tofraudulent addresses and from receiving fraudulent funds.

A new token developer and seller may refer to an entity (e.g., a companyor individual) that creates software that, when run by a network ofpeers, creates a new decentralized token. This company may also sellsome initial distribution of the token to interested buyers. Thesetransactors may perform an initial coin offering (ICO). A new tokendeveloper and seller may use the consensus trust score to ensure fundsbeing used to purchase their token are not fraudulent, ensuring thatthey are selling tokens in a compliant manner.

A decentralized application may refer to an application that runs on adecentralized network. These may include applications that manage money,applications where money is involved but require another piece, andother applications, which includes voting and governance systems.Decentralized applications may use the consensus trust score for anyactivity involving the evaluation of counterparty trust, in addition toprotecting participants against fraud.

A blockchain-enabled merchant may refer to an entity (e.g., a company)that accepts payment in the form of cryptoassets (e.g., securitytokens). Blockchain-enabled merchants may use the consensus trust scoreto ensure funds being used as payment are not fraudulent. They may alsoreceive alerts on the addresses with which they interact.

Referring back to FIG. 293 , the environment includes data sources 29324that the trust network 29300 may use to determine whether blockchainaddresses are fraudulent. Example data sources 29324 described hereininclude fraud data sources and custody data sources. The trust network29300 may determine local trust scores based on the data included in thedata sources 29324 along with the data included in the cryptocurrencyledger 29322.

FIG. 298 illustrates an example method that describes operation of theenvironment illustrated in FIGS. 293-296 . For example, the method ofFIG. 298 illustrates the determination of local trust scores, candidatetrust scores, and a consensus trust score for a single cryptocurrencyblockchain address. The method of FIG. 298 may be performed multipletimes to determine local trust scores, candidate trust scores, andconsensus trust scores for multiple cryptocurrency blockchain addresses.

In block 29400, the nodes 29300 acquire and process fraud and custodydata 29324 associated with a cryptocurrency address. In block 29402, thenodes 29300 acquire and process cryptocurrency blockchain dataassociated with the cryptocurrency address. In block 29404, the nodes29300 each determine local trust scores for the cryptocurrency addressbased on the data acquired in blocks 29400-29402.

In block 29406, the nodes 29300 communicate the local trust scores forthe cryptocurrency address with one another. After communication of thelocal trust scores, each of the nodes may include a plurality of localtrust scores calculated by other nodes. In block 29408, the nodes 29300determine candidate trust scores for the cryptocurrency address based onthe local trust scores. In block 29410, the nodes 29300 determine aconsensus trust score for the cryptocurrency address based on thecandidate trust scores for the cryptocurrency addresses. In block 29412,the nodes 29300 can update a distributed consensus trust score ledger toinclude the calculated consensus trust score. In blocks 29414-29416, thetrust network 29300 receives a trust request 29330 for thecryptocurrency address from a requesting device and sends a trustresponse 29332, including the consensus trust score, to the requestingdevice.

FIG. 299 illustrates generation of local trust scores. FIGS. 300-301illustrate generation of consensus trust scores. In addition togenerating trust scores, the trust network 29300 may implementadditional features described with respect to FIGS. 302-304 . In someimplementations, the trust network 29300 may implement a reputationprotocol that calculates and stores reputation values that indicate avariety of parameters associated with the nodes, such as an amount ofwork performed by the nodes (e.g., see FIG. 302 ).

In some implementations, the trust network 29300 may implement a tokeneconomy that operates as a medium of exchange in the trust network29300. The token economy described herein uses a token referred to as“UTOKEN.” Each of the nodes may implement a wallet module (e.g., seeFIG. 303 ) that can send, receive, stake, and burn UTOKEN according tothe protocols implemented in the trust network.

In some implementations, the trust network 29300 may implement a rewardprotocol that tracks various parameters of the nodes (e.g., an amount ofwork) and rewards the nodes using UTOKEN (e.g., see FIGS. 303-304 ). Thetrust network 29300 may also implement a staking protocol thatdetermines the functionality of the nodes and penalizes certain nodebehaviors (e.g., the production of fraudulent data).

Different nodes in the trust network 29300 may be configured toimplement different features of the trust network 29300. For example,different nodes may be configured to implement different protocols, orportions of protocols, described herein. In some implementations, thestaking protocol may determine which features the nodes may implement.The modules and data stores included in the nodes may represent theprotocols implemented by the nodes and the data stored by the nodes.Each node may include one or more computing devices. In someimplementations, multiple nodes may be run on a single computing device.

FIG. 299 illustrates an example node 29300-1 that includes a trust scoredetermination module 29500 and a local trust data store 29502. The trustscore determination module 29500 acquires and processes a variety ofdata described herein, such as fraud and custody data 29324 along withblockchain data. The local trust data store 29502 can store data for aplurality of cryptocurrency addresses.

The data associated with a single cryptocurrency address is illustratedherein as a blockchain address record 29504. The local trust data store29502 may include a plurality of such blockchain address records, eachfor a different cryptocurrency address. Each blockchain address record29504 can include a blockchain address 29506 that uniquely identifiesthe record 29504. Each blockchain address record 29504 can also includea local trust score 29508 associated with the blockchain address 29506.The blockchain address record 29504 may include a history of local trustscores calculated over time for the blockchain address.

The blockchain address record 29504 described herein represents datastored in the local trust data store 29502. The node 29300-1 may includea variety of different data structures that are used to implement thedata. Accordingly, the blockchain address record 29504 may beimplemented using one or more different data structures than explicitlyillustrated herein.

In FIG. 299 , the trust score determination module 29500 acquires andprocesses a variety of types of data, such as custody data and frauddata 29324. Example fraud and custody data may include data thatprovides evidence of fraud with respect to a cryptocurrency addressand/or indicates the party that owns/controls the cryptocurrencyaddress. The trust score determination module 29500 may store custodyand fraud data related to a cryptocurrency address in the blockchainaddress record 29504. The trust score determination module 29500 mayalso generate a fraud label that indicates whether the cryptocurrencyaddress is likely fraudulent based on the acquired fraud data.

The trust score determination module 29500 acquires and processesblockchain data (e.g., the cryptocurrency ledger 29322). The trust scoredetermination module 29500 may store raw and processed blockchain datarelevant to a cryptocurrency address in the blockchain address record29504. Example cryptocurrency blockchain data may include data for aplurality of blockchain transactions between a plurality of differentcryptocurrency addresses.

The trust score determination module 29500 determines local trust scoresfor the cryptocurrency addresses based on the acquired blockchain dataand fraud/custody data. In some implementations, the trust scoredetermination module 29500 may generate a blockchain graph datastructure based on the blockchain data (e.g., see FIG. 317 ). The trustscore determination module 29500 may also process the graph to determineone or more graph-based values (e.g., importance values) that may beused to generate local trust scores.

In some implementations (e.g., see FIG. 318 ), the trust scoredetermination module 29500 may generate scoring features forcryptocurrency addresses and generate one or more scoring models basedon the scoring features and other data (e.g., labeled fraud data). Inthese implementations, the trust score determination module 29500 maygenerate one or more local trust scores for blockchain addresses usingone or more scoring models and the scoring features associated with theblockchain addresses. FIGS. 312-319 illustrate a more detailedimplementation of the trust score determination module 29500 and thelocal trust data store 29502.

A plurality of nodes can communicate with one another in order to cometo a consensus trust score for a cryptocurrency address. Each node maybe identified (e.g., uniquely identified) by a node identifier (ID). Insome implementations, a public/private key pair is generated uponinception of a node. In these implementations, a node's public key mayserve as a node ID, although other identifiers may be used.

Different nodes may compute the same/similar local trust scores in caseswhere the different nodes have access to the same/similar cryptocurrencyblockchain data and fraud/custody data. In some cases, the local trustscores may differ among nodes. For example, the local trust scores maydiffer when nodes have access to different fraud and custody data. In aspecific example, nodes located in different jurisdictions (e.g.,countries) may have access to data sources that are blocked in otherjurisdictions. In another specific example, some nodes may accessinformation at different rates.

The nodes 29300 implement a trust consensus protocol that determinesconsensus trust scores for cryptocurrency addresses. The consensus trustscores can be stored in a consensus trust score ledger 29600 that isdistributed to nodes across the trust network 29300. FIG. 300illustrates an example node 29300-1 that includes a consensusdetermination module 29510 and a trust consensus data store 29512(hereinafter “consensus data store 29512”). The trust network 29300 caninclude a plurality of nodes that include the functionality describedwith respect to FIGS. 300-301 . The consensus determination module 29510can communicate with other consensus determination modules (e.g., viacommunication module 29510-1) of other nodes to determine consensustrust scores. The consensus data store 29512 includes the consensustrust scores and other data in a consensus trust score ledger 29600.

The consensus determination module 29510 can communicate with othernodes (e.g., consensus modules of other nodes). For example, each nodemay communicate its local trust score to other nodes via outgoing trustconsensus messages 29602. Additionally, each node may receive localtrust scores from other nodes via incoming trust consensus messages29604. An example trust consensus message may include a node ID, a nodeIP address, a cryptocurrency blockchain address and an associated localtrust score. In some cases, instead of indicating an associated localtrust score, a trust consensus message may indicate that a local trustscore has not been calculated or is in the process of being calculated.

The consensus determination module 29510 can generate a local trustscore list 29606 (“trust score list 29606”) based on the local trustscores received from other nodes (e.g., using list building module29510-2). The trust score list 29606 may include a list of node IDs andcorresponding local trust scores for a cryptocurrency address. Theconsensus determination module 29510 may generate a local trust scorelist 29606 for each cryptocurrency address. Each node can communicateits trust score list to other nodes. For example, a node cansend/receive trust score messages that include trust score lists. A nodecan update the local trust score list based on other received trustscore lists.

Each node in the trust network 29300 may be configured to communicatewith different sets of nodes. Put another way, nodes in the trustnetwork 29300 may be configured to communicate with non-overlapping setsof nodes. Since different nodes may communicate with different sets ofother nodes, eventually, each of the nodes communicating local trustscores with one another may have knowledge of other nodes' local trustscore calculations. In this scenario, different nodes may includesimilar trust score lists. In some examples, the trust scores in thetrust score lists may converge in fractions of a second or a matter ofseconds.

The trust score list 29606 for a cryptocurrency address may include afrequency (count) distribution of local trust scores. In some cases, thetrust score list 29606 may include a large number of local trust scoreswithin a tight grouping. In some cases, a trust score list 29606 mayinclude outlier trust scores that have values outside of a majorgrouping. For example, an outlier may be due to variations ininformation used to produce the local trust scores. As another example,one or more outliers may be caused by nodes that areproducing/distributing fraudulent trust scores. As described herein,nodes that produce/distribute fraudulent trust scores may be heldaccountable (e.g., via burning of staked funds).

The consensus determination module 29510 determines a candidate trustscore based on the local trust scores included in the trust score list29606 (e.g., using candidate determination module 29510-3). In someimplementations, the consensus determination module 29510 may include“candidate determination criteria” that trigger the determination of acandidate trust score. Example candidate determination criteria mayinclude the presence of local trust scores for a threshold number ofnodes and/or a threshold fraction of nodes. For example, the consensusdetermination module 29510 may determine a candidate trust score inresponse to the presence of a threshold number/fraction of local trustscores included in the trust score list.

In some implementations, the consensus determination module 29510 maydetermine a candidate trust score in response to the distributionpattern of trust scores in the trust score list. For example, theconsensus determination module 29510 may be triggered to determine acandidate trust score when the trust scores are centered in adistribution (e.g., tightly centered in a single distribution). If thetrust score distribution includes outliers, the consensus determinationmodule 29510 may continue communicating local trust scores with theother nodes. In a specific example, the consensus determination module29510 may be triggered to determine a candidate trust score when thevariance of a distribution is less than a threshold variance. In caseswhere there are multiple modes of distribution, the consensusdetermination module 29510 may determine whether the modes are valid orwhether the modes are due to fraudulent trust scores. Similarly, if thevariance of the distribution is too great (e.g., greater than athreshold value), the consensus determination module 29510 may determinewhether the variance is due to variations in calculations and/orfraudulent behavior. The consensus determination module 29510 may filterout (i.e., remove) trusts scores that are attributable to fraudulentbehavior before determining a candidate trust score.

The consensus determination module 29510 can determine the candidatetrust score using a variety of techniques. In some implementations, theconsensus determination module 29510 may remove outlier local trustscores from the trust score list before determining the candidate trustscore. The consensus determination module 29510 may determine thecandidate trust score based on an average (e.g., a blended average) ofthe remaining local trust scores in the trust score list 29606. Forexample, the consensus determination module 29510 may determine thecandidate trust score by using a statistically weighted average of localtrust scores based on node count.

The nodes may communicate the candidate trust scores between oneanother. The nodes may also store the candidate trust scores 29608. Aset of consensus determination modules can determine a consensus trustscore for a cryptocurrency address based on a plurality of candidatetrust scores 29608. In some implementations, consensus determinationmodules may monitor the candidate trust scores to determine whether thecandidate trust scores are converging on a similar trust score. Theconsensus determination modules may be configured to determine aconsensus trust score in response to one or more consensus triggersassociated with the candidate trust scores. For example, the consensusdetermination modules may be configured to determine a consensus trustscore if greater than a threshold number/fraction of candidate trustscores are in agreement (e.g., within a threshold variance).

In some implementations, the consensus determination modules may performvalidation operations associated with the candidate trust scores (e.g.,using validation module 29510-4). For example, the consensusdetermination modules may perform error checking of the candidate trustscores. The error checking operations may include verifying whethercommunication of local trust scores actually occurred for the candidatescores or whether a conspiracy occurred that led to the candidatescores. In some implementations, the consensus determination modules canquery a plurality of nodes that participated in communication of localtrust scores and determination of candidate scores to determine what theplurality of nodes communicated to one another. In some implementations,the nodes may elect a leader node to perform the error checkingoperations and determine whether the nodes are in agreement.

After validating the candidate trust scores, the consensus determinationmodule 29510 may calculate the consensus trust score. In someimplementations, the consensus determination modules may determine theconsensus trust score based on an average (e.g., a blended average) ofthe candidate trust scores. For example, the consensus determinationmodules may determine the consensus trust score by using a statisticallyweighted average of candidate trust scores based on count. The consensusdetermination module 29510 may then update the consensus ledger 29600with the consensus trust score. The consensus determination module 29510may then distribute the updated ledger to other nodes (e.g., using theledger update module 29510-5). In some implementations, only a subset ofthe nodes can write trust scores and other data to the consensus ledger29600, although nodes that do not participate in generating theconsensus ledger 29600 may receive updated versions of the consensusledger 29600.

The consensus ledger 29600 includes consensus trust scores for differentcryptocurrency addresses over time. The consensus trust scores includedin the consensus ledger 29600 may be provided to trust score requestors.The consensus ledger 29600 can also include timing data that indicateswhen the consensus trust scores were written to the ledger 29600. For adetermined consensus trust score, the consensus ledger may includevalidation information associated with the consensus trust score, suchas the candidate trust scores used for the consensus trust score and thenodes that were validated. Storing the validation information for aconsensus trust score may allow the nodes to review how the consensustrust scores were validated.

The nodes 29300 may be configured to generate new trust scores for newcryptocurrency addresses and update the trust scores over time. Forexample, the trust score determination modules may be configured togenerate/update local trust scores for cryptocurrency addresses. Asanother example, the consensus determination modules may be configuredto generate/update candidate trust scores and consensus trust scoresover time. The frequency of updates can be set by the consensusprotocol. In some cases, data associated with cryptocurrency addressesmay change over time. In some cases, data included in the cryptocurrencyblockchain may change over time. The trust score determination modulesand consensus determination modules may be configured to generate newtrust scores in response to such changes in data.

In some implementations, the consensus determination modules maycommunicate new local trust scores and/or updated local trust scores toother nodes. For example, the consensus determination modules maycommunicate updates in local trust scores to other nodes if the updateresulted in greater than a threshold amount of change in the local trustscore. Updates to the local trust scores may cause changes in candidatetrust scores. In turn, changes to candidate trust scores may cause achange in consensus trust scores and the consensus ledger. In thismanner, the consensus ledger 29600 may reflect the history of consensustrust scores over time for a plurality of cryptocurrency addresses.

Different nodes may have different levels of functionality with respectto the calculation of trust scores. The differing functionality may bebased on the amount of value (e.g., UTOKEN) staked by the nodes, where agreater staked amount can authorize more functionality. In someimplementations, all nodes may be authorized to buy trust scores andinclude copies of consensus ledgers. In these implementations, a subsetof nodes may be configured to calculate local trust scores, candidatetrust scores, and consensus trust scores. Additionally, the subset ofnodes, or a further subset, may be configured to write consensus trustscores to the consensus ledger.

FIG. 301 illustrates an example method that describes calculation of aconsensus trust score from the perspective of an example node 29300-1.The method of FIG. 301 may be performed multiple times to determinelocal trust scores, candidate trust scores, and consensus trust scoresfor multiple cryptocurrency blockchain addresses.

In blocks 29610-29612, the trust score determination module 29510acquires and processes fraud and custody data 29324 and cryptocurrencyblockchain data. In block 29614, the trust score determination module29510 determines a local trust score for a cryptocurrency address. Inblock 29616, the consensus determination module 29510 receives localtrust scores from other nodes. In block 29618, the consensusdetermination module 29510 sends local trust scores to other nodes.

In block 29620, the consensus determination module 29510 determineswhether to calculate a candidate trust score (e.g., based on candidatedetermination criteria). If the candidate determination criteria are notsatisfied, the consensus determination module 29510 may continuecommunicating local trust scores with other nodes in blocks 29616-29618.If the consensus determination module 29510 determines that thecandidate determination criteria are satisfied, in block 29622, theconsensus determination module 29510 may determine a candidate trustscore based on the local trust scores in the trust score list 29606. Inblock 29624, the consensus determination module 29510 may determine aconsensus trust score based on a plurality of candidate trust scores. Inblock 29626, the consensus determination module 29510 may update theconsensus trust ledger 29600 to include the consensus trust score.

Referring to FIG. 302 , the trust network 29300 may implement areputation protocol in which a plurality of nodes each may compute oneor more reputation values. The reputation values for a node may indicatea variety of parameters associated with the node, such as an amount ofwork the node performed during trust score calculations anddistribution, the quality of the work performed (e.g., the accuracy),and the consistency of node operation (e.g., node uptime). Thereputation values may be used by other protocols in the trust network29300. For example, nodes may determine candidate and/or consensus trustscores based on the reputation values associated with one or more nodes.As another example, the nodes may be awarded and/or punished accordingto their reputation values.

The node 29300-1 includes a reputation determination module 29700 thatdetermines reputation values for the node. In some implementations, thenodes 29300 can transmit reputation messages 29704-1, 29704-2 to othernodes. The reputation messages 29704 can include reputation data, suchas reputation values associated with one or more nodes. In this manner,each node may receive reputation values for a plurality of other nodes.In a specific example, each node can be configured to communicatereputation data with a set of other nodes. In this specific example,each node can directly request reputation data from any node in the setof nodes. Additionally, each node may also request reputation data for aplurality of other nodes from any node in the set of nodes.

The node includes a reputation data store 29702 that stores reputationdata for a plurality of nodes (e.g., a subset of nodes on the trustnetwork). The reputation data may be stored in a reputation ledger 29706that includes a plurality of node IDs along with associated reputationvalues. The reputation data store 29702 may also store additionalinformation 29708, such as data used for generating reputation valuesand data associated with generating the consensus trust scores.

The reputation determination module 29700 can determine a plurality ofdifferent reputation values for each node. In some implementations, thereputation determination module 29700 may determine one or more workreputation values for the amount of work a node performs with respect tocalculating trust scores. For example, the reputation determinationmodule 29700 may determine one or more reputation values based on thenumber of local trust scores calculated, the number of candidate trustscores calculated, and the amount of work related to calculatingconsensus trust scores. One or more work reputation values may also bebased on an amount of communication (e.g., trust consensus messages)performed by the node.

The reputation determination module 29700 may also determine a pluralityof quality reputation values for nodes based on the quality of thecalculations performed by the nodes. For example, the quality reputationvalues may be based on a number of trust score outliers produced by thenodes and how fast trust scores were produced. The reputationdetermination module 29700 may also determine a plurality ofdistribution reputation values for nodes based on the distribution ofconsensus trust scores to requestors and the distribution of trustscores as fraud alerts.

The reputation determination module 29700 may also determine a pluralityof node performance reputation values based on a variety of nodeparameters, such as node bandwidth, node processing power, nodethroughput, and node availability. Example reputation values associatedwith node availability may be based on uptime values, mean time betweenfailure (MTBF) values, and/or mean time to repair (MTTR) values.

The reputation determination module 29700 may determine one or more datastorage reputation values based on the amount of data (e.g., historicdata) stored at a node and the amount of time for which the data isstored. The reputation determination module 29700 may also determine oneor more reputation values that indicate an amount of time the node hasbeen included (e.g., online) in the trust network 29300. The reputationdetermination module 29700 may determine one or more staking reputationvalues based on the amount staked by a node. Additionally, thereputation determination module 29700 may determine one or more outlierreputation values that indicate a number of outliers associated with anode and whether the outliers were considered fraudulent or supported byevidence.

In some implementations, the reputation determination module 29700 maycalculate one or more composite reputation values, each of which may bea function of any individual reputation values described herein. Forexample, a composite reputation value may be a weighted calculation ofone or more component reputation values.

The reputation data store 29702 may store information in addition to thereputation ledger. For example, the reputation data store 29702 maystore historic trust score data or other data used to determine thereputation values. In one example, the reputation data store 29702 maystore a history of each of the trust scores and contribution to thetrust scores from each node. In a more specific example, the historicaldata may include the number of nodes that participated in the consensuscalculation, the range of scores used in the calculation, along withother factors upon which the consensus scores were based.

In some implementations, the consensus determination module 29510 candetermine candidate trust scores and/or consensus trust scores based onone or more reputation values. For example, the consensus determinationmodule 29510 may determine whether a trust score is an outlier based onthe reputation associated with the node. In some implementations, theconsensus determination module 29510 may consider reputation valuesduring validation operations.

Referring to FIG. 303 , in some implementations, the trust network 29300may implement a token economy that operates as a medium of exchange inthe trust network 29300. For example, the trust network nodes mayinclude utility token modules 29800 that implement a utility tokenprotocol 29802. The utility token protocol 29802 may be powered by atoken (e.g., a native utility token). The utility token may be assigneda name (e.g., a coined name). For example, the utility token may bereferred to herein as “UTOKEN,” although other names may be used.

The trust network 29300 includes a utility token blockchain ledger 29806that may be stored in utility token data stores 29804 across the nodes.The utility token ledger 29806 may be a version of a publictransactional ledger integrated into the trust network 29300. Theutility token ledger 29806 may include a list of UTOKEN transactionsbetween different utility token blockchain addresses. For example, theutility token ledger 29806 may indicate the various transactionsassociated with the nodes 29300, such as the purchase of UTOKEN,purchase of trust scores, payment into the reward protocol, rewards paidby the reward protocol, and amount of funds staked by the nodes. Theutility token ledger 29806 may also include additional data, such astransaction metadata.

UTOKEN can be used in a variety of ways on the trust network 29300. Insome implementations, UTOKEN can be used as payment for access to trustscores and fraud alerts. In some implementations, UTOKEN can be used toreward nodes for performing work. In some implementations, a node maystake UTOKEN in order to enable additional functionality within thetrust network 29300. Although UTOKEN is described herein as a medium ofexchange in the trust network 29300, other payment types may be used asa medium of exchange in the trust network 29300. For example, othertypes of payments/tokens may be used for acquiring trust scores,acquiring fraud reports, paying rewards, and staking. In someimplementations, the utility token modules 29800 may implement smartcontracts for the trust network 29300. Communication between the nodesduring implementation of the utility token protocol is illustrated at29801.

Initially, the trust network 29300 may include a set number of UTOKEN.For example, there may initially be 1,000,000,000 UTOKEN. UTOKEN may beinitially granted and/or sold to nodes. In some implementations, thesupply of UTOKEN may expand in a deflationary manner, which may trackeconomic indicators including the total number of nodes, transactionvolume, staking amount, and fractionalization of the UTOKEN token.

Each node may include a wallet module 29814 that can be used to performtransactions on the utility token blockchain 29806. The wallet module29814 may implement a variety of functionalities. In someimplementations, the wallet module 29814 may be used to purchase trustscores. Payments for trust scores may be put into the reward protocol,as described herein. In some implementations, the wallet module 29814may be used to send/receive UTOKENS (e.g., with other nodes). In someimplementations, the wallet module 29814 can be used to stake UTOKEN. Insome implementations, the wallet module 29814 can lock UTOKEN, therebyindicating to the trust network 29300 that the locked UTOKEN are notavailable to be sent until unlocked. In some implementations, the walletmodule 29814 can be used to burn UTOKEN. Burning UTOKEN may prevent theburnt UTOKEN from being used for any function in the future.

The trust network 29300 may implement a reward protocol that receivespayments for a variety of activities, such as purchasing a trust scoreand purchasing fraud alerts. The trust network 29300 may pay out UTOKENto nodes (e.g., node wallets) based on a variety of factors. The nodesinclude reward modules 29808 and reward data stores 29810 that implementthe reward protocol. For example, the reward modules 29808 can receiveUTOKEN payments and pay out UTOKEN as a reward payment to nodes (e.g.,according to work performed). The reward data store 29810 may store areward ledger 29816 that indicates the nodes that received rewardpayments along with the corresponding factors (e.g., work performed)associated with the reward payments. For example, the reward ledger29816 may provide an accounting of the amount of UTOKEN received by thereward protocol, the calculation of the rewards, and the amount ofUTOKEN paid out to different nodes in response to the calculations.UTOKEN payments associated with the reward protocol may be storedaccording to one or more reward addresses on the utility token ledger29806.

The reward protocol can receive UTOKEN from a variety of sources. Forexample, the reward protocol can receive UTOKEN that was used topurchase trust score reports. As another example, the reward protocolcan receive UTOKEN used to purchase fraud alerts.

The reward protocol can make reward payouts to nodes based on a varietyof factors associated with the nodes. In some implementations, thereward protocol may pay rewards to nodes based on reputation valuesassociated with the nodes. The reward protocol can examine one or morereputation ledgers 29706 to determine the reputation values associatedwith the nodes. The reward payout calculations may be a multifactorcalculation that includes one or more reputation values. The rewardcalculations and payments may be performed periodically in some cases.

In some implementations, the reward protocol may pay rewards based onone or more work reputation values that indicate an amount of work anode performed with respect to calculating/communicating trust scores.The reward protocol may pay a larger portion of rewards for nodes thatperform more work with respect to calculating and communicating trustscores. In some implementations, the reward protocol may pay rewardsbased on one or more quality/distribution reputation values for nodesbased on the quality of the calculations performed by the nodes. Thereward protocol may pay a smaller portion of rewards for nodes thatproduce outlier trust scores.

In some implementations, the reward protocol may pay rewards based onone or more distribution reputation values for nodes based on thedistribution of consensus trust scores to requestors and thedistribution of trust scores as fraud alerts. The reward protocol maypay a larger portion of rewards for nodes that distribute a greateramount of trust scores. In some implementations, the reward protocol maypay rewards based on one or more performance reputation values. Forexample, the reward protocol may pay larger rewards to nodes withgreater bandwidth, processing power, throughput, and availability.

In some implementations, the reward protocol may pay rewards based onone or more data storage reputation values. For example, the rewardprotocol may pay larger rewards to nodes that store more data. In someimplementations, the reward protocol may pay rewards based on one ormore reputation values that indicate an amount of time the node has beenincluded (e.g., online) in the trust network 29300. For example, thereward protocol may pay larger rewards to nodes that have been onlinefor a longer period of time in the trust network 29300. In someimplementations, the reward protocol may pay rewards based on one ormore staking reputation values. For example, the reward protocol may paylarger rewards to nodes that have staked more UTOKEN.

In some implementations, algorithm suppliers may be rewarded in UTOKENfor providing nodes that contribute algorithms to the ecosystem. In someimplementations, proof of work security providers that may validate theUTOKEN ledger using a proof-of-work consensus algorithm may receiveUTOKEN as a block reward to incentivize their participation in theecosystem. Communication between the nodes during implementation of thereward protocol is illustrated at 29803.

FIG. 304 illustrates an example method that describes operation of thereward protocol. In block 29900, the reward protocol receives paymentsfor trust scores and fraud alerts. In block 29902, the reward protocolretrieves reputation values for a plurality of nodes. In block 29904,the reward protocol determines reward payouts to the plurality of nodesbased on the reputation values associated with the nodes. In block29906, the reward protocol pays the nodes according to the determinedpayouts. In block 29908, the reward protocol updates the reward ledgers29816 to reflect the payment calculations (e.g., based on reputationvalues) and the payment amounts. The reward protocol may periodicallyrepeat the method of FIG. 304 so that the nodes may be periodicallyrewarded for their relative contributions to the trust network 29300.

The trust network 29300 may implement a staking protocol (e.g., usingstaking modules 29812) in which each node can stake an amount of UTOKEN.The amount of UTOKEN staked may determine the level of functionalityafforded to the node. The staked UTOKEN can be reflected in the utilitytoken ledger 29806.

Staked UTOKENs may be under the temporary control of the trust network29300. For example, in some implementations, the reward protocol maypenalize a node by removing staked UTOKEN. In some implementations, thestaking functionality may be implemented as a smart contract in whichviolation of the contract results in the surrender (e.g., burning) ofsome staked UTOKEN. In these implementations, fulfillment of the smartcontract results in the UTOKEN being returned to the staking party. Insome implementations, the reward protocol may penalize a node forproducing outlier trust scores if the outlier scores are determined tobe fraudulent.

In some implementations, a node can be formed when a network participantstakes a quantity of UTOKEN (e.g., a required quantity). The amount ofUTOKEN staked by a node may determine the amount of functionality thatmay be implemented by the node. For example, staking more UTOKEN mayallow a node to implement a greater amount of network functions. Inthese cases, nodes may be assigned different levels of functionality. Alower level node may have functionality that is limited to requestingtrust scores. Higher level nodes may participate in calculating localtrust scores, candidate trust scores, and consensus trust scores. Theremay be any number of node levels that may perform any number of serviceson the trust network 29300. If the trust network penalizes a node andburns the node's stake, the node may descend in level and lose thecorresponding functionality. Communication between the nodes duringimplementation of the staking protocol is illustrated at 29805.

In some implementations, the cost a node is required to pay for a trustscore may decrease as the amount of UTOKEN staked by the node increases.In these cases, the more a node stakes, the fewer UTOKEN is required foracquiring a trust score (e.g., via real-time reporting). An examplerelationship of staked UTOKEN and consensus trust score cost isdescribed in the table of FIG. 308 . In one specific example, to beeligible for the discount, the staked amount of UTOKEN may be requiredto be staked for a period of time (e.g., at least 90 days).

FIGS. 305-306 illustrate example GUIs that may be generated on a usertransactor device 29302 by the transaction application 29316 or theintermediate transaction system 29304. The illustrated GUIs may be for asender in a cryptocurrency transaction. It can be assumed that thecryptocurrency network on which the blockchain transactions occur inFIGS. 305-306 use units of “coins” for transactions. The top portion ofthe GUIs includes fields that indicate the sender's information, such asthe sender's blockchain address and their balance (e.g., 100 coins). Thetop portion of the GUIs also include fields in which the sender canspecify a receiver address and indicate the transaction amount (e.g., 5coins) for the potential transaction. The GUIs include a “Send Coins”GUI element that can initiate the specified transaction between thesender and the receiver.

The lower portion of the GUIs in FIGS. 305-306 provide the sender withthe option of acquiring a trust report from the trust network 29300before engaging in the transaction. For example, in FIG. 305 , the usercan select (e.g., touch/click) the “Request Trust Report” GUI element tosend a trust request to the trust network 29300. The trust request mayinclude the receiver's address, as specified in the “To:” box above.FIG. 306 illustrates an example trust report received in response to thetrust request.

In FIG. 306 , the received trust report indicates that the receiver hada trust score of −0.90. It can be assumed in this case that a negativevalued trust score near −1.00 indicates that the receiver address islikely fraudulent. Similarly, a positive valued trust score near 1.00may indicate that the receiver address is not likely fraudulent. Inaddition to the numeric score of −0.90, the trust report also summarizesthe meaning of the trust score number. Specifically, the trust reportindicates that the “trust score indicates that the receiver has likelyengaged in fraudulent activity.” The GUI also provides a “CancelTransaction” GUI element that the sender may select (e.g., touch/click)to cancel the specified transaction.

In some implementations, the sender may be charged an amount forrequesting the consensus trust score. In implementations where thesender is transacting via an exchange, the exchange may spend UTOKENinto the reward protocol to retrieve the consensus trust score. Inimplementations where the sender does not interact via an intermediatetransaction system 29304, the sender may purchase UTOKEN from the trustnetwork 29300 for use in acquiring a consensus trust score.

FIG. 307 illustrates an example in which the trust network 29300 isqueried as part of a payment insurance process. In FIG. 307 , the usertransactor device 29302 transacts on the cryptocurrency blockchainnetwork 29318 via an intermediate transaction system. The intermediatetransaction system 30110 includes a trust request module 29326 that canretrieve trust reports from the trust network 29300. The intermediatetransaction system 30110 may also provide payment insurance to thetransactor.

The intermediate transaction system 30110 of FIG. 307 includes a paymentinsurance module 30112 that may determine whether a transaction will beinsured. The terms on which transactions are insurable may be agreed toby the owner/operator of the intermediate transaction system 30110 andthe owner/operator of the payment insurance system 30114 (e.g., anunderwriter system). In some implementations, payment insurance may beprovided for transactions in which the transacting blockchain addresseshave trust scores that indicate a low likelihood of fraud. The paymentinsurance system 30114 can acquire data related to the transactions(e.g., trust scores, timing, etc.) for auditing purposes.

In FIG. 307 , initially, the transactor device 29302 may initiate atransaction with the intermediate transaction system 30110. In responseto the initiated transaction, the intermediate transaction system 30110(e.g., the trust request module 29326) may retrieve a trust report forthe receiver and/or the sender. The intermediate transaction system30110 may then determine whether the transaction is insurable. Forexample, the payment insurance module 30112 may determine whether thetransacting blockchain addresses have trust scores that indicate a lowlikelihood of fraud. In some implementations, the payment insurancemodule 30112 may compare consensus trust scores to trust score thresholdvalues that indicate a maximum tolerable likelihood for fraud. In theseimplementations, the payment insurance module 30112 may indicate thatthe transaction is insurable if the consensus trust score(s) are lessthan the trust score threshold value. If the consensus trust score(s)are greater than the tolerable level for fraud, payment insurance may bedeclined.

In some implementations, the payment insurance module 30112 may querythe payment insurance system 30114 to determine whether the transactionis insurable. The query may indicate the consensus trust scores for thetransacting parties. In these implementations, the payment insurancesystem 30114 can make the determination of whether to insure thetransaction. The payment insurance system 30114 may then notify theintermediate transaction system 30110 of whether the transaction isinsurable.

In addition to the trust network 29300 playing a part in paymentinsurance processes, the trust network 29300 may also play a role inother financial processes. For example, the trust scores/reportsgenerated by the trust network 29300 may be used in order to freezetransactions and/or clawback funds.

The trust network 29300 may include any number of nodes. As describedherein, the nodes 29300 may have different levels of functionality(e.g., based on stake). The levels of nodes may be variable anddifferent levels of nodes may be eligible to participate in differentservices. FIG. 309 illustrates example services associated with threedifferent levels of nodes.

In some implementations, level 1 nodes may stake a minimum quantity ofUTOKEN, X, for a period of time (e.g., at least 90 days). Level 1 nodesmay perform all node activities in some implementations. In addition toperforming updates to trust scores and participating in real-timereporting, a level 1 node may participate in trust score processing andthe trust quorum, gathering and validating evidence of fraud andcustody, and sending transactors fraud alerts. Level 1 nodes may be themost important nodes in some implementations. In some cases, to maintainthe security of the trust network, there should be a minimum number oflevel 1 nodes (e.g., 29300 level 1 nodes). A decentralized autonomousorganization (DAO) may include a mandate to run additional level 1 nodesin the event that the minimum is not met.

In some implementations, level 2 nodes may stake the minimum quantity ofUTOKEN (e.g., proportional to X/2) for a period of time (e.g., at least90 days) to perform trust score updates and participate in the trustquorum. Level 2 nodes may additionally update the UTOKEN ledger, addevidence of custody to the blockchain, and deliver fraud alerts. Level 3nodes may stake the minimum quantity of UTOKEN (e.g., proportional toX/5) for a period of time (e.g., at least 90 days) to validate fraud andcustody evidence.

Trust report requests can be stored in a node's transaction mempool forprocessing and prioritization. Each node level may have a separatetransaction mempool. Level 3 nodes, for example, may not store reportrequests in some implementations. Level 1, however, may store reportrequests (e.g., state channel updates). In some implementations, nodesthat participate in fraud and custody verification can use separatemempools for those purposes.

Nodes may earn rewards for performing services. For example, nodes mayearn rewards proportional to their level. When a cryptocurrencytransactor accesses a trust score, the UTOKEN may be split between theawarded nodes. Additionally, when a block is secured in the utilitytoken blockchain, nodes may earn a percentage (e.g., 45%) of the miningreward.

A node level can have a respective reward queue. The amount of themining reward (e.g., 45% of the mining reward) each node receives can beproportional to the amount they staked (e.g., their node level). When anode joins the trust network 29300, it may be placed at the bottom ofthe queue. It may go up the queue so long as it actively providesservice on the trust network 29300 (e.g., proof of service). When itreaches the top of the queue (e.g., top 10%), it may be eligible forreward selection. In a specific example, the probability of randomselection may be 1/n, where n is the number of nodes in the top 10% ofthe queue.

In a specific implementation, the target par value for the cost of nodeservices may be 1 UTOKEN, while the par value of node earnings may be1.2 UTOKEN. In this specific implementation, the consensus trust scoreprices are set above cost to help ensure rewards for nodes.

A node contribution program can enable nodes to co-develop algorithmsfor the consensus trust score. This program may be a path tocontribution that allows nodes to better the accuracy of the algorithm(e.g., predictive algorithm) that assigns trust scores. The program mayallow the network to evolve to best fit new use cases and exploits onthe blockchain. Rewards to contributors may be controlled by the DAOthrough a bounty program.

The utility token protocol may be governed by a DAO. The DAO may allowthe protocol to keep pace with new developments. Participants may useUTOKEN to vote in the DAO. The DAO may be funded with an endowment ofthe initial token supply (e.g., 5%) and receive a percentage of miningrewards (e.g., 10% of every mining reward). If the number of level 1nodes falls below a set number (e.g., 29300), the DAO may run theminimum required nodes using DAO funds.

The DAO may determine protocol updates including adjusting supplycoefficients, setting bounties to update the protocol, improving oradjusting existing integrations with partners, and accepting work whenit is done. The DAO may assign and reward the bounty program. Ifmalicious actors attempt to exploit the trust network 29300, a bugbounty for algorithms may be opened to address that particular pattern,thereby strengthening the whole ecosystem. The DAO may additionallyapprove changes to open source bot software.

The DAO may control key system variables, including the number of UTOKENstaked per node level, staking period, and cryptocurrency transactordiscounts per staking amount.

Correctly proportioned node staking may enable a healthy token economy.The DAO may adapt the protocol to ensure there is enough UTOKEN incirculation to facilitate transactions and sufficient staking to promotea healthy and balanced token velocity. FIG. 311 illustrates sampleUTOKEN staking amounts and number of level 1 nodes. For example, thechart may describe example node numbers for when there is between 35percent and 50 percent of UTOKEN in circulation.

Nodes may separate the work of updating trust scores into cliques basedon the organic underlying graph topology of the blockchain. Cliques canthen be assigned to nodes to update and report on. The separation of thegraph into cliques may prevent individual nodes from having full accessto all of the trust data. This may protect the integrity of the tokeneconomy while still maintaining a full graph. There may be an overlap ofaddresses within cliques. As the number of nodes increases, cliqueoverlap may increase.

The table of FIG. 310 illustrates an example relationship of the numberof nodes, the number of cliques, the address overlap, and theprobability that a node will get a single address in their control.Here, overlap may scale with the number of nodes. The security of thetrust network 29300 may scale with the number of nodes on the trustnetwork 29300. The maximum probability of a node getting a specificaddress is 5%, with a minimum of 5 overlapping addresses per clique.

The probability of a node getting a single address may be determined by:P(Address A)=(number of cliques with Address A)/(total number ofcliques).

However, the probability of getting control of a specific address may bedifferent than the probability of getting control of any address. If aparticipant only has one node, that participant cannot get control overa single address, since other nodes contain that address in theircliques as well.

The high cost to become a node may be one way the network prevents Sybilattacks. Because clique placement may be pseudo-random, in some cases, amalicious actor must control 51% of the nodes on average to have 51%control over a single address.

A hypergeometric distribution described below may be used to calculatethe probability of a node randomly getting 51% control of a singleaddress or any address.

-   -   N=Number of nodes    -   B=Bad nodes (who want to control address)    -   O=Overlap    -   C=Number of nodes to control for 51%=(O/2)+1

$\begin{matrix}{{P\left( {{control}{over}a{specific}{address}} \right)} = \prod_{k = C}^{O}} & \frac{\left( {\left( {B{choose}k} \right)\left( {\left( {N - B} \right){{choose}{}\left( {(O) - k} \right)}} \right)} \right.}{\left( {N{}{choose}O} \right)}\end{matrix}$

The more nodes there are and the higher the cost of UTOKEN, the moredifficult it is to mount such an attack. For the base case of 29300nodes with 5 overlap, the probability of getting 51% control of anaddress may be: P(51% control)=P(3 nodes gain control over address)+P(4nodes gain control over address)+P(5 nodes gain control overaddress)=Π_(k=3) ⁵ (5 choose k) (95 choose 5−K)/(100 choose 5)=6.2e-05.

When the number of cliques increases to 1,000, and overlap increases to50, the probability becomes 1.7e-10. As the number of nodes and cliquesincrease, the probability of a 51% attack to control the trust score ofany single address approaches 0.

FIG. 312 is an example detailed functional block diagram of the trustscore determination module 29500 (hereinafter “trust module 29500”) andthe local trust data store 29502. FIG. 313 is a method that describesoperation of the trust module 29500. Referring to FIG. 312 , the trustmodule 29500 acquires and processes a variety of data described herein.The processed data can be included in the local trust data store 29502.The data associated with a single cryptocurrency blockchain address isillustrated herein as a blockchain address record 29504. A record datastore 30010 may include a plurality of such blockchain address records29504, each for a different blockchain address. Each blockchain addressrecord 29504 can include a blockchain address 29506 that uniquelyidentifies the record. The blockchain address record 29504 describedherein represents data stored in the local trust data store 29502. Thelocal trust data store 29502 may include a variety of different datastructures that are used to implement the data. Accordingly, theblockchain address record 29504 may be implemented using one or moredifferent data structures than explicitly illustrated herein.

FIG. 313 is a method that describes operation of the trust module 29500illustrated in FIG. 312 . In block 30030, the data acquisition andprocessing module 30000 acquires and processes a variety of types ofdata 29324, such as custody data and fraud data (e.g., see FIG. 314 ).The data acquisition and processing module 30000 may store custody andfraud data 30018 related to a blockchain address in the blockchainaddress record 29504. The data acquisition and processing module 30000may also generate a fraud label 30020 that indicates whether theblockchain address is likely fraudulent based on the acquired frauddata.

In block 30032, the blockchain acquisition and processing module 30002acquires and processes blockchain data (e.g., the blockchain ledger29322) (e.g., see FIG. 315 ). The blockchain acquisition and processingmodule 30002 may store raw and processed blockchain data 30022 relevantto a blockchain address in the blockchain address record 29504. In block30034, the graph generation and processing module 30004 generates ablockchain graph data structure based on the blockchain data (e.g., seeFIGS. 316-317 ). The blockchain graph data structure may be stored inthe graph data store 30012. The graph generation and processing module30004 may also process the graph to determine one or more graph-basedvalues 30024 (e.g., importance values) that may be used to generatelocal trust scores.

In block 30036, the feature generation module 30006 generates scoringfeatures 30026 for blockchain addresses (e.g., see FIG. 318 ). In block30038, the scoring model generation module 30008 generates one or morescoring models based on the scoring features and other data (e.g.,labeled fraud data). The one or more scoring models may be stored in thescoring model data store 30014. In block 30040, the score generationmodule 30016 generates one or more local trust scores 29508 forblockchain addresses using one or more scoring models and the scoringfeatures associated with the blockchain addresses (e.g., see FIG. 319 ).Data related to the requests and responses for consensus trust scoresmay be stored as request data 30028 of the blockchain address record29504.

Detailed examples of the trust module 29500 and the local trust datastore 29502 are now described with respect to FIGS. 314-316 and FIGS.318-319 . Various modules and data stores have been omitted from thefigures for illustration purposes only. For example, the various modulesand data stores have been omitted to focus on the functionalityassociated with the modules and data stores that are illustrated.

FIG. 314 illustrates data acquisition and processing of fraud andcustody data sources. FIG. 315 illustrates acquisition and processing ofblockchain data. FIGS. 316-317 illustrate generation and processing of ablockchain graph data structure. FIG. 318 illustrates scoring featuregeneration and scoring model generation. FIG. 319 illustrates thegeneration of local trust scores for a blockchain address using ascoring model and scoring features for the blockchain address.

Referring to FIG. 314 , the data acquisition and processing module 30000includes a data acquisition module 30000-1 that acquires data from thefraud and custody data sources 29324. The data acquisition andprocessing module 30000 also includes a data processing module 30000-2that processes the acquired data. The raw and processed data 30018 canbe stored in the record data store 30010. The data acquisition module30000-1 can acquire data in a variety of ways. In some implementations,the data acquisition module 30000-1 can acquire curated data, such ascurated/purchased data provided by partners/customers. In some cases,data can be user peer-reviewed structured data.

In some implementations, the data acquisition module 30000-1 may beconfigured to automatically acquire data (e.g., crawl/scrape websites).For example, the data acquisition module 30000-1 may be configured to dotargeted data acquisition, such as acquiring data for specific socialmedia accounts. As another example, the data acquisition module 30000-1may perform more general data acquisition, such as more generalcrawling/scraping of sites.

The data acquisition module 30000-1 can acquire custody data fromcustody data sources 29324-1. Custody data may indicate the party thatowns/controls the blockchain address (e.g., the keys). Example partiesthat may take custody of blockchain addresses may include, but are notlimited to, exchanges, wallets, and banks. In some implementations, thecustody sources 29324-1 can provide the custody data.

In some implementations, the trust module 29500 may implement custodianspecific trust score generation. For example, the trust module 29500 mayselect a specific scoring model based on the custodian associated withthe blockchain address. In some implementations, the trust module 29500may implement customer/custodian specific reporting for blockchainaddresses (e.g., based on the custodian associated with the blockchainaddress). For example, the trust report may be formatted in a specificmanner for a specific custodian.

The data acquisition module 30000-1 acquires data that may provideevidence of fraud from a variety of fraud data sources 29324-2. Thetrust module 29500 may make a determination of the likelihood of fraudfor blockchain addresses based on fraud data. For example, the trustmodule 29500 may label blockchain addresses as fraud based on the frauddata. Subsequently, the trust module 29500 may generate scoring featuresand scoring models based on the labeled blockchain addresses.

In some implementations, the trust module 29500 may be configured toacquire databases and lists that indicate fraudulent activity associatedwith a blockchain address. In one example, fraud data sources 29324-2can include databases of fraud information, such as third-partydatabases of fraud information and/or customer provided databases offraud information. The databases may be provided by public entities(e.g., government watchlists) and/or private entities (e.g., a companygenerated watchlist).

In some examples, a database of fraud information may be provided in theform of a blacklist that includes a list of blockchain addresses thathave been identified as having engaged in fraud. In this example, thedata acquisition module 30000 may acquire public blacklists, purchaseblacklists, and/or receive blacklists from customers. In some cases,blacklists may have been peer reviewed by a community of trusted parties(e.g., experts). In some implementations, the data processing module30000-2 can mark addresses as fraudulent if the address is included on ablacklist. In other implementations, the presence of the blockchainaddress on a blacklist can be used as a scoring feature for determiningwhether the blacklisted blockchain address is likely fraudulent.

In some implementations, the data acquisition module 30000-1 may beconfigured to acquire fraud data from targeted locations, such aslocations on the internet specified by web uniform resource locators(URLs) and/or usernames (e.g., a specific social media account). In someimplementations, locations may be provided (e.g., web URLs) that thedata acquisition module 30000-1 may monitor for fraudulent activity. Forexample, a customer may provide a web address to a social media pageassociated with a specific blockchain address. In this example, the dataprocessing module 30000-2 may identify fraudulent behavior if ablockchain address other than the specified blockchain address appearsin the web contents at the web address. In another example, if there isa known contribution address of an initial coin offering (ICO), accountsand blockchain addresses fraudulently attempting to acquire funds (e.g.,phish) may be detected. The trust network 29300 may notify a user of thefraudulent address and use the evidence of fraudulent activity asdescribed herein.

Although the data acquisition module 30000-1 may be configured toacquire fraud data from targeted locations, in some implementations, thedata acquisition module 30000-1 can generally crawl and scrape otherdata sources (e.g., social media sites) for fraud data and other data.In these examples, the data processing module 30000-2 may identifyfraudulent blockchain addresses based on behavior across a social mediaplatform, such as scams that request funds from multiple social mediausers, new accounts that directly ask other users for funds, and fakeinitial coin offering scams.

In some implementations, the trust module 29500 (e.g., the dataprocessing module 30000-2) may label a blockchain address as fraudulent(e.g., at 30020). For example, the data processing module 30000-2 maylabel a blockchain address as fraud based on fraud data. In a specificexample, the data processing module 30000-2 may label a blockchainaddress as fraud if the blockchain address is included in one or moreblacklists. If a blockchain address is not labeled as fraud, the fraudstatus of the blockchain address may be unknown. Put another way, anunlabeled blockchain address does not necessarily indicate that theblockchain address is not fraudulent. In some cases, a blockchainaddress may be labeled as a known good address that is not fraudulent.For example, an exchange wallet or verified smart contract may beexamples of known good addresses.

For blockchain addresses that are assigned one or more trust scores andlabeled as fraud, the fraud label for the blockchain address may bedispositive on the issue of fraud for the blockchain address. As such,in these implementations, the trust module 29500 may disregard the trustscore for the blockchain address and/or set the trust score for theblockchain address to a 100% certainty of fraud. In otherimplementations, the trust module 29500 may continue to calculate trustscores for the blockchain addresses labeled as fraud.

The fraud label 30020 can also include fraud label metadata. The fraudlabel metadata may indicate the source of the information used to labelthe blockchain address as fraud (e.g., a specific blacklist). The fraudlabel metadata may also indicate a type of fraud (e.g., a phishingscam). The fraud label metadata can also include the content of thefraudulent behavior, such as text associated with a scam (e.g., textposted online or in an email). The trust module 29500 can return thefraud label metadata to a requesting device to clearly explain thereason the trust module 29500 has labeled a blockchain address asfraudulent.

Referring to FIG. 315 , the blockchain data acquisition module 30002-1(hereinafter “blockchain acquisition module 30002-1”) can acquireblockchain data from the blockchain network 29318. For example, theblockchain acquisition module 30002-1 can acquire the blockchaintransaction ledger 29322. The blockchain acquisition module 30002-1 canstore the raw blockchain data 30022 in the record data store 30010. Theblockchain processing module 30002-2 can process the blockchaintransaction ledger 29322 and store the processed blockchain values 30022(e.g., transaction amounts, dormancy, etc.) in the record data store30010 (e.g., in a blockchain address record 29504).

The blockchain transaction ledger 29322 includes data for a plurality ofblockchain transactions. Each transaction may include: 1) a senderaddress, 2) a receiver address, and 3) a value amount (e.g., a coinamount). A transaction may also include transaction identification datathat uniquely identifies the transaction on the blockchain. Thetransaction identification data may be referred to herein as atransaction identifier (ID). In some implementations, a transaction hashcan be used as a unique identifier for a transaction. A transaction hashmay be a string of pseudorandom characters that uniquely identify atransaction. Some blockchains may also include additional data that maybe stored and processed. Example additional data may include internaltransaction data, such as a program that is executed in an Ethereumsmart contract.

The blockchain transaction ledger can include a plurality of blocks.Each of the blocks can include a collection of transactions. A block mayinclude a collection of transactions that occurred on the blockchainwithin a certain period of time. A block may include a block number(e.g., a sequentially assigned number) that may act as an identifier forthe block. In the case of Bitcoin, a transaction may include the sendingparty's address, the receiving party's address, the amount sent, andvarious parameters describing speed. Ethereum may include similartransaction data, as well as raw data around what function on a smartcontract was executed, if a function was executed.

Different blockchain networks may include different types of blockchainledgers. For example, different blockchain ledgers may includeblockchain transaction data in different formats. As another example,different blockchain ledgers may include additional or alternative dataassociated with transactions. The blockchain acquisition module 30002-1can be configured to acquire blockchain transaction data for differentblockchains. For example, the blockchain acquisition module 30002-1 caninclude different modules, each of which may be configured for acquiringblockchain transaction data for a different blockchain network.

In some cases, a blockchain network can include timing data thatindicates the time of blockchain transactions (e.g., relative/absolutetime). In these implementations, the blockchain acquisition module30002-1 can use the provided timing data to indicate when thetransaction occurred. In other cases, a blockchain network may notinclude timing data. In these implementations, the blockchainacquisition module 30002-1 can generate a time stamp for thetransaction. In some cases, timing data can be generated from the blockassigned to the transaction. Blocks may be assigned as part of themining process whereby actors on the blockchain compete to verify thevalidity of a set of transactions. Once a block is mined, and thetransactions are verified, then the timing data can be assumed fromother miners' consensus.

The blockchain processing module 30002-2 can determine a variety ofvalues based on the acquired blockchain data. The trust module 29500(e.g., the score generation module 30016) can use the determined valuesas scoring features for determining trust scores. The trust module 29500(e.g., the model generation module 30008) can also generate scoringmodels based on the determined values. The blockchain values for ablockchain address can be stored in the blockchain address record (e.g.,at 30022).

The blockchain processing module 30002-2 can include functionality fordetermining the different blockchain values described herein. Forexample, the blockchain processing module 30002-2 of FIG. 315 includes adormancy determination module 30050 that can determine dormancy valuesfor a blockchain address. The blockchain processing module 30002-2 alsoincludes a behavior identification module 30052 that can determinewhether the blockchain address matches one or more behavioral templates(e.g., patterns or fingerprints). The modules 30050, 30052 included inthe blockchain processing module 30002-2 of FIG. 315 are only examplemodules. As such, the blockchain processing module 30002-2 may includeadditional/alternative modules than those modules illustrated in FIG.315 . Additionally, the blockchain values included in the blockchaindata 30022 of FIG. 315 are only example values. As such, the blockchaindata for a blockchain address may include additional/alternative values.

In some implementations, the blockchain processing module 30002-2 maydetermine values associated with the amount of funds transacted by ablockchain address. For example, the blockchain processing module30002-2 may determine: 1) a total amount of funds received by theblockchain address, 2) a total amount of funds sent by the blockchainaddress, 3) the total amount of funds transacted in and out of theblockchain address, and 4) the average transaction amount for theblockchain address.

In some implementations, the blockchain processing module 30002-2 maydetermine values associated with the timing of transactions associatedwith a blockchain address. For example, the blockchain processing module30002-2 can determine an activity level of the blockchain address, suchas how often the address is involved in a transaction (e.g., averagetimes between transactions and variance). As another example, theblockchain processing module 30002-2 can determine the age oftransactions associated with the address. Another example scoringfeature related to timing may include the time between entrance of fundsand exit of funds from a blockchain address (e.g., timing for a singletransaction or average over multiple transactions). In some cases,fraudulent activity may not immediately exit an address.

As another example, the dormancy determination module 30050 candetermine the probability of dormancy for the blockchain address. Anexample dormancy probability may indicate an amount of time for whichthe blockchain address is not associated with transactions. For example,a dormancy probability may indicate an amount of time for which theblockchain address is not associated with transactions relative to theaddress' expected time between transactions. Put another way, theexample dormancy time may indicate the probability that the blockchainaddress is dormant. With respect to dormancy likelihood, fraudulentaddresses may not stay active for long in some cases.

In some implementations, the blockchain processing module 30002-2 maydetermine values associated with the timing of transactions and theamount of the transactions. For example, the blockchain processingmodule 30002-2 may determine: 1) a total amount of funds received over aperiod of time, 2) a total amount of funds transferred over a period oftime, and 3) a total amount of funds transacted over a period of time.

In some implementations, the blockchain processing module 30002-2 maydetermine values associated with how the blockchain address interactswith other blockchain addresses. For example, the blockchain processingmodule 30002-2 may determine the list of addresses that have interactedwith the blockchain address and/or the total number of addresses thathave interacted with the blockchain address (e.g., as senders and/orreceivers). This value may be iteratively computed to determine howimportant an address is to its local neighborhood and the blockchain asa whole.

The blockchain processing module 30002-2 includes a behavioridentification module 30052 that can determine whether a blockchainaddress matches specific behavior templates that may be indicative offraud. If the behavior identification module 30052 identifies a matchbetween a blockchain address' behavior and a behavior template, thematch may be stored in the blockchain address record 29504. In someimplementations, the local trust data store 29502 may store a set ofbehavior templates. In these implementations, the behavioridentification module 30052 can determine whether the blockchainaddress' behavior matches one or more of the set of behavior templates.

A behavior template may include a set of conditions that, if satisfied,cause the behavior template to be matched to the blockchain address. Abehavior template may include conditions that are based on any of theblockchain values described herein. For example, a behavior template mayinclude conditions that are based on at least one of 1) amounts of fundstransferred, 2) the number of transactions, 3) the timing oftransactions (e.g., rate of transactions), 4) how the blockchain addressinteracts with other addresses (e.g., number of differentsenders/receivers and patterns of transactions), and 5) the likelihoodof dormancy of the address.

In one specific example, a behavior template may define a thresholdnumber of transactions (e.g., 5 transactions in and out) at a thresholdrate. In this example, the behavior template may be matched if ablockchain address engages in a low number of transactions (e.g., lessthan or equal to the threshold number) in quick succession (e.g., ashort rapid burst). Another example condition for the behavior templatemay be a high dormancy probability, as any transactions may be limitedto bursts. In another specific example, a behavior template may define ahigh threshold number of transactions (e.g., irregularly high for theblockchain). In this example, a behavior template may be matched if theblockchain address engages in greater than the threshold number oftransactions. In this example, the behavior template may also require ahigh importance value, such that the blockchain address is required tohave a minimum importance value to match the template. Furthermore, thebehavior template may require a low likelihood of dormancy, as thefraudulent behavior may follow a pattern of regular transactions.

If the blockchain address matches a behavior template, the match may bestored as a blockchain value in the blockchain address record 29504. Forexample, the blockchain address record may store a binary value (e.g.,0/1) for each behavior template that indicates whether the behaviortemplate was matched. In implementations where the behavioridentification module 30052 determines a value (e.g., a decimal orinteger value) that indicates how well the blockchain address matchesthe behavior template, the value may be stored in the blockchain addressrecord 29504.

Referring to FIGS. 316-317 , the graph generation module 30004-1generates a blockchain graph data structure based on the blockchaintransactions for a plurality of different blockchain addresses. Thegraph data structure includes blockchain addresses and transactionsbetween the blockchain addresses. For example, for each blockchainaddress, the graph data structure may describe each transactionassociated with the blockchain address along with the direction of thetransaction, such as whether the blockchain address was a sender orreceiver. The graph data structure can also include the transactionamount for each transaction. In some implementations, the graph datastructure can include fraud data (e.g., fraud labels). The fraud labelcan indicate that the address has been involved in fraudulent activity(e.g., a known fraudulent address).

FIG. 317 illustrates an example representation of the graph datastructure. In FIG. 317 , the graph data structure is represented bynodes and edges. The graph data structure includes blockchain addressesas nodes of the graph. The transactions between blockchain addresses areedges between the nodes, where the arrows indicate the direction of thetransaction (e.g., the receiver is at the arrowhead). The amount foreach transaction is labeled adjacent to the arrow. The fraud label foreach blockchain address is included above the node. FIG. 317 includes 4transactors with blockchain addresses A, X, Y, Z. Blockchain address Yhas been labeled as a fraudulent address. The other blockchain addresseshave an unknown fraud status. The graph illustrates 3 blockchaintransactions. A first transaction is from blockchain address X toblockchain address A for a first amount (i.e., amount 1). A secondtransaction is from blockchain address Y to blockchain address A for asecond amount (i.e., amount 2). A third transaction is from blockchainaddress A to blockchain address Z for a third amount (i.e., amount 3).

The graph data structure is stored in the graph data store 30012. Thegraph generation module 30004-1 can update the graph data structure overtime so that the graph data structure includes an up to daterepresentation of the transactions included on the blockchain network29318.

The graph processing module 30004-2 can generate graph-based values30024 using the graph data structure. The graph-based values 30024 maybe stored in the blockchain address record 29504. The graph processingmodule 30004-2 can update the graph-based values 30024 over time.

In some implementations, the graph processing module 30004-2 candetermine one or more importance values for each of the blockchainaddresses. The importance values may indicate the importance of ablockchain address relative to other blockchain addresses (e.g.,relative to all blockchain addresses). In some implementations, thegraph processing module 30004-2 may determine the importance values fora blockchain address based on adjacent blockchain addresses. In someimplementations, the graph processing module 30004-2 may weight thecontribution of adjacent blockchain addresses by the importance of theblockchain addresses.

In some implementations, the graph processing module 30004-2 maydetermine an importance value by counting the number of transactionscoming into a blockchain address. In this specific example, moretransactions may indicate that the blockchain address is more importantthan other blockchain addresses with fewer incoming transactions. Insome implementations, the graph processing module 30004-2 may determinean importance value by determining the number of different blockchainaddresses with which the blockchain interacts. In some implementations,the graph processing module 30004-2 may determine an importance valuethat indicates the total amount of funds into the blockchain addressrelative to the amount of funds out of the address (e.g., amount outdivided by amount in). In another example, the graph processing module30004-2 may determine an importance value that indicates the number oftransactions into the blockchain address relative to the number oftransactions out of the blockchain address (e.g., total number oftransactions in divided by total number of transactions out). In anotherexample, the graph processing module 30004-2 may determine an importancevalue based on the number of transactions in to the blockchain address,the number of transactions out of the blockchain address, the amount offunds in, and the amount of funds out. In some implementations, thegraph processing module 30004-2 may implement other processingtechniques, such as PageRank (PR) and/or personalized hitting time(PHT).

In some implementations, the graph processing module 30004-2 maydetermine a fraud distance scoring feature that indicates the blockchainaddress' distance from fraud in the graph. For example, fraud distancescoring features may include a minimum distance from fraud, an averagedistance from fraud, and/or the number of fraudulent blockchainaddresses with which the blockchain address has interacted.

Referring to FIG. 318 , the feature generation module 30006 can generatescoring features for each of the blockchain addresses. The trust module29500 (e.g., score generation module 30016) can generate one or morelocal trust scores for a blockchain address based on the scoringfeatures associated with the blockchain address. The scoring featurescan be numerical values (e.g., integer or decimal values), Booleanvalues (e.g., 0/1), enumerated values, or other values.

The feature generation module 30006 can generate the scoring featuresbased on any of the blockchain values described herein. For example,scoring features for a blockchain address may be based on 1) amountsassociated with transactions, 2) timing data associated withtransactions (e.g., dormancy), 3) graph-based values (e.g., one or moreimportance values) associated with the blockchain address, and/or 4)behavior-based data associated with the blockchain address.

With respect to behavior-based data, the feature generation module 30006may generate a Boolean scoring feature that indicates whether theblockchain address matches any of the behavior templates. In anotherexample, the feature generation module 30006 may generate a Booleanscoring feature for each of the behavior templates, such that thescoring features identify which of the behavior templates are matched.In another example, the feature generation module 30006 may generate ascoring feature that indicates how many of the behavior templates werematched (e.g., a percentage of the total available). In another example,instead of generating Boolean features, the feature generation module30006 may generate numeric values that indicate how well the blockchainaddress matched the behavior templates, such as a decimal value (e.g.,0.00-1.00) that indicates how well the behavior template was matched.

The trust module 29500 includes a scoring model generation module 30008(referred to herein as a “model generation module 30008”) that cangenerate scoring models 1600 that are used to generate local trustscores for a blockchain address. For example (e.g., see FIG. 319 ), ascoring model can receive scoring features for a blockchain address andoutput a local trust score for the blockchain address. The modelgeneration module 30008 can generate a scoring model based on trainingdata. The training data can include scoring features along withassociated fraud labels. The set of scoring features a model uses asinput may be referred to herein as a “feature vector.” In someimplementations, the trust module 29500 can use a deep neural net toscore, where the classification is determined by known good/badaddresses. The neural net may be trained over the feature vectors. Insome implementations, the trust module 29500 can leverage models basedon random forests, decision trees, and logistic regression, and combinethem in the form of a “consensus of experts.”

The model generation module 30008 can generate a scoring model (e.g., amachine learned model) based on training data that includes sets offeature vectors and their corresponding fraud label (e.g., Fraud: 0/1).In this example, the generated scoring model may output a local trustscore (e.g., a decimal value) that indicates the likelihood theblockchain address is fraudulent. In some implementations, the trainingdata may also include a label that positively indicates that theblockchain address is a known good address (e.g., not fraudulent).

Although the trust module 29500 can generate scoring models that areused to generate local trust scores, the trust module 29500 may generatelocal trust scores in other manners. For example, the trust module 29500may generate local trust scores using scoring functions (e.g., weightedscoring functions) and/or heuristic models that generate local trustscores according to rules.

FIG. 319 illustrates an example score generation module 30016 thatgenerates local trust scores for blockchain addresses. The scoregeneration module 30016 may generate a local trust score for ablockchain address by using a feature vector for the blockchain addressand a scoring model. For example, the score generation module 30016 mayinput a feature vector for a blockchain address into a scoring modelthat outputs a local trust score.

The local trust score 29508 for a blockchain address can be stored inthe blockchain address record 29504. The score generation module 30016can generate local trust scores for each of the blockchain addresses.The score generation module 30016 can also update the local trust scoresover time, such as when additional data is acquired. A blockchainaddress record 29504 can include the most recently calculated localtrust score, as well as historically calculated local trust scores. Insome implementations, the trust module 29500 can leverage the change inlocal trust score (and historical score) to provide a real-time alertingsystem, such that a party can be notified if the trust score of anaddress drops (e.g., as in the case of an organization receivingfraudulent funds through an address they control). The trust module29500 may provide an API that can hook into their service that canfreeze the transaction and alert the relevant people at thatorganization (e.g., by phone, email, etc.).

The score generation module 30016 may be configured to provide the localtrust score in a variety of formats. In some implementations, the localtrust score may be an integer value with a minimum and maximum value.For example, the local trust score may range from 1-7, where a trustscore of ‘1’ indicates that the blockchain address is likely fraudulent.In this example, a trust score of ‘7’ may indicate that the blockchainaddress is not likely fraudulent (i.e., very trustworthy). In someimplementations, the local trust score may be a decimal value. Forexample, the local trust score may be a decimal value that indicates alikelihood of fraud (e.g., a percentage value from 0-100%). In someimplementations, a local trust score may range from a maximum negativevalue to a maximum positive value (e.g., −1.00 to 1.00), where a largernegative value indicates that the address is more likely fraudulent. Inthis example, a larger positive value may indicate that the address ismore likely trustworthy. The customer may select the trust score formatthey prefer.

In some implementations, the blockchain address record 29504 can storerequest data 30028 for each trust request. The request data 30028 mayinclude any data associated with a received trust request and/or theprovided trust response. The request data 30028 may be stored in theassociated blockchain address record 29504. In some implementations, ablockchain address record may store request data 30028 each time a trustrequest is made for the blockchain address. In these implementations,the request data 30028 may indicate a number of times a trust requestwas made for the blockchain address. The request data 30028 may alsoindicate the blockchain address that made the trust request, theconsensus trust score that was reported to the requestor, and the timeof the request. Accordingly, the request data 30028 may show trends overtime regarding the parties that are requesting trust scores for ablockchain address. In some implementations, the scoring features for ablockchain address may include scoring features that are based on therequest data 30028. One example scoring feature may be a total number oftimes a trust request was made for the blockchain address. Anotherexample scoring feature may be a number of different blockchainaddresses that made trust requests for the blockchain address. Otherexample features may include the frequency at which trust requests weremade for the blockchain address (e.g., a number of requests over a timeperiod).

Although the trust module 29500 may calculate a single local trust scorefor each of the blockchain addresses, regardless of whether theblockchain address is a sender or receiver, in some implementations, thetrust module 29500 may calculate a receiver trust score and a sendertrust score for each address. In one example, a blockchain address whichregularly falls prey to scams can have the sender trust score set asless trustworthy than a blockchain address which does not often fallprey to scams. In another example, a blockchain address which regularlyfalls prey to phishing scams may not have a modified receiver trustscore when there is no indication of nefarious activity associated withreceiving funds at the blockchain address.

FIGS. 320-328 are directed to an environment in which a cryptocurrencyblockchain network 30070 can execute smart contracts provided by a trustnetwork 30072-1 (e.g., including trust nodes 30072-1 a, 30072-1 b, . . ., 30072-1N) or a centralized trust system 30072-2 (e.g., see FIG. 320and FIG. 323 ). For example, the blockchain network 30070 may provide avirtual machine 30074 (VM) (e.g., a decentralized virtual machine) thatexecutes the smart contracts. The smart contracts described herein caninclude a variety of functionality. For example, a smart contract mayrequest a trust score for a potential receiver address from the trustnetwork/system 30072 and then complete or cancel a blockchaintransaction based on the trustworthiness of the receiver address. Usingsmart contracts according to FIGS. 320-328 provides for the acquisitionof trust scores using the native cryptocurrency of the blockchainnetwork 30070 (e.g., cryptocurrency blockchain tokens) instead of usingUTOKEN.

FIG. 320 illustrates an environment that includes a cryptocurrencyblockchain network 30070 that executes smart contracts. Thecryptocurrency blockchain network 30070 may receive the smart contractsfrom a variety of sources, such as a decentralized trust network 30072-1or a centralized trust system 30072-2 (hereinafter “trust system30072-2”). The blockchain network 30070 includes cryptocurrencyblockchain protocols 30076 and a cryptocurrency blockchain ledger 30078,as described with respect to FIG. 293 . The cryptocurrency blockchainprotocols 30076 of FIG. 320 may create virtual machines 30074 (e.g.,process virtual machines) that execute the smart contracts 30080. Forexample, the virtual machines 30074 may execute the smart contracts30080 on one or more nodes of the blockchain network 30070. An examplevirtual machine on a blockchain network is the Ethereum Virtual Machinethat operates on the Ethereum blockchain.

The distributed trust network 30072-1 of FIG. 320 may provide similarfunctionality as the trust network 29300 of FIG. 293 described herein.The trust network 30072-1 may also provide functionality associated withthe smart contracts. For example, the trust network 30072-1 may providesmart contracts for execution on the blockchain network 30070. In someimplementations, the trust network 30072-1 may include smart contracttemplates 30110 (e.g., see FIG. 323 ) that may be provided to parties(e.g., exchanges/wallets) for execution on the blockchain network 30070.In some implementations, the trust network 30072-1 may also provide aninterface (e.g., an API) for completing smart contract templates withdata, such as a sender address, a receiver address, and a specifiedtrust level for transactions. The trust network 30072-1 can also beconfigured to provide trust scores to smart contracts that areinstantiated on the blockchain network 30070. A smart contract maycomplete or cancel a transaction based on the received trust score.

In some implementations, a party (e.g., a company) may operate a trustsystem 30072-2 that provides functionality associated with the smartcontracts described herein. In some implementations, the same party(e.g., company) can operate one or more nodes on the trust network30072-1 and also operate a trust system 30072-2. For example, the partymay provide different trust nodes/systems for different businesspartners/customers. In other implementations, different parties (e.g.,companies) may operate trust network nodes and one or more trustsystems. Furthermore, different trust systems/networks may beimplemented for different cryptocurrency blockchain networks.

The trust system 30072-2 can provide similar functionality as the trustnetwork 30072-1. The trust system 30072-2 (e.g., a server) generatestrust scores for cryptocurrency transactors (e.g., user devices 29302,intermediate systems 29304, and automated transaction systems 29306).For example, the trust system 30072-2 can generate trust scores fordifferent blockchain addresses that interact on the blockchain network30070. The trust system 30072-2 may determine the trust scores based ondata retrieved from various data sources along with blockchain data uponwhich the cryptocurrency is based. For example, the trust system 30072-2can determine trust scores for blockchain transactions in a similarmanner as described with respect to the trust node 29300-1. Acryptocurrency transactor can request trust scores from the trust system30072-2 before engaging in a blockchain transaction in which funds(e.g., blockchain tokens) are transacted on the blockchain.

FIG. 323 illustrates an example trust system 30072-2 that can determinetrust scores for blockchain addresses. The trust system 30072-2 of FIG.323 includes modules that perform similar functions as described withrespect to the trust node 29300-1. The detailed example trust system30072-2 of FIG. 323 is only an example trust system. As such, a trustsystem may include additional/alternative functionality than thatillustrated in FIG. 323 .

The data acquisition and processing module 30112 acquires and processesa variety of types of data, such as custody data and fraud data, whichmay be stored in the trust system data store 30111. The data acquisitionand processing module 30112 may also generate a fraud label thatindicates whether the blockchain address is likely fraudulent based onthe acquired fraud data. The blockchain acquisition and processingmodule 30114 acquires and processes blockchain data that may be storedin the trust system data store 30111. The trust system data store 30111may also include a plurality blockchain address records. The graphgeneration and processing module 30116 generates a blockchain graph datastructure based on the blockchain data. The graph generation datastructure may be stored in the graph data store 30113. The graphgeneration and processing module 30116 may also process the graph todetermine one or more graph-based values (e.g., importance values) thatmay be used to generate trust scores. The feature generation module30118 generates scoring features for blockchain addresses. The scoringmodel generation module 30120 generates one or more scoring models basedon the scoring features and other data (e.g., labeled fraud data). Thescoring models may be stored in the scoring model data store 30115. Thetrust score generation module 30122 generates one or more trust scoresfor blockchain addresses using one or more scoring models and thescoring features associated with the blockchain addresses.

The trust system 30072-2 includes a transactor interface module 30124that receives a trust request for a blockchain address from a requestingdevice. The trust request may refer to a request other than a contracttrust request. The transactor interface module 30124 sends a trustresponse including a trust score to the requesting device.

As described with respect to the trust network 30072-1, the trust system30072-2 can also provide smart contract templates 30110 and completedsmart contracts to requesting parties for execution on the blockchainnetwork 30070. Additionally, the trust system 30072-2 can also beconfigured to provide trust scores to smart contracts instantiated onthe blockchain network 30070.

FIG. 321 illustrates a method that describes operation of theenvironment of FIG. 320 . FIG. 322 is a functional block diagram thatillustrates interactions between a sender user device 29302, anintermediate transaction system 29304 (e.g., an exchange or centralizedwallet system) (hereinafter “intermediate system 29304”), the blockchainnetwork 30070, and the trust network/system 30072 according to FIG. 321. FIGS. 323-324 illustrate an example trust system 30072-2 and trustnode 30072-1 a that implement functionality associated with the smartcontracts. For example, FIGS. 323-324 illustrate contract distributionmodules 30130-1, 30130-2, contract template data stores 30132-1,30132-2, and contract interface modules 30134-1, 30134-2 that implementfunctionality associated with smart contracts.

The method of FIG. 321 is now described with respect to FIGS. 322-326 .In block 30090, a smart contract template 30110 is provided by the trustnetwork/system 30072 for completion and distribution. The contractdistribution modules 30130 and contract template data stores 30132provide the smart contract completion and distribution functionality forthe trust system 30072-2 and trust node 30072-1 a. The contract templatedata stores 30132 store smart contract templates. The contractdistribution modules 30130 provide an interface (e.g., an API) that aparty (e.g., an exchange) can use to modify/complete the smart contracttemplate 30110 and acquire the completed smart contract forinstantiation on the blockchain network 30070. In the trust network30072-1, smart contract template storage, smart contract generation, andsmart contract distribution may be decentralized across the plurality oftrust nodes. The trust node 30072-1 a of FIG. 324 is an example trustnode. As such, trust nodes may include additional and/or alternativefunctionality (e.g., see FIG. 303 ).

Referring to FIG. 323 , a smart contract template 30110 can includesmart contract code 30140 that executes on the blockchain network 30070.The smart contract code 30140 may include computer-readable instructionsthat may be executed by one or more processing units. Smart contractsmay be written in one or more languages and compiled into bytecode thatis deployed on the blockchain network 30070 for execution. With respectto the Ethereum blockchain, a smart contract can be written in one ormore programming languages (e.g., Solidity) and compiled into EthereumVirtual Machine bytecode for deployment on the Ethereum blockchain. Thesmart contract code can be executed on the blockchain network 30070 toimplement the functionality attributed to the smart contracts herein.For example, the smart contract code 30140 can include a series of rules(e.g., business rules) that are run on the blockchain network 30070.Example functionality executed by the smart contract code 30140 mayinclude, but is not limited to: 1) generating a contract trust request,2) receiving a trust score, 3) determining whether the trust scoreindicates a receiver is trustworthy, and 4) completing/canceling atransaction based on the trust score. Since the smart contracts may beimplemented on a blockchain network, the smart contracts may also bereferred to as “blockchain smart contracts” or “decentralizedblockchain-based smart contracts.”

A smart contract template 30140 may include fields for receiving valuesthat can complete the smart contract template 30140. The intermediatesystem 29304 (e.g., an exchange) can provide the values for completingthe fields in the smart contract. Example smart contract fields mayinclude, but are not limited to: 1) a contract trust threshold field30142, 2) a sender address field 30144, 3) a receiver address field30146, 4) a transaction amount field 30148, 5) a trust fee field 30140,and 6) a trust fee payment address 30142. A sender transactor canprovide some/all of the values for completing the smart contract to theintermediate system 29304 (e.g., see FIG. 325 ). The contractdistribution modules 30130 can complete the smart contract templatesaccording to the values received from the intermediate system 29304. Theintermediate system 29304 can instantiate the completed smart contracton the blockchain network 30070.

In block 30092, the sender transactor is provided with an interface forarranging a transaction with a receiver address. For example, theintermediate system 29304 (e.g., an exchange) can provide a userinterface (e.g., a GUI) for the sender transactor to set up atrust-protected transaction with a receiver. The interface can receive asender's inputs for setting up and completing the transaction. Forexample, the interface can include GUI elements for receiving one ormore addresses, coin amounts, and a specified contract trust threshold.In some implementations, the trust system/node administrator can workwith parties (e.g., exchanges/wallets) to build an interface (e.g., anexchange/wallet interface).

FIGS. 325-326 illustrate an example sender interface on a user device29302. The sender interface allows the sender to specify a transactionamount (e.g., 5 coins) to send to a receiver address. In FIG. 325 , theGUI includes GUI elements that the sender can use to select whether touse trust protection. The example GUI elements are radio buttons thatprovide the user with a yes/no selection. If a trust-protectedtransaction is selected, there is a GUI element that can receive auser-specified level of trust for the transaction. For example, thesender may select the low, medium, or high GUI buttons to select a low,medium, or high risk level for the transaction. In FIG. 325 , the userhas selected the medium level of trust, as indicated by the darkened“Med.” GUI element. The selectable risk levels may correspond tocontract trust threshold values. For example, the low risk level mayrequire a higher trust score (e.g., a greater trustworthiness) forcompletion of the transaction than the medium and high risk levels. Insome implementations, the intermediate system 29304 may include a presetcontract trust threshold (e.g., set by an exchange) instead of providingthe user with a GUI element for selecting the risk level. In someimplementations, the smart contract template 30110 may include a presetcontract trust threshold.

The GUI of FIG. 325 provides a summary of the total amount for thetrust-protected transaction. For example, the GUI indicates the trustfee and the network fee for the trust report and the contract execution,respectively. The GUI includes a “Send Coins” button GUI element thatthe user can select to begin the transaction according to the smartcontract. FIG. 326 illustrates an example GUI provided by theintermediate system 29304 that indicates the transaction was completed.The GUIs illustrate in FIGS. 325-326 may be provided by the intermediatesystem 29304 (e.g., via a web-based interface) and/or an applicationinstalled on the user device 29302.

In block 30094, the smart contract is generated according to thesender's input and instantiated on the blockchain network 30070. Forexample, the intermediate system 29304 can send the values entered intothe sender interface (e.g., see FIG. 325 ) to the contract distributionmodules 30130. The contract distribution modules 30130 can generate acompleted contract using the received values and a smart contracttemplate 30110. The contract distribution modules 30130 can send thecompleted contract to the intermediate system 29304. The intermediatesystem 29304 can then instantiate the smart contract on the blockchainnetwork 30070.

The intermediate system 29304 may instantiate the smart contract withdifferent completed fields. For example, the intermediate system 29304can instantiate the smart contract with a specified contract trustthreshold in some implementations. In another example, the intermediatesystem 29304 can instantiate the smart contract without a specifiedcontract trust threshold. In this example, the contract trust thresholdvalue may be sent to the contract (e.g., by the intermediate system29304) after the smart contract is instantiated on the blockchainnetwork 30070. In a similar manner, the intermediate system 29304 mayinclude any values described herein in the instantiated smart contractor may provide any of the values after instantiation of the smartcontract. The number of defined values included in the smart contractcan vary based on the implementation chosen by the operators of theintermediate system 29304 and/or the capabilities of the blockchainnetwork 30070. It can be assumed hereinafter that a completed smartcontract includes values for the contract trust threshold, senderaddress, receiver address, transaction amount, trust fee, and trust feepayment address.

Other smart contract implementations may vary based on decisions made bythe operator of the intermediate system 29304 and/or the capabilities ofthe blockchain network 30070. For example, instead of retrieving a newsmart contract for each transaction and completing the smart contract,in some cases, a smart contract may remain on the blockchain network andrun subsequent transactions based on newly received values (e.g., newreceiver addresses and transaction amounts). In some cases, anintermediate system may retrieve a single version of the smart contractfrom the trust network/system and modify it for multiple transactions.

In some implementations, the sender pays a network fee in order toinstantiate a smart contract on the blockchain network 30070. In theseimplementations, the network fee may be paid from the sender address toinstantiate the smart contract. The blockchain network 30070 may beginexecution of the smart contract after payment of the network fee. Thenetwork fee may be referred to as “gas” in some cases. In someimplementations, the network fee may be set by the blockchain network30070. Over time, the blockchain network 30070 may modify the networkfee. Although a sender address may pay the network fee for instantiationand execution of the smart contract, in some implementations, thenetwork fee may be paid for by an existing intermediate system address.

The instantiated smart contract 30080 is associated with a smartcontract address on the blockchain network 30070. The sender funds(e.g., transaction amount and trust fee amount) may be transferred tothe contract blockchain address when the smart contract is instantiated.The smart contract 30080 can hold the funds and distribute the fundsaccording to the smart contract 30080 described herein. The intermediatesystem 29304 may send the instantiated contract address to the trustnetwork/system 30072. The trust network/system 30072 can monitor theinstantiated contract address to verify trust fee payments and monitorthe blockchain ledger 30078 for a contract trust request.

As described herein, the trust network/system 30072 may receive trustfee payments for providing trust reports. With respect to trust reportsprovided to the smart contract 30080, the trust network/system 30072 mayreceive payments at a trust fee payment address on the blockchainnetwork 30070. The trust fee payment address may be controlled by thetrust network/system 30072. The contract address may pay the trust feeusing the blockchain's native tokens, referred to herein ascryptocurrency blockchain tokens. In some implementations, the trustnetwork/system 30072 can include one or more blockchain addresses forreceiving trust fee payments. The trust network/system 30072 can acquireone or more trust fee payments from the trust fee payment address. Forthe trust system 30072-2, the trust fee may be set/modified by theoperator of the trust system 30072-2. For the trust network 30072-1, thereward protocol can set/modify the trust fee.

In block 30096, the smart contract 30080 acquires a trust score from thetrust network/system 30072. To acquire a trust score, the smart contract30080 may generate a contract trust request for the receiver address.The contract trust request may be a request for a trust report for thereceiver address (e.g., one or more trust scores for the receiveraddress). The smart contract 30080 can generate the contract trustrequest in a variety of ways. In some implementations, the smartcontract 30080 can write the contract trust request to a ledger (e.g.,the blockchain ledger 30078) under the contract address. The contracttrust request can include a code written to the ledger 30078 thatidentifies the ledger entry as a contract trust request for the trustnetwork/system 30072. Additionally, the contract trust request mayindicate the receiver address for which the trust report is requested.Although the smart contract 30080 may write the trust request to theledger 30078, in some implementations, the smart contract 30080 may beconfigured to send requests to the trust network/system 30072 (e.g., viaan API) if the blockchain network 30070 supports the functionality.

The trust network/system 30072 (e.g., the contract interface modules30134) may be configured to scan the blockchain ledger 30078 to identifycontract trust requests made by smart contracts 30080. Inimplementations where the contract address is reported to the trustnetwork/system 30072 (e.g., by the intermediate system 29304), the trustnetwork/system 30072 may be configured to scan the reported contractaddresses for contract trust requests.

In some implementations, prior to providing the trust score to the smartcontract 30080, the trust network/system 30072 determines whether thetrust fee has been paid. The trust network/system 30072 (e.g., thecontract interface modules 30134) can scan the blockchain ledger 30078to determine whether the trust fee has been paid. For example, the trustnetwork/system 30072 may determine that the trust fee has been paid byidentifying a transaction between the contract address and the trust feepayment address for the trust fee amount.

After identification of the contract trust request and verification ofthe trust fee payment, the trust network/system 30072 (e.g., thecontract interface modules 30134) may identify/calculate the trustscore(s) for the receiving address indicated in the contract trustrequest. The trust network/system 30072 may then send the trust score tothe smart contract 30080. Inputting data into a smart contract may varybased on the blockchain network. For example, on the Ethereum network,data may be input into the smart contract according to the EthereumRequest for Comments (ERC) technical standard.

In block 30098, the smart contract 30080 determines whether the receiveris trustworthy based on the received trust score. In someimplementations, the smart contract 30080 may compare the received trustscore to the contract trust threshold to determine whether the receiveris trustworthy. For example, where a larger trust score indicates a moretrustworthy address, the smart contract 30080 may determine that thereceiver address is trustworthy when the received trust score is greaterthan the contract trust threshold. In this example, the smart contract30080 may determine that the receiver address is not sufficientlytrustworthy when the received trust score is less than the contracttrust threshold. In a specific example, where the trust score is adecimal value that indicates a likelihood of fraud (e.g., a percentagevalue from 0-100%), an example contract trust threshold may be set to0.35 (i.e., 35%). As described herein, in some implementations, a trustscore may range from a maximum negative value to a maximum positivevalue (e.g., −1.00 to 1.00), where a larger negative value indicatesthat the address is more likely fraudulent. In this specific example, acontract trust threshold may be set somewhere in the range of −1.00 to1.00.

If the smart contract 30080 determines that the receiver address istrustworthy in block 30098, the smart contract 30080 sends thetransaction amount to the receiver address in block 30100. If the smartcontract 30080 determines that the receiver address is not trustworthyin block 30098, the smart contract 30080 sends the transaction amountback to the sender address in block 30102.

In block 30104, a transaction report is sent to the sender thatsummarizes the transaction. For example, the intermediate system 29304may read transaction data from the blockchain network 30070, such aswhether the transaction was completed/canceled and the amount of tokensent in the transaction. The intermediate system 29304 may then generatea transaction report for the sender user device 29302. An exampletransaction report is illustrated in FIG. 326 . The transaction reportof FIG. 326 indicates that the transaction was completed because thereceiver address was sufficiently trustworthy.

FIG. 327 illustrates an example method describing operation of theintermediate system 29304 of FIG. 320 . In block 30150, the intermediatesystem 29304 generates an interface (e.g., a web-based GUI) for thesender user device 29302. In block 30152, the intermediate system 29304receives contract values from the sender user device 29302, such asvalues inserted into the interface. In block 30154, the intermediatesystem 29304 retrieves a smart contract that was completed by the trustnetwork/system 30072 based on the contract values.

In block 30156, the intermediate system 29304 instantiates the completedsmart contract on the blockchain network 30070. In block 30158, theintermediate system 29304 reports the smart contract address to thetrust network/system 30072. In block 30160, the intermediate system29304 generates a transaction report for the sender based on thecompletion/cancellation of the transaction associated with theinstantiated smart contract.

FIG. 328 illustrates an example method describing operation of the trustnetwork/system 30072 of FIG. 320 . In block 30180, the trustnetwork/system 30072 receives contract values for a smart contracttemplate from the intermediate system 29304. In block 30182, the trustnetwork/system 30072 generates a completed smart contract based on thereceived contract values. In block 30184, the trust network/system 30072sends the completed smart contract to the intermediate system 29304.

In block 30186, the trust network/system 30072 receives a contractaddress from the intermediate system 29304 for the instantiated smartcontract. In block 30188, the trust network/system 30072 monitors theblockchain ledger 30078 for a trust fee payment. In block 30190, thetrust network/system 30072 monitors the blockchain ledger 30078 for acontract trust request associated with the contract address. In block30192, the trust network/system 30072 sends a trust score for thereceiver address to the smart contract.

Modules and data stores included in the trust networks/systems representfeatures that may be included in the trust networks/systems of thepresent disclosure. The modules and data stores described herein may beembodied by electronic hardware, software, firmware, or any combinationthereof. Depiction of different features as separate modules and datastores does not necessarily imply whether the modules and data storesare embodied by common or separate electronic hardware or softwarecomponents. In some implementations, the features associated with theone or more modules and data stores depicted herein may be realized bycommon electronic hardware and software components. In someimplementations, the features associated with the one or more modulesand data stores depicted herein may be realized by separate electronichardware and software components.

The modules and data stores may be embodied by electronic hardware andsoftware components including, but not limited to, one or moreprocessing units, one or more memory components, one or moreinput/output (I/O) components, and interconnect components. Interconnectcomponents may be configured to provide communication between the one ormore processing units, the one or more memory components, and the one ormore I/O components. For example, the interconnect components mayinclude one or more buses that are configured to transfer data betweenelectronic components. The interconnect components may also includecontrol circuits (e.g., a memory controller and/or an I/O controller)that are configured to control communication between electroniccomponents.

The one or more processing units may include one or more centralprocessing units (CPUs), graphics processing units (GPUs), digitalsignal processing units (DSPs), or other processing units. The one ormore processing units may be configured to communicate with memorycomponents and I/O components. For example, the one or more processingunits may be configured to communicate with memory components and I/Ocomponents via the interconnect components.

A memory component (e.g., main memory and/or a storage device) mayinclude any volatile or non-volatile media. For example, memory mayinclude, but is not limited to, electrical media, magnetic media, and/oroptical media, such as a random access memory (RAM), read-only memory(ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM(EEPROM), Flash memory, hard disk drives (HDD), magnetic tape drives,optical storage technology (e.g., compact disc, digital versatile disc,and/or Blu-ray Disc), or any other memory components.

Memory components may include (e.g., store) data described herein. Forexample, the memory components may include the data included in the datastores. Memory components may also include instructions that may beexecuted by one or more processing units. For example, memory mayinclude computer-readable instructions that, when executed by one ormore processing units, cause the one or more processing units to performthe various functions attributed to the modules and data storesdescribed herein.

The I/O components may refer to electronic hardware and software thatprovide communication with a variety of different devices. For example,the I/O components may provide communication between other devices andthe one or more processing units and memory components. In someexamples, the I/O components may be configured to communicate with acomputer network. For example, the I/O components may be configured toexchange data over a computer network using a variety of differentphysical connections, wireless connections, and protocols. The I/Ocomponents may include, but are not limited to, network interfacecomponents (e.g., a network interface controller), repeaters, networkbridges, network switches, routers, and firewalls. In some examples, theI/O components may include hardware and software that is configured tocommunicate with various human interface devices, including, but notlimited to, display screens, keyboards, pointer devices (e.g., a mouse),touchscreens, speakers, and microphones. In some examples, the I/Ocomponents may include hardware and software that is configured tocommunicate with additional devices, such as external memory (e.g.,external HDDs).

In some implementations, the trust networks/systems may include one ormore computing devices (e.g., node computing/server devices) that areconfigured to implement the techniques described herein. Put anotherway, the features attributed to the modules and data stores describedherein may be implemented by one or more computing devices. Each of theone or more computing devices may include any combination of electronichardware, software, and/or firmware described above. For example, eachof the one or more computing devices may include any combination ofprocessing units, memory components, I/O components, and interconnectcomponents described above. The one or more computing devices of thetrust networks/systems may also include various human interface devices,including, but not limited to, display screens, keyboards, pointingdevices (e.g., a mouse), touchscreens, speakers, and microphones. Thecomputing devices may also be configured to communicate with additionaldevices, such as external memory (e.g., external HDDs).

The one or more computing devices may reside within a single machine ata single geographic location in some examples. In other examples, theone or more computing devices may reside within multiple machines at asingle geographic location. In still other examples, the one or morecomputing devices of the trust networks/systems may be distributedacross a number of geographic locations.

Dual Process Artificial Neural Network

In embodiments, the platform 100 includes a dual process artificialneural network (DPANN) system 32900. The DPANN system 32900 includes anartificial neural network (ANN) having behaviors and operationalprocesses (such as decision-making) that are products of a trainingsystem and a retraining system. The training system is configured toperform automatic, trained execution of ANN operations. The retrainingsystem performs effortful, analytical, intentional retraining of theANN, such as based on one or more relevant aspects of the ANN, such asmemory, one or more input data sets (including time information withrespect to elements in such data sets), one or more goals or objectives(including ones that may vary dynamically, such as periodically and/orbased on contextual changes, such as ones relating to the usage contextof the ANN), and/or others. In cases involving memory-based retraining,the memory may include original/historical training data and refinedtraining data. The DPANN system 32900 includes a dual process learningfunction (DPLF) configured to manage and perform an ongoing dataretention process. The DPLF (including, where applicable, memorymanagement process) facilitate retraining and refining of behavior ofthe ANN. The DPLF provides a framework by which the ANN creates outputssuch as predictions, classifications, recommendations, conclusionsand/or other outputs based on a historic inputs, new inputs, and newoutputs (including outputs configured for specific use cases, includingones determined by parameters of the context of utilization (which mayinclude performance parameters such as latency parameters, accuracyparameters, consistency parameters, bandwidth utilization parameters,processing capacity utilization parameters, prioritization parameters,energy utilization parameters, and many others).

In embodiments, the DPANN system 32900 stores training data, therebyallowing for constant retraining based on results of decisions,predictions, and/or other operations of the ANN, as well as allowing foranalysis of training data upon the outputs of the ANN. The management ofentities stored in the memory allows the construction and execution ofnew models, such as ones that may be processed, executed or otherwiseperformed by or under management of the training system. The DPANNsystem 32900 uses instances of the memory to validate actions (e.g., ina manner similar to the thinking of a biological neural network(including retrospective or self-reflective thinking about whetheractions that were undertaken under a given situation where optimal) andperform training of the ANN, including training that intentionally feedsthe ANN with appropriate sets of memories (i.e., ones that producefavorable outcomes given the performance requirements for the ANN).

In embodiments, the DPLF may be or include the continued processretention of one or more training datasets and/or memories stored in thememory over time. The DPLF thereby allows the ANN to apply existingneural functions and draw upon sets of past events (including ones thatare intentionally varied and/or curated for distinct purposes), such asto frame understanding of and behavior within present, recent, and/ornew scenarios, including in simulations, during training processes, andin fully operational deployments of the ANN. The DPLF may provide theANN with a framework by which the ANN may analyze, evaluate, and/ormanage data, such as data related to the past, present and future. Assuch, the DPLF plays a crucial role in training and retraining the ANNvia the training system and the retraining system.

In embodiments, the DPLF is configured to perform a dual-processoperation to manage existing training processes and is also configuredto manage and/or perform new training processes, i.e., retrainingprocesses. In embodiments, each instance of the ANN is trained via thetraining system and configured to be retrained via the retrainingsystem. The ANN encodes training and/or retraining datasets, stores thedatasets, and retrieves the datasets during both training via thetraining system and retraining via the retraining system. The DPANNsystem 32900 may recognize whether a dataset (the term dataset in thiscontext optionally including various subsets, supersets, combinations,permutations, elements, metadata, augmentations, or the like, relativeto a base dataset used for training or retraining), storage activity,processing operation and/or output, has characteristics that nativelyfavor the training system versus the retraining system based on itsrespective inputs, processing (e.g., based on its structure, type,models, operations, execution environment, resource utilization, or thelike) and/or outcomes (including outcome types, performance requirements(including contextual or dynamic requirements), and the like. Forexample, the DPANN system 32900 may determine that poor performance ofthe training system on a classification task may indicate a novelproblem for which the training of the ANN was not adequate (e.g., intype of data set, nature of input models and/or feedback, quantity oftraining data, quality of tagging or labeling, quality of supervision,or the like), for which the processing operations of the ANN are notwell-suited (e.g., where they are prone to known vulnerabilities due tothe type of neural network used, the type of models used, etc.), andthat may be solved by engaging the retraining system to retrain themodel to teach the model to learn to solve the new classificationproblem (e.g., by feeding it many more labeled instances of correctlyclassified items). With periodic or continuous evaluation of theperformance of the ANN, the DPANN system may subsequently determine thathighly stable performance of the ANN (such as where only smallimprovements of the ANN occur over many iterations of retraining by theretraining system) indicates readiness for the training system toreplace the retraining system (or be weighted more favorably where bothare involved). Over longer periods of time, cycles of varyingperformance may emerge, such as where a series of novel problems emerge,such that the retraining system of the DPANN is serially engaged, asneeded, to retrain the ANN and/or to augment the ANN by providing asecond source of outputs (which may be fused or combined with ANNoutputs to provide a single result (with various weightings acrossthem), or may be provided in parallel, such as enabling comparison,selection, averaging, or context- or situation-specific application ofthe respective outputs).

In embodiments, the ANN is configured to learn new functions inconjunction with the collection of data according to the dual-processtraining of the ANN via the training system and the retraining system.The DPANN system 32900 performs analysis of the ANN via the trainingsystem and performs initial training of the ANN such that the ANN gainsnew internal functions (or internal functions are subtracted ormodified, such as where existing functions are not contributing tofavorable outcomes). After the initial training, the DPANN system 32900performs retraining of the ANN via the retraining system. To perform theretraining, the retraining system evaluates the memory and historicprocessing of the ANN to construct targeted DPLF processes forretraining. The DPLF processes may be specific to identified scenarios.The ANN processes can run in parallel with the DPLF processes. By way ofexample, the ANN may function to operate a particular make and model ofa self-driving car after the initial training by the training system.The DPANN system 32900 may perform retraining of the functions of theANN via the retraining system, such as to allow the ANN to operate adifferent make and model of car (such as one with different cameras,accelerometers and other sensors, different physical characteristics,different performance requirements, and the like), or even a differentkind of vehicle, such as a bicycle or a spaceship.

In embodiments, as quality of outputs and/or operations of the ANNimproves, and as long as the performance requirements and the context ofutilization for the ANN remain fairly stable, performing thedual-process training process can become a decreasingly demandingprocess. As such, the DPANN system 32900 may determine that fewerneurons of the ANN are required to perform operations and/or processesof the ANN, that performance monitoring can be less intensive (such aswith longer intervals between performance checks), and/or that theretraining is no longer necessary (at least for a period of time, suchas until a long-term maintenance period arrives and/or until there aresignificant shifts in context of utilization). As the ANN continues toimprove upon existing functions and/or add new functions via thedual-process training process, the ANN may perform other, at times more“intellectually-demanding” (e.g., retraining intensive) taskssimultaneously. For example, utilizing dual process-learned knowledge ofa function or process being trained, the ANN can solve an unrelatedcomplex problem or make a retraining decision simultaneously. Theretraining may include supervision, such as where an agent (e.g., humansupervisor or intelligent agent) directs the ANN to a retrainingobjective (e.g., “master this new function”) and provides a set oftraining tasks and feedback functions (such as supervisory grading) forthe retraining. In-embodiments, the ANN can be used to organize thesupervision, training and retraining of other dual process-trained ANNs,to seed such training or retraining, or the like.

In embodiments, one or more behaviors and operational processes (such asdecision-making) of the ANN may be products of training and retrainingprocesses facilitated by the training system and the retraining system,respectively. The training system may be configured to perform automatictraining of ANN, such as by continuously adding additional instances oftraining data as it is collected by or from various data sources. Theretraining system may be configured to perform effortful, analytical,intentional retraining of the ANN, such as based on memory (e.g., storedtraining data or refined training data) and/or optionally based onreasoning or other factors. For example, in a deployment managementcontext, the training system may be associated with a standard responseby the ANN, while the retraining system may implement DPLF retrainingand/or network adaptation of the ANN. In some cases, retraining of theANN beyond the factory, or “out-of-the-box,” training level may involvemore than retraining by the retraining system. Successful adjustment ofthe ANN by one or more network adaptations may be dependent on theoperation of one or more network adjustments of the training system.

In embodiments, the training system may facilitate fast operating by andtraining of the ANN by applying existing neural functions of the ANNbased on training of the ANN with previous datasets. Standardoperational activities of the ANN that may draw heavily on the trainingsystem may include one or more of the methods, processes, workflows,systems, or the like described throughout this disclosure and thedocuments incorporated herein, such as, without limitation: definedfunctions within networking (such as discovering available networks andconnections, establishing connections in networks, provisioning networkbandwidth among devices and systems, routing data within networks,steering traffic to available network paths, load balancing acrossnetworking resources, and many others); recognition and classification(such as of images, text, symbols, objects, video content, music andother audio content, speech content, and many others); spoken words;prediction of states and events (such as prediction of failure modes ofmachines or systems, prediction of events within workflows, predictionsof behavior in shopping and other activities, and many others); control(such as controlling autonomous or semi-autonomous systems, automatedagents (such as automated call-center operations, chat bots, and thelike) and others); and/or optimization and recommendation (such as forproducts, content, decisions, and many others). ANNs may also besuitable for training datasets for scenarios that only require output.The standard operational activities may not require the ANN to activelyanalyze what is being asked of the ANN beyond operating on well-defineddata inputs, to calculate well-defined outputs for well-defined usecases. The operations of the training system and/or the retrainingsystem may be based on one or more historic data training datasets andmay use the parameters of the historic data training datasets tocalculate results based on new input values and may be performed withsmall or no alterations to the ANN or its input types. In embodiments,an instance of the training system can be trained to classify whetherthe ANN is capable of performing well in a given situation, such as byrecognizing whether an image or sound being classified by the ANN is ofa type that has historically been classified with a high accuracy (e.g.,above a threshold).

In embodiments, network adaptation of the ANN by one or both of thetraining system and the retraining system may include a number ofdefined network functions, knowledge, and intuition-like behavior of theANN when subjected to new input values. In such embodiments, theretraining system may apply the new input values to the DPLF system toadjust the functional response of the ANN, thereby performing retrainingof the ANN. The DPANN system 32900 may determine that retraining the ANNvia network adjustment is necessary when, for example, withoutlimitation, functional neural networks are assigned activities andassignments that require the ANN to provide a solution to a novelproblem, engage in network adaptation or other higher-order cognitiveactivity, apply a concept outside of the domain in which the DPANN wasoriginally designed, support a different context of deployment (such aswhere the use case, performance requirements, available resources, orother factors have changed), or the like. The ANN can be trained torecognize where the retraining system is needed, such as by training theANN to recognize poor performance of the training system, highvariability of input data sets relative to the historical data sets usedto train the training system, novel functional or performancerequirements, dynamic changes in the use case or context, or otherfactors. The ANN may apply reasoning to assess performance and providefeedback to the retraining system. The ANN may be trained and/orretrained to perform intuitive functions, optionally including by acombinatorial or re-combinatorial process (e.g., including geneticprogramming wherein inputs (e.g., data sources), processes/functions(e.g., neural network types and structures), feedback, and outputs, orelements thereof, are arranged in various permutations and combinationsand the ANN is tested in association with each (whether in simulationsor live deployments), such as in a series of rounds, or evolutionarysteps, to promote favorable variants until a preferred ANN, or preferredset of ANNs is identified for a given scenario, use case, or set ofrequirements). This may include generating a set of input “ideas” (e.g.,combinations of different conclusions about cause-and-effect in adiagnostic process) for processing by the retraining system andsubsequent training and/or by an explicit reasoning process, such as aBayesian reasoning process, a casuistic or conditional reasoningprocess, a deductive reasoning process, an inductive reasoning process,or others (including combinations of the above) as described in thisdisclosure or the documents incorporated herein by reference.

Referring to FIG. 329 , in embodiments, the DPLF may perform an encodingprocess of the DPLF to process datasets into a stored form for futureuse, such as retraining of the ANN by the retraining system. Theencoding process enables datasets to be taken in, understood, andaltered by the DPLF to better support storage in and usage from thememory. The DPLF may apply current functional knowledge and/or reasoningto consolidate new input values. In the example provided, data is takenin as data input 32902. The memory can include short-term memory (STM)32904, long-term memory (LTM) 32906, or a combination thereof. Thedatasets may be stored in one or both of the STM and the LTM. The STMmay be implemented by the application of specialized behaviors insidethe ANN (such as recurrent neural network, which may be gated orun-gated, or long-term short-term neural networks). The LTM may beimplemented by storing scenarios, associated data, and/or unprocesseddata that can be applied to the discovery of new scenarios. The encodingprocess may include processing and/or storing, for example, visualencoding data (e.g., processed through a Convolution Neural Network),acoustic sensor encoding data (e.g., how something sounds, speechencoding data (e.g., processed through a deep neural network (DNN),optionally including for phoneme recognition), semantic encoding data ofwords, such to determine semantic meaning, e.g., by using a HiddenMarkov Model (HMM); and/or movement and/or tactile encoding data (suchas operation on vibration/accelerometer sensor data, touch sensor data,positional or geolocation data, and the like). While datasets may enterthe DPLF system through one of these modes, the form in which thedatasets are stored may differ from an original form of the datasets andmay pass-through neural processing engines to be encoded into compressedand/or context-relevant format. For example, an unsupervised instance ofthe ANN can be used to learn the historic data into a compressed format.

In embodiments, the encoded datasets are retained within the DPLFsystem. Encoded datasets are first stored in short-term DPLF, i.e., STM.For example, sensor datasets may be primarily stored in STM, and may bekept in STM through constant repetition. The datasets stored in the STMare active and function as a kind of immediate response to new inputvalues. The DPANN system 32900 may remove datasets from STM in responseto changes in data streams due to, for example, running out of space inSTM as new data is imported, processed and/or stored. For example, it isviable for short-term DPLF to only last between 15 and 30 seconds. STMmay only store small amounts of data typically embedded inside the ANN.

In embodiments, the DPANN system 32900 may measure attention based onutilization of the training system, of the DPANN system 32900 as awhole, and/or the like, such as by consuming various indicators ofattention to and/or utilization of outputs from the ANN and transmittingsuch indicators to the ANN in response (similar to a “moment ofrecognition” in the brain where attention passes over something and thecognitive system says “aha!”). In embodiments, attention can be measuredby the sheer amount of the activity of one or both of the systems on thedata stream. In embodiments, a system using output from the ANN canexplicitly indicate attention, such as by an operator directing the ANNto pay attention to a particular activity (e.g., to respond to adiagnosed problem, among many other possibilities). The DPANN system32900 may manage data inputs to facilitate measures of attention, suchas by prompting and/or calculating greater attention to data that hashigh inherent variability from historical patterns (e.g., in rates ofchange, departure from norm, etc.), data indicative of high variabilityin historical performance (such as data having similar characteristicsto data sets involved in situations where the ANN performed poorly intraining), or the like.

In embodiments, the DPANN system 32900 may retain encoded datasetswithin the DPLF system according to and/or as part of one or morestorage processes. The DPLF system may store the encoded datasets in LTMas necessary after the encoded datasets have been stored in STM anddetermined to be no longer necessary and/or low priority for a currentoperation of the ANN, training process, retraining process, etc. The LTMmay be implemented by storing scenarios, and the DPANN system 32900 mayapply associated data and/or unprocessed data to the discovery of newscenarios. For example, data from certain processed data streams, suchas semantically encoded datasets, may be primarily stored in LTM. TheLTM may also store image (and sensor) datasets in encoded form, amongmany other examples.

In embodiments, the LTM may have relatively high storage capacity, anddatasets stored within LTM may, in some scenarios, be effectively storedindefinitely. The DPANN system 32900 may be configured to removedatasets from the LTM, such as by passing LTM data through a series ofmemory structures that have increasingly long retrieval periods orincreasingly high threshold requirements to trigger utilization (similarto where a biological brain “thinks very hard” to find precedent to dealwith a challenging problem), thereby providing increased salience ofmore recent or more frequently used memories while retaining the abilityto retrieve (with more time/effort) older memories when the situationjustifies more comprehensive memory utilization. As such, the DPANNsystem 32900 may arrange datasets stored in the LTM on a timeline, suchas by storing the older memories (measured by time of origination and/orlatest time of utilization) on a separate and/or slower system, bypenalizing older memories by imposing artificial delays in retrievalthereof, and/or by imposing threshold requirements before utilization(such as indicators of high demand for improved results). Additionallyor alternatively, LTM may be clustered according to other categorizationprotocols, such as by topic. For example, all memories proximal in timeto a periodically recognized person may be clustered for retrievaltogether, and/or all memories that were related to a scenario may beclustered for retrieval together.

In embodiments, the DPANN system 32900 may modularize and link LTMdatasets, such as in a catalog, a hierarchy, a cluster, a knowledgegraph (directed/acyclic or having conditional logic), or the like, suchas to facilitate search for relevant memories. For example, all memorymodules that have instances involving a person, a topic, an item, aprocess, a linkage of n-tuples of such things (e.g., all memory modulesthat involve a selected pair of entities), etc. The DPANN system 32900may select sub-graphs of the knowledge graph for the DPLF to implementin one or more domain-specific and/or task-specific uses, such astraining a model to predict robotic or human agent behavior by usingmemories that relate to a particular set of robotic or human agents,and/or similar robotic or human agents. The DPLF system may cachefrequently used modules for different speed and/or probability ofutilization. High value modules (e.g., ones with high-quality outcomes,performance characteristics, or the like) can be used for otherfunctions, such as selection/training of STM keep/forget processes.

In embodiments, the DPANN system 32900 may modularize and link LTMdatasets, such as in various ways noted above, to facilitate search forrelevant memories. For example, memory modules that have instancesinvolving a person, a topic, an item, a process, a linkage of n-tuplesof such things (such as all memory modules that involve a selected pairof entities), or all memories associated with a scenario, etc., may belinked and searched. The DPANN system 32900 may select subsets of thescenario (e.g., sub-graphs of a knowledge graph) for the DPLF for adomain-specific and/or task-specific use, such as training a model topredict robotic or human agent behavior by using memories that relate toa particular set of robotic or human agents and/or similar robotic orhuman agents. Frequently used modules or scenarios can be cached fordifferent speed/probability of utilization, or other performancecharacteristics. High value modules or scenarios (ones wherehigh-quality outcomes results) can be used for other functions, such asselection/training of STM keep/forget processes, among others.

In embodiments, the DPANN system 32900 may perform LTM planning, such asto find a procedural course of action for a declaratively describedsystem to reach its goals while optimizing overall performance measures.The DPANN system 32900 may perform LTM planning when, for example, aproblem can be described in a declarative way, the DPANN system 32900has domain knowledge that should not be ignored, there is a structure toa problem that makes the problem difficult for pure learning techniques,and/or the ANN needs to be trained and/or retrained to be able toexplain a particular course of action taken by the DPANN system 32900.In embodiments, the DPANN system 32900 may be applied to a planrecognition problem, i.e., the inverse of a planning problem: instead ofa goal state, one is given a set of possible goals, and the objective inplan recognition is to find out which goal was being achieved and how.

In embodiments, the DPANN system 32900 may facilitate LTM scenarioplanning by users to develop long-term plans. For example, LTM scenarioplanning for risk management use cases may place added emphasis onidentifying extreme or unusual, yet possible, risks and opportunitiesthat are not usually considered in daily operations, such as ones thatare outside a bell curve or normal distribution, but that in fact occurwith greater-than-anticipated frequency in “long tail” or “fat tail”situations, such as involving information or market pricing processes,among many others. LTM scenario planning may involve analyzingrelationships between forces (such as social, technical, economic,environmental, and/or political trends) in order to explain the currentsituation, and/or may include providing scenarios for potential futurestates.

In embodiments, the DPANN system 32900 may facilitate LTM scenarioplanning for predicting and anticipating possible alternative futuresalong with the ability to respond to the predicted states. The LTMplanning may be induced from expert domain knowledge or projected fromcurrent scenarios, because many scenarios (such as ones involvingresults of combinatorial processes that result in new entities orbehaviors) have never yet occurred and thus cannot be projected byprobabilistic means that rely entirely on historical distributions. TheDPANN system 32900 may prepare the application to LTM to generate manydifferent scenarios, exploring a variety of possible futures to the DPLMfor both expected and surprising futures. This may be facilitated oraugmented by genetic programming and reasoning techniques as notedabove, among others.

In embodiments, the DPANN system 32900 may implement LTM scenarioplanning to facilitate transforming risk management into a planrecognition problem and apply the DPLF to generate potential solutions.LTM scenario induction addresses several challenges inherent to forecastplanning. LTM scenario induction may be applicable when, for example,models that are used for forecasting have inconsistent, missing,unreliable observations; when it is possible to generate not just onebut many future plans; and/or when LTM domain knowledge can be capturedand encoded to improve forecasting (e.g., where domain experts tend tooutperform available computational models). LTM scenarios can be focusedon applying LTM scenario planning for risk management. LTM scenariosplanning may provide situational awareness of relevant risk drivers bydetecting emerging storylines. In addition, LTM scenario planning cangenerate future scenarios that allow DPLM, or operators, to reasonabout, and plan for, contingencies and opportunities in the future.

In embodiments, the DPANN system 32900 may be configured to perform aretrieval process via the DPLF to access stored datasets of the ANN. Theretrieval process may determine how well the ANN performs with regard toassignments designed to test recall. For example, the ANN may be trainedto perform a controlled vehicle parking operation, whereby theautonomous vehicle returns to a designated spot, or the exit, byassociating a prior visit via retrieval of data stored in the LTM. Thedatasets stored in the STM and the LTM may be retrieved by differingprocesses. The datasets stored in the STM may be retrieved in responseto specific input and/or by order in which the datasets are stored,e.g., by a sequential list of numbers. The datasets stored in the LTMmay be retrieved through association and/or matching of events tohistoric activities, e.g., through complex associations and indexing oflarge datasets.

In embodiments, the DPANN system 32900 may implement scenario monitoringas at least a part of the retrieval process. A scenario may providecontext for contextual decision-making processes. In embodiments,scenarios may involve explicit reasoning (such as cause-and-effectreasoning, Bayesian, casuistic, conditional logic, or the like, orcombinations thereof) the output of which declares what LTM-stored datais retrieved (e.g., a timeline of events being evaluated and othertimelines involving events that potentially follow a similarcause-and-effect pattern). For example, diagnosis of a failure of amachine or workflow may retrieve historical sensor data as well as LTMdata on various failure modes of that type of machine or workflow(and/or a similar process involving a diagnosis of a problem state orcondition, recognition of an event or behavior, a failure mode (e.g., afinancial failure, contract breach, or the like), or many others).

FIG. 330 shows a biology based transaction system and FIG. 331 shows athalamus service 33000 and a set of input sensors streaming data fromvarious sources across a system 33002 with its centrally-managed datasources 33004. The thalamus service 33000 filters the into the controlsystem 33002 such that the control system is never overwhelmed by thetotal volume of information. In embodiments, the thalamus service 33000provides an information suppression mechanism for information flowswithin the system. This mechanism monitors all data streams and stripsaway irrelevant data streams by ensuring that the maximum data flowsfrom all input sensors are always constrained.

The biology-based transaction system include a PMCP devices interfaceand the control system 18002. The PMCP devices interface may beassociated with a PMCP API, an intelligence system a PMCP controller,classification, behavior analysis, prediction, augmentation, anetworking module, a security module, an ETL interface, or a PMCPdatabase. The control system 18002 may include the intelligence service33010, a quantum computing service, and the intake management system33006. The intake management system 33006 may include an intakeapplication library with networking and security, an intelligencesystem, an intake learning module, configured thalamus parameters, theintake controller 33018, and an intake management system withprioritizing, area focus, formatting, filtering, suppression, andcombining.

The thalamus service 33000 may be a gateway for all communication thatresponds to the prioritization of the control system 33002. The controlsystem 33002 may decide to change the prioritization of the datastreamed from the thalamus service 33000, for example, during a knownfire in an isolated area, and the event may direct the thalamus service33000 to continue to provide flame sensor information despite the factthat majority of this data is not unusual. The thalamus service 33000may be an integral part of the overall system communication framework.

In embodiments, the thalamus service 33000 includes an intake managementsystem 33006. The intake management system 33006 may be configured toreceive and process multiple large datasets by converting them into datastreams that are sized and organized for subsequent use by a centralcontrol system 33002 operating within one or more systems. For example,a robot may include vision and sensing systems that are used by itscentral control system 33002 to identify and move through an environmentin real time. The intake management system 33006 can facilitate robotdecision-making by parsing, filtering, classifying, or otherwisereducing the size and increasing the utility of multiple large datasetsthat would otherwise overwhelm the central control system 33002. Inembodiments, the intake management system may include an intakecontroller 33008 that works with an intelligence service 33010 toevaluate incoming data and take actions-based evaluation results.Evaluations and actions may include specific instruction sets receivedby the thalamus service 33000, for example the use of a set of specificcompression and prioritization tools stipulated within a “Networking”library module. In another example, thalamus service inputs may directthe use of specific filtering and suppression techniques. In a thirdexample, thalamus service inputs may stipulate data filtering associatedwith an area of interest such as a certain type of financialtransaction. The intake management system is also configured torecognize and manage datasets that are in a vectorized format such asPCMP, where they may be passed directly to central control, oralternatively deconstructed and processed separately. The intakemanagement system 33006 may include a learning module that receives datafrom external sources that enables improvement and creation ofapplication and data management library modules. In some cases, theintake management system may request external data to augment existingdatasets.

In embodiments the control system 33002 may direct the thalamus service33000 to alter its filtering to provide more input from a set ofspecific sources. This indication more input is handled by the thalamusservice 33000 by suppressing other information flows based to constrainthe total data flows to within a volume the central control system canhandle.

The thalamus service 33000 can operate by suppressing data based onseveral different factors, and in embodiments, the default factor maybeunusualness of the data. This unusualness is a constant monitoring ofall input sensors and determining the unusualness of the data.

In some embodiments, the thalamus service 33000 may suppress data basedon geospatial factors. The thalamus service 33000 may be aware of thegeospatial location of all sensors and is able to look for unusualpatterns in data based on geospatial context and suppress dataaccordingly.

In some embodiments, the thalamus service 33000 may suppress data basedon temporal factors. Data can be suppressed temporally, for example, ifthe cadence of the data can be reduced such that the overall data streamis filtered to level that can be handled by the central processing unit.

In some embodiments, the thalamus service 33000 may suppress data basedon contextual factors. In embodiments, context-based filtering is afiltering event in which the thalamus service 33000 is aware of somecontext-based event. In this context the filtering is made to suppressinformation flows not relating to the data from the event.

In embodiments, the control system 33002 can override the thalamusfiltering and decide to focus on a completely different area for anyspecific reason.

In embodiments, the system may include a vector module. In embodiments,the vector module may be used to convert data to a vectorized format. Inmany examples, the conversion of a long sequence of oftentimes similarnumbers into a vector, which may include short term future predictions,makes the communication both smaller in size and forward looking innature. In embodiments, forecast methods may include: moving average;weighted moving average; Kalman filtering; exponential smoothing;autoregressive moving average (ARMA) (forecasts depend on past values ofthe variable being forecast, and on past prediction errors);autoregressive integrated moving average (ARIMA) (ARMA on theperiod-to-period change in the forecasted variable); extrapolation;linear prediction; trend estimation (predicting the variable as a linearor polynomial function of time); growth curve (e.g., statistics); andrecurrent neural network.

In embodiments, the system may include a predictive model communicationprotocol (PMCP) system to support vector-based predictive models and apredictive model communication protocol (PMCP). Under the PMCP protocol,instead of traditional streams where individual data items aretransmitted, vectors representing how the data is changing or what isthe forecast trend in the data is communicated. The PMCP system maytransmit actual model parameters and receiving units such that edgedevices can apply the vector-based predictive models to determine futurestates. For example, each automated device in a network could train aregression model or a neural network, constantly fitting the datastreams to current input data. All automated devices leveraging the PMCPsystem would be able to react in advance of events actually happening,rather than waiting for depletion of inventory for an item, for example,to occur. Continuing the example, the stateless automated device canreact to the forecast future state and make the necessary adjustments,such as ordering more of the item.

In embodiments, the PMCP system enables communicating vectorizedinformation and algorithms that allow vectorized information to beprocessed to refine the known information regarding a set ofprobability-based states. For example, the PMCP system may supportcommunicating the vectorized information gathered at each point of asensor reading but also adding algorithms that allow the information tobe processed. Applied in an environment with large numbers of sensorswith different accuracies and reliabilities, the probabilisticvector-based mechanism of the PMCP system allows large numbers, if notall, data streams to combine to produce refined models representing thecurrent state, past states and likely future states of goods.Approximation methods may include importance sampling, and the resultingalgorithm is known as a particle filter, condensation algorithm, orMonte Carlo localization.

In embodiments, the vector-based communication of the PMCP system allowsfuture security events to be anticipated, for example, by simple edgenode devices that are running in a semi-autonomous way. The edge devicesmay be responsible for building a set of forecast models showing trendsin the data. The parameters of this set of forecast models may betransmitted using the PMCP system.

Security systems are constantly looking for vectors showing change instate, as unusual events tend to trigger multiple vectors to showunusual patterns. In a security setting, seeing multiple simultaneousunusual vectors may trigger escalation and a response by, for example,the control system. In addition, one of the major areas of communicationsecurity concern is around the protection of stored data, and in avector-based system data does not need to be stored, and so the risk ofdata loss is simply removed.

In embodiments, PMCP data can be directly stored in a queryable databasewhere the actual data is reconstructed dynamically in response to aquery. In some embodiments, the PMCP data streams can be used torecreate the fine-grained data so they become part of an ExtractTransform and Load (ETL) process.

In embodiments where there are edge devices with very limitedcapacities, additional edge communication devices can be added toconvert the data into PMCP format. For example, to protect distributedmedical equipment from hacking attempts many manufacturers will chooseto not connect the device to any kind of network. To overcome thislimitation, the medical equipment may be monitored using sensors, suchas cameras, sound monitors, voltage detectors for power usage, chemicalsniffers, and the like. Functional unit learning and other datatechniques may be used to determine the actual usage of the medicalequipment detached from the network functional unit.

Communication using vectorized data allows for a constant view of likelyfuture states. This allows the future state to be communicated, allowingvarious entities to respond ahead of future state requirements withoutneeding access to the fine-grained data.

In embodiments, the PMCP protocol can be used to communicate relevantinformation about production levels and future trends in production.This PMCP data feed, with its built-in data obfuscation allows realcontextual information about production levels to be shared withconsumers, regulators, and other entities without requiring sensitivedata to be shared. For example, when choosing to purchase a new car, ifthere is an upcoming shortage of red paint then the consumer could beencouraged to choose a different color in order to maintain a desireddelivery time. PMCP and vector data enables simple data informedinteractive systems that user can apply without having to buildenormously complex big data engines. As an example, an upstreammanufacturer has an enormously complex task of coordinating manydownstream consumption points. Through the use of PMCP, the manufactureris able to provide real information to consumers without the need tostore detailed data and build complex models.

In embodiments, edge device units may communicate via the PMCP system toshow direction of movement and likely future positions. For example, amoving robot can communicate its likely track of future movement.

In embodiments, the PMCP system enables visual representations ofvector-based data (e.g., via a user interface), highlighting of areas ofconcern without the need to process enormous volumes of data. Therepresentation allows for the display of many monitored vector inputs.The user interface can then display information relating to the keyitems of interest, specifically vectors showing areas of unusual ortroublesome movement. This mechanism allows sophisticated models thatare built at the edge device edge nodes to feed into end usercommunications in a visually informative way.

Functional units produce a constant stream of “boring” data. By changingfrom producing data, to being monitored for problems, issues with thelogistical modules are highlighted without the need for scrutiny offine-grained data. In embodiments, the vectorizing process couldconstantly manage a predictive model showing future state. In thecontext of maintenance, these changes to the parameters in thepredictive model are in and of themselves predictors of change inoperational parameters, potentially indicating the need for maintenance.In embodiments, functional areas are not always designed to beconnected, but by allowing for an external device to virtually monitordevices, functional areas that do not allow for connectivity can becomepart of the information flow in the goods. This concept extends to allowfunctional areas that have limited connectivity to be monitoredeffectively by embellishing their data streams with vectorized monitoredinformation. Placing an automated device in the proximity of thefunctional unit that has limited or no connectivity allows capture ofinformation from the devices without the requirement of connectivity.There is also potential to add training data capture functional unitsfor these unconnected or limitedly connected functional areas. Thesetraining data capture functional units are typically quite expensive andcan provide high quality monitoring data, which is used as an input intothe proximity edge device monitoring device to provide data forsupervised learning algorithms.

Oftentimes, locations are laden with electrical interference, causingfundamental challenges with communications. The traditional approach ofstreaming all the fine-grained data is dependent on the completeness ofthe data stream. For example, if an edge device was to go offline for 10minutes, the streaming data and its information would be lost. Withvectorized communication, the offline unit continues to refine thepredictive model until the moment when it reconnects, which allows theupdated model to be transmitted via the PMCP system.

In embodiments, systems and devices may be based on the PMCP protocol.For example, cameras and vision systems (e.g., liquid lens systems),user devices, sensors, robots, smart containers, and the like may usePMCP and/or vector-based communication. By using vector-based cameras,for example, only information relating to the movement of items istransmitted. This reduces the data volume and by its nature filtersinformation about static items, showing only the changes in the imagesand focusing the data communication on elements of change. The overallshift in communication to communication of change is similar to how thehuman process of sight functions, where stationary items are not evencommunicated to the higher levels of the brain.

Radio Frequency Identification allows for massive volumes of mobile tagsto be tracked in real-time. In embodiments, the movement of the tags maybe communicated as vector information via the PMCP protocol, as thisform of communication is naturally suited to handing informationregarding the location of tag within the goods. Adding the ability toshow future state of the location using predictive models that can usepaths of prior movement allows the goods to change the fundamentalcommunication mechanism to one where units consuming data streams areconsuming information about the likely future state of the goods. Inembodiments, each tagged item may be represented as a probability-basedlocation matrix showing the likely probability of the tagged item beingat a position in space. The communication of movement shows thetransformation of the location probability matrix to a new set ofprobabilities. This probabilistic locational overview provides forconstant modeling of areas of likely intersection of moving units andallows for refinement of the probabilistic view of the location ofitems. Moving to a vector-based probability matrix allows units toconstantly handle the inherent uncertainty in the measurement of statusof various items, entities, and the like. In embodiments, statusincludes, but is not limited to, location, temperature, movement andpower consumption.

In embodiments, continuous connectivity is not required for continuousmonitoring of sensor inputs in a PMCP-based communication system. Forexample, a mobile robotic device with a plurality of sensors willcontinue to build models and predictions of data streams whiledisconnected from the network, and upon reconnection, the updated modelsare communicated. Furthermore, other systems or devices that use inputfrom the monitored system or device can apply the best known, typicallylast communicated, vector predictions to continue to maintain aprobabilistic understanding of the states of the goods.

Deployment

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions, orother types of instructions capable of being executed by the computingor processing device may include but may not be limited to one or moreof a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, andthe like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor, and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other network types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g., USB sticks orkeys), floppy disks, magnetic tape, paper tape, punch cards, standaloneRAM disks, Zip drives, removable mass storage, off-line, and the like;other computer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagrams,or any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above, and combinations thereof,may be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein may be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosure,and does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

While the foregoing written description enables one skilled to make anduse what is considered presently to be the best mode thereof, thoseskilled in the art will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiment,method, and examples herein. The disclosure should therefore not belimited by the above described embodiment, method, and examples, but byall embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f). The term “set” as used herein refers to a group having one ormore members.

Persons skilled in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platforms. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like,including a central processing unit (CPU), a general processing unit(GPU), a logic board, a chip (e.g., a graphics chip, a video processingchip, a data compression chip, or the like), a chipset, a controller, asystem-on-chip (e.g., an RF system on chip, an AI system on chip, avideo processing system on chip, or others), an integrated circuit, anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), an approximate computing processor, a quantumcomputing processor, a parallel computing processor, a neural networkprocessor, or other types of processor. The processor may be or mayinclude a signal processor, digital processor, data processor, embeddedprocessor, microprocessor, or any variant such as a co-processor (mathco-processor, graphic co-processor, communication co-processor, videoco-processor, AI co-processor, and the like) and the like that maydirectly or indirectly facilitate execution of program code or programinstructions stored thereon. In addition, the processor may enableexecution of multiple programs, threads, and codes. The threads may beexecuted simultaneously to enhance the performance of the processor andto facilitate simultaneous operations of the application. By way ofimplementation, methods, program codes, program instructions and thelike described herein may be implemented in one or more threads. Thethread may spawn other threads that may have assigned prioritiesassociated with them; the processor may execute these threads based onpriority or any other order based on instructions provided in theprogram code. The processor, or any machine utilizing one, may includenon-transitory memory that stores methods, codes, instructions, andprograms as described herein and elsewhere. The processor may access anon-transitory storage medium through an interface that may storemethods, codes, and instructions as described herein and elsewhere. Thestorage medium associated with the processor for storing methods,programs, codes, program instructions or other type of instructionscapable of being executed by the computing or processing device mayinclude but may not be limited to one or more of a CD-ROM, DVD, memory,hard disk, flash drive, RAM, ROM, cache, network-attached storage,server-based storage, and the like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(sometimes called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, switch,infrastructure-as-a-service, platform-as-a-service, or other suchcomputer and/or networking hardware or system. The software may beassociated with a server that may include a file server, print server,domain server, internet server, intranet server, cloud server,infrastructure-as-a-service server, platform-as-a-service server, webserver, and other variants such as secondary server, host server,distributed server, failover server, backup server, server farm, and thelike. The server may include one or more of memories, processors,computer readable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of programs across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationswithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for the execution of methods asdescribed in this application may be considered as a part of theinfrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of programs across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more locations without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network with multiple cells.The cellular network may either be frequency division multiple access(FDMA) network or code division multiple access (CDMA) network. Thecellular network may include mobile devices, cell sites, base stations,repeaters, antennas, towers, and the like. The cell network may be aGSM, GPRS, 3G, 4G, 5G, LTE, EVDO, mesh, or other network types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic book readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g., USB sticks orkeys), floppy disks, magnetic tape, paper tape, punch cards, standaloneRAM disks, Zip drives, removable mass storage, off-line, and the like;other computer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink,network-attached storage, network storage, NVME-accessible storage, PCIEconnected storage, distributed storage, and the like.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable code using aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices, artificial intelligence, computing devices,networking equipment, servers, routers, and the like. Furthermore, theelements depicted in the flow chart and block diagrams, or any otherlogical component may be implemented on a machine capable of executingprogram instructions. Thus, while the foregoing drawings anddescriptions set forth functional aspects of the disclosed systems, noparticular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable devices, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions. Computer software may employvirtualization, virtual machines, containers, dock facilities,portainers, and other capabilities.

Thus, in one aspect, methods described above, and combinations thereof,may be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “with,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitations of ranges ofvalues herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein can be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate the disclosure, and does not pose a limitation on the scopeof the disclosure unless otherwise claimed. The term “set” may include aset with a single member. No language in the specification should beconstrued as indicating any non-claimed element as essential to thepractice of the disclosure.

While the foregoing written description enables one skilled to make anduse what is considered presently to be the best mode thereof, thoseskilled in the art will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiment,method, and examples herein. The disclosure should therefore not belimited by the above described embodiment, method, and examples, but byall embodiments and methods within the scope and spirit of thedisclosure.

All documents referenced herein are hereby incorporated by reference asif fully set forth herein.

What is claimed is:
 1. A system for training models and monitoring, thesystem comprising: a machine learning system that trains a set ofmachine-learned models to generate a prediction, wherein the machinelearning system trains the set of machine-learned models using trainingdata including market features and outcomes; a neural network systemthat generates the prediction based on the machine-learned models inresponse to a prediction request; and a lending platform including anInternet of Things data collection system for monitoring at least one ofa set of assets and a set of collateral.
 2. The system of claim 1,wherein the prediction is a prediction for a parameter of demand in aforward market for an asset.
 3. The system of claim 1, wherein theprediction is a prediction for a parameter of supply in a forward marketfor an asset.
 4. The system of claim 1, wherein the prediction is aprediction of a set of terms and/or conditions for a smart contract. 5.The system of claim 1, wherein the prediction is based at least in parton crowdsourced data.
 6. The system of claim 1, wherein the predictionis based at least in part on behavioral data collected from a set of IoTsystems monitoring a set of entities in a set of environments.
 7. Thesystem of claim 1, wherein the neural network system includes arecurrent neural network.
 8. The system of claim 1, wherein the neuralnetwork system includes a convolutional neural network.
 9. The system ofclaim 1, wherein the neural network system includes a combination of arecurrent neural network and a convolutional neural network.
 10. Thesystem of claim 6, wherein the set of Internet of Things systemsincludes a set of smart home Internet of Things devices.
 11. The systemof claim 6, wherein the set of Internet of Things systems includes a setof workplace Internet of Things devices.
 12. The system of claim 6,wherein the set of Internet of Things systems includes a set of Internetof Things device to monitor a set of consumer goods stores.
 13. Thesystem of claim 1, further comprising a security monitoring system formonitoring assets and/or collateral based on the data collected by theInternet of Things data collection platform.
 14. The system of claim 13,wherein the security monitoring system uses machine-learned models todetermine a condition or value of items based on data collected by theInternet of Things data collection platform.
 15. The system of claim 14,wherein the data collected by the Internet of Things data collectionplatform is image data, sensor data, or location data.
 16. The system ofclaim 13, further comprising a management system that enables access toinformation from the Internet of Things data collection system and thesecurity monitoring system.
 17. The system of claim 1, wherein the setof machine-learned models employ a convolutional neural network, arecurrent neural network, a feed forward neural network, along-term/short-term memory (LTSM) neural network, a self-organizingneural network, and hybrids and combinations of the foregoing.
 18. Amethod, comprising: training a set of machine-learned models to generatea prediction, wherein training the set of machine-learned models usestraining data including market features and outcomes; generate theprediction with a neural network based on the machine-learned models inresponse to a prediction request; and monitoring at least one of a setof assets and a set of collateral in a lending platform using anInternet of Things data collection system.
 19. The method of claim 18,wherein the prediction is a prediction for a parameter of demand in aforward market for an asset.
 20. The method of claim 18, wherein theprediction is a prediction for a parameter of supply in a forward marketfor an asset.
 21. The method of claim 18, wherein the prediction is aprediction of a set of terms and/or conditions for a smart contract. 22.The method of claim 18, wherein the prediction is based at least in parton crowdsourced data.
 23. The method of claim 18, wherein the predictionis based at least in part on behavioral data collected from a set of IoTsystems monitoring a set of entities in a set of environments.
 24. Themethod of claim 18, wherein the neural network system includes arecurrent neural network.
 25. The method of claim 18, wherein the neuralnetwork system includes a convolutional neural network.
 26. The methodof claim 18, wherein the neural network system includes a combination ofa recurrent neural network and a convolutional neural network.
 27. Themethod of claim 23, wherein the set of Internet of Things systemsincludes at least one of a set of smart home Internet of Things devices,a set of workplace Internet of Things devices, or a set of Internet ofThings devices to monitor a set of consumer goods stores.
 28. The methodof claim 18, further comprising a security monitoring system formonitoring assets and/or collateral based on the data collected by theInternet of Things data collection platform.
 29. The method of claim 28,wherein the security monitoring system uses machine-learned models todetermine a condition or value of items based on data collected by theInternet of Things data collection platform.
 30. The method of claim 18,wherein the set of machine-learned models employ a convolutional neuralnetwork, a recurrent neural network, a feed forward neural network, along-term/short-term memory (LTSM) neural network, a self-organizingneural network, and hybrids and combinations of the foregoing.